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Transcript
Section 4
4. The Goods Market
The goods market balances expenditure and output decisions of households, business and
government. It can be considered in two segments, domestic and international.
Many of the equations relevant to behaviour in the domestic goods market have already been
discussed in Section 1 on the private business sector. Section 1 analyses, in particular, the
effect of domestic aggregate demand/aggregate supply imbalances on aggregate non-commodity
output prices. Section 1.2.2 discusses the estimation of domestic non-commodity prices based
around a production function that does not include imports as a factor of production. As noted
in Section 1.2.2, changes in import prices would be expected to change the price of total supply
(domestic plus imported production). Thus, changes in import prices would be expected to
impact differently on the various components of domestic expenditure depending on their import
intensiveness. Section 4.1 outlines the behavioural equations that translate changes in both
import prices and the aggregate supply price into changes in the prices of the major components
of domestic expenditure: the ‘relative price block’ equations.
Section 4.2 covers the international segment of the goods market. The section covers the
behavioural equations for the prices and quantities of commodity exports, non-commodity
exports and imports of goods and services.
4.1 Relative Prices
4.1.1 Background
On the supply side of the TRYM model, prices are disaggregated into commodity and
non-commodity prices. Commodity prices are determined in world auction markets and are
quite flexible. Non-commodity prices are determined in domestic markets (as a function of
wages and excess demand) and are relatively sticky. The estimation of non-commodity prices
is covered in more detail in Section 1.2.2.
The price of business output is an aggregation of commodity and non-commodity prices.
The price of business output (price of domestic supply), together with import prices (price of
import supply), determine the price (PT) of total supply (domestically produced and foreign)
available to the domestic market.
On the demand side, the relative price block models changes in the relative prices of the major
expenditure components of GNE. Five expenditure prices are examined, namely the prices of
non-rental consumption (PCNR), dwelling investment (PIDW), real estate transfers (PIRET),
private sector business investment (PIB) and government market demand (PDGM).
The relative price block is particularly concerned with the impact of changes in import prices
(PMGS) on the various components of domestic demand. The impact on each component
differs largely because of differing exposure to international competition.
A number of other prices on the demand side of the economy are not explicitly modelled.
These include the price of stocks (both farm and non-farm) and a number of other deflators.
4.1
The Goods Market
Ex post, the price of supply and the price of demand must be equal. In the TRYM model, this is
achieved via a scaling factor (PSF), which adjusts to take account of changes in prices that are
not adequately captured by the relative price block (including those prices that are not modelled
explicitly). With the relative expenditure prices resulting from the relative price block denoted
by the suffix 'F', the scaling factor is defined as follows.
PSF =
PT (CNR + IDW + IRET + IB + DGM)
PCNRF CNR + PIDWF IDW + PIRETF IRET + PIBF IB + PDGMF DGM
4.1.2 Equation Specification, Results and Interpretation
The equations for the five expenditure components are estimated using the seemingly unrelated
regression equations method to take advantage of contemporaneous correlations in the error
terms.
The specification adopted implies that the import intensiveness of each type of expenditure
determines the movements in its relative price. Wages and excess demand conditions captured
in the price of total supply (PT) are assumed to affect each expenditure component equally in the
long run. Therefore, changes in PT do not alter relative prices in the long run. However, the
pace of adjustment to changes in PT varies across expenditure goods.
The equations are estimated in error correction form and the distributed lags are simplified from
a general specification (up to four lags) by omitting insignificant variables.
Unconstrained estimation produced plausible long run responses in four of the five equations.
A one per cent increase in import prices increased the relative price of business investment (PIB)
the most but only slightly more than the relative price of government market demand (PDGM),
while the relative prices of non-rental consumption (PCNR) fell slightly and the price of
dwelling investment (PIDW) fell significantly. The relative intensities of these responses are
broadly in line with the import intensiveness obtained from PRISMOD of the respective
components of expenditure.
The relative price of real estate transfer expenses did not behave as expected, increasing by more
than any other price in the long run, even though it would be expected to fall in relative terms,
as it has little import content. Data on PIRET are essentially residuals formed from deflating
the current value of real estate transfer expenses (including stamp duties)1 by a scaled measure
of turnover (measured by the number of sales) as an approximation of constant price volumes.
Real estate transfer expenses include expenses on sales of assets such as land and office
buildings in central business districts, the price of which is trending up over time. Therefore,
the unusually large long run elasticity obtained in the estimation may reflect the methodology
underlying the ABS data construction which may result in an asset price component being
included. The significant trend evident in PIRET relative to other prices may reflect rising
values of residential land and land in central building districts.
As a result of these problems, the long run elasticity for PIRET was constrained to a value
of -0.25, which is consistent with no change in PIRET when import prices increase. It was also
1
Real estate transfers include transfers of existing assets such as offices. Relative movements in PIRET therefore reflect movements in
established house and office prices. This poses a measurement problem that makes PIRET difficult to model in a way consistent with other
price indices that reflect the price of flows in the economy.
4.2
The Goods Market
necessary to constrain the parameter on one of the lagged dependent variables (a3) in the PDGM
equation to produce stable simulation properties.
The relative price equations were estimated as follows.
ln( PDGM) (1 a 2 a 3 ) ln PDGM( 1)
a 2 ln PDGM( 2)
a 3 ln PT
c 2 QTIME - QTIME(-1)
PDGM(-1)
ln
PT(-1)
a0
PMGS(-1) (1+ RTMGS(-1))
c 0 c 1 ln PD(-1) (1- RTDGM(-1)) c 2 QTIME(-1)
ln( PIB) (1 a 3 ) ln PIB( 1)
+ a 3 ln PT
PMGS (1 + RTMGS)
+ a 4 ln
PD (1- RTIB)
PIB(-1)
ln
PT(-1)
a0
PMGS(-1) (1 + RTMGS(-1))
c 0 c 1 ln
PD(-1) (1- RTIB(-1))
ln( PCNR ) (1 a 3 ) ln PCNR(-1)
+ a 3 ln PT
PCNR(-1)
ln
PT(-1)
a0
PMGS(-1) (1+ RTMGS(-1))
c 0 c 1 ln PD(-1) (1- RTCNR(-1))
ln( PIDW) (1 a 2 a 3 ) ln PIDW( 1)
+ a 2 ln PIDW( 2)
+ a 3 ln PT
PMGS (1 + RTMGS)
+ a 4 ln
PD (1- RTIDW)
PIDW(-1)
ln
PT(-1)
-a0
PMGS(-1) (1 + RTMGS(-1))
c 0 c 1 ln PD(-1) (1- RTIDW(-1))
4.3
The Goods Market
ln( PIRET) (1 a 3 ) ln PIRET( 1)
+ a 3 ln PT
PMGS (1+ RTMGS)
a 4 ln
PD
c 2 QTIME - QTIME(-1)
PIRET(-1)
ln
PT(-1)
a0
PMGS(-1) (1 + RTMGS(-1))
c 2 QTIME(-1)
c 0 c 1 ln
PD(-1)
Results
Price of Government Market Demand
Sample: 1974(4) to 1995(3)
Parameter
Interpretation
a2
a3
a0
c0
c1
c2
second lag dependent
change in supply price
error correction
long run constant
relative price imports
time trend
Estimate
0.347
0.5
0.308
-0.014
0.085
0.011
t-Statistic
7.04
constrained
3.61
-0.46*
0.87*
4.48
Price of Business Investment
Sample: 1974(4) to 1995(3)
Parameter
Interpretation
a3
a4
a0
c0
c1
change in supply price
change in rel price import
error correction
long run constant
relative price imports
Estimate
0.811
0.107
0.192
-0.127
0.403
t-Statistic
11.11
5.43
4.39
-7.73
9.38
Price of Non-Rental Consumption
Sample: 1974(4) to 1995(3)
Parameter
Interpretation
a3
a0
c0
c1
change in supply price
error correction
long run constant
relative price imports
4.4
Estimate
0.912
0.079
0.081
-0.176
t-Statistic
22.51
2.25
3.07
-2.92
The Goods Market
Price of Dwelling Investment
Sample: 1974(4) to 1995(3)
Parameter
Interpretation
a2
a3
a4
a0
c0
c1
second lag dependent
change in supply price
change in rel price imports
error correction
long run constant
relative price imports
Estimate
t-Statistic
-0.378
0.174
-0.023
0.054
0.044
0.395
-3.43
3.62
-1.91*
2.76
1.69*
5.29
Estimate
t-Statistic
Price of Real Estate Transfer Expenses
Sample: 1974(4) to 1995(3)
Parameter
Interpretation
a3
a4
a0
c0
c1
c2
change in supply price
change in rel price import
error correction
long run constant
relative price imports
long run time trend
0.829
-0.264
0.069
-0.183
0.25
0.004
7.56
-2.00
1.75*
-2.23
constrained
0.25*
Diagnostic Statistics
Equations
Tests
R2
S.E.
DW
Box-Pierce (1-8 order)
Jarque-Bera
Chow
Ramsey Reset
Breusch-Pagan
i) Trend
ii) Y-hat
iii) Joint
PDGM
PIB
0.39
2.02%
2.12
9.16
6.37*
3.03*
0.03
0.79
0.66%
1.92
9.90
0.47
0.74
0.004
2.48
1.38
2.61
2.76
0.38
3.17
PCNR
0.87
0.31%
2.65*
18.97*
0.42
2.26
5.32*
.28
0.99
1.20
PIDW
PIRET
0.91
0.39%
2.06
5.77
0.63
4.72*
0.04
0.16
4.38%
2.02
6.87
3.64
1.24
0.004
0.78
1.26
1.30
0.26
0.20
0.28
* Indicates the test has failed at the 5% confidence level
Interpretation
Figure 3 shows the impact of a 1 per cent increase in the supply price on the relative prices of
each expenditure component. This could be generated by a rise in wages or any other factor
that affects the cost of supply for producers. By construction, all five prices are assumed to
increase by 1 per cent in the long run (price homogeneity).
4.5
The Goods Market
Figure 3: Relative Price Dynamics for a 1 per cent Increase in Underlying Price Level (PT)
1.4
1.2
PDGM
PCNR
PIDW
1
0.8
PIB
0.6
0.4
0.2
PCNR
0
Year 0
Year 1
Year 2
Year 3
PDGM
Year 4
PIB
Year 5
Year 6
PIDW
Year 7
PIRET
Year 8
Year 9
The different speed of adjustment for these prices can be explained by factors such as different
contractual arrangements, the extent to which expenditure is on goods that are durable and can
be stockpiled or have long production lags, competition in the product market and wage
arrangements that might affect the supply price. Each of the prices is considered below.
A general distinguishing feature of PCNR, PIB and PIRET is the large initial increase, compared
to PDGM and PIDW. The former three also adjust quite rapidly to the long run value that
suggests other factors are more important in these price adjustments.
•
PIDW jumps only slightly initially and, like PDGM, adjusts relatively slowly. This could
reflect the time taken to build new houses. The subsequent overshooting observed may
reflect profit margins in the PIDW data. These margins may vary with conditions of
demand and supply in the dwelling sector. For example, the existence of excess demand
may mean higher profit margins.
•
PDGM increases by 0.5 per cent initially (by construction), overshoots and adjusts slowly
to the long run value. The slow adjustment may reflect the existence of long lived
contracts that government departments have with some suppliers. PDGM also includes
military expenditures, such as spending on submarines, frigates and planes. With large
expenditure items and items that are constructed on order, the price would be expected to
lag changes in supply prices. Thus, the price of these goods may not reflect the cost of
production today but, say, the average over the last year; that is, a rise in PT would not be
expected to feed immediately or rapidly into the price of these goods.
•
The rapid adjustment of PCNR may reflect the nature of non-rental consumption goods.
If they are non-durable or durable goods without long production lags, the selling price
would reflect the current costs of production. Hence, any increase in PT would flow
rapidly into higher non-rental consumption prices.
•
The main linkage of domestic supply prices to PIB (price of business investment) would
be through non-dwelling construction (IOB) as a large part of plant and equipment is
imported. The IOB component of the business investment deflator is based on input costs
(wages and raw material prices). Hence, any change to wages or supply price flows
relatively quickly into the business investment deflator. Further, the quick adjustment in
PIB reflects the high import component of equipment, implying rapid exchange rate
adjustment and pass through.
4.6
The Goods Market
•
As noted, not much economic justification can be given for the PIRET price adjustment
because of the nature of the series. The figure indicates that PIRET increases immediately
and adjusts to the long run value rapidly.
The results of a 1 per cent increase in import prices are shown in Figure 4. The relative increase
in each expenditure price would be expected to reflect its dependence on imports. Thus, from
the differing import weights (penetration ratios) given by PRISMOD, we would expect the
relative change in prices, from highest to lowest, to be in the following order: government
market demand (PDGM); business investment (PIB); non-rental consumption (PCNR); dwelling
investment (PIDW); and real estate transfer expenses (PIRET).
Figure 4: Relative Price Dynamics for a 1 per cent Increase in Import Prices
0.5
% Deviation from Baseline
0.4
PIB
0.3
0.2
PDGM
0.1
0
-0.1
PCNR
-0.2
PIRET
-0.3
PIDW
-0.4
-0.5
Year 0
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
The dynamics and long run values shown in the figure are broadly consistent with the weights
given in PRISMOD. PDGM and PIB both increase as expected. PIB, however, increases more
than PDGM. PCNR falls slightly, suggesting a slight fall in the price of non-rental
consumption relative to the prices of other expenditure components when import prices rise.
PIDW and PIRET fall further, with PIDW falling the most. This is opposite to the expected
relative movement but, again, the gap between the two is not large.
There is a large immediate fall in PIRET, which is consistent with its small import component.
The immediate increase in PIB is a distinguishing feature from the other prices. PIDW has a
cyclical adjustment path: it over-adjusts and then returns to its long run equilibrium. This
cyclical nature is also observed in the PIDW series in history.
4.2 The Market for Exports
Exports are separated into commodity exports and non-commodity exports. Commodity
exports (XC) consist primarily of mining and agricultural products, while non-commodity
exports (XNC) consist primarily of manufactured goods and services.
Foreign demand for Australian exports depends on external competitiveness; that is, the price of
Australian exports relative to the price of substitutes on world markets. Demand rises as our
export prices fall relative to world prices.
4.7
The Goods Market
The supply of exports is driven by internal competitiveness; that is, the ability of the traded
goods sector to attract resources from the non-traded goods sector. Domestic (internal)
producers move resources into the production of exports on the basis of relative domestic prices
of traded goods and non-traded goods.
To capture the impact of the two different types of cost competitiveness, exports are modelled
using a demand and supply framework that is simplified greatly by exploiting the following
dichotomy.
•
For commodity exports, Australia is assumed to be a small open economy where
commodity export prices are determined by world prices. The world will take as much as
Australia wishes to supply at the going world price. Demand and supply curves are
estimated for commodity exports. The former determines the $A export commodity price
and the latter determines the quantity of commodities produced.
•
In contrast, domestic producers of non-commodities export only a relatively small
proportion of their total output and, therefore, foreigners can purchase as much of
Australia's exports as they wish without affecting the price. At that price there is limited
foreign demand for those exports. Only a demand curve is estimated for non-commodity
exports. Since the supply curve is assumed to be perfectly elastic, it does not need to be
estimated because the price of non-commodity exports is determined by the domestic price
of non-commodities, which is estimated in the business sector of the model.
Factors other than external and internal competitiveness that have an influence, include:
•
the growth of our major trading partners. An increase in our major trading partner growth
will, other things equal, increase demand for our exports and lead to a corresponding
increase in export prices;
•
the strong productivity growth in the mining and agricultural sectors, which appears to
have assisted in maintaining Australia's commodity export supply;
•
fluctuations in oil prices relative to world prices generally. An increase in the world oil
price increases the price of Australia's oil exports and increases the price of commodities
exported by Australia that are substitutes for oil; and
•
the declining trend in world commodity prices relative to world prices generally.
4.2.1 Measuring Competitiveness and 'World Economic Growth'
Bilateral export weights are applied to quarterly data of the exchange rate, GDP and the GDP
deflator for a variety of countries to construct a trade weighted exchange rate (RETWI), a trade
weighted index of world output (WGDP) and a trade weighted index of world prices (WPGDP).
The weights have been allowed to vary over time according to the size of each country's export
trade with Australia but the weighting pattern has been smoothed to account for 'one-off'
influences.
For some Asian countries it was difficult to obtain quarterly data and this required that a
quarterly data series be constructed using annual figures. In some cases, it was also necessary
to substitute GNP for GDP and use a measure of consumer prices instead of a GDP deflator.
4.8
The Goods Market
Further details on how these series are constructed can be found in the documentation for the
TRYM data base.
The level of foreign prices, adjusted for movements in the exchange rate, is measured by
adjusting WPGDP for movements in RETWI and is compared with domestic export prices to
obtain an index of external competitiveness.
Internal competitiveness is measured by comparing the domestic price that Australian producers
receive for their exports with the domestic price of non-commodities.
4.2.2 Equation Specification, Results and Interpretation
The equations for commodity demand and commodity supply are estimated jointly.
Commodity Export Demand
In the long run, the price of commodity exports (PXC) is assumed to fully adjust to changes in
the level of world prices (WPGDP) and the exchange rate (RETWI). The equilibrium price of
commodity exports is also assumed to be a function of a time trend, which is included to capture
the effects of a trend fall in the world commodity prices relative to world prices. The price of
commodity exports was found not to be a function of the quantity of commodity exports (XC).
This may reflect the aggregative nature of the analysis which dilutes the influence of those
commodities for which Australia has a degree of 'market power'.
In equilibrium, the commodity export price level is determined as follows:
ln(PXC) = ln(WPGDP) - ln(RTWI ) + c1 QTIME
Among other things, the presence of contractual and delivery lags with commodity exports
suggests that this relationship should not hold instantaneously. In the short run, quarter on
quarter changes in the price of exports relative to world prices are assumed to be a function of
changes in the GDP growth of our major trading partners, the relative price of oil and the real
exchange rate calculated using Australia's GDP deflator.
Changes in the exchange rate can be expected to influence commodity export prices in the short
run, though the full flow-on will be delayed since a portion of commodity export contracts are
denominated in Australian dollars. It is estimated that around 75 per cent of Australia's export
contracts are denominated in foreign currencies. Assuming that export contracts denominated
in Australian dollars are not renegotiated quickly, the coefficient on the real exchange rate (a3) is
constrained to equal 0.25.
Combining the short run dynamics with the long run equilibrium relationship and adjusting to
ensure that there is no steady state bias leads to the following error correction equation:
4.9
The Goods Market
ln(WGDP( 1) - GR + ln(WGDP(-1)) - GR(-1)
c1
RETWI PXC
ln
a1
WPGDP
+ ln(WGDP(-2)) - GR(-2) + ln(WGDP(-3)) - GR(-3) 4
WPMPE
a 2 ln
WPGDP
RETWI PGDPA
a 3 ln
WPGDP
PXC(-1) RETWI(-1)
a 0 ln
- c 0 - c 1 QTIME(-1)
WPGDP(-1)
Results (from joint estimation of commodity demand and supply equations)
Sample: 1975(1) to 1995(3)
Parameter
Interpretation
a1
a2
a3
a0
c0
c1
MTP growth
oil prices
real exchange rate
error correction
long run constant
time trend
Estimate
t-Statistic
0.585
0.140
0.250
0.054
1.420
0.040
1.97
5.54
constrained
1.96
11.03
3.97
Diagnostic Statistics (based on single equation estimates)
R2 = 0.60
SE = 2.88%
DW = 1.33*
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
25.12*
1.08
6.33*
2.61
1.09
0.38
1.38
* Indicates the test has failed at the 5% confidence level.
Interpretation
All coefficients are significant and have plausible economic interpretations. The equation
implies that about 5 per cent of any disequilibrium between the actual and desired level of PXC
is eliminated per quarter, so that the average lag length is about 5 years. The short run
dynamics of the equation imply that:
•
a 10 per cent depreciation in the trade weighted index (RETWI) will increase PXC by
7.5 per cent within a quarter (reflecting the constrained parameter on the real exchange
rate) and by 10 per cent in the long run;
•
a one per cent fall in the level of output growth of our major trading partners will reduce
the price of commodity exports by about 2.3 per cent after one year; and
4.10
The Goods Market
•
a 10 per cent permanent rise in oil prices relative to world prices increases the price of
commodity exports by about 1.4 per cent in the short run but will not have any effect in the
long run.
Commodity Export Supply
Australian firms are assumed to maximise their revenue, given the quantity of their factors of
production, by allocating commodity export supply between the domestic and foreign sectors on
the basis of relative prices; that is, on the basis of internal competitiveness. Therefore, in the
long run, the quantity of commodities exported (XC) is driven by the price of commodity
exports (PXC) adjusted for the indirect tax rate on commodity exports (RTXC) relative to the
domestic price of non-commodities (PNC). In equilibrium, the supply of commodity exports is
also a function of the short run equilibrium level of supply in the economy (GSTAR) - that is,
the level of output from current levels of employment and capital stock.
It is also assumed that:
•
productivity growth in the commodity producing sector is 3.5 per cent per annum higher
than the underlying productivity growth for the rest of the economy. This assumption is
imposed by setting the coefficient on the time trend equal to 0.035; and
•
farm stockbuilding (SFM) is part of exportable commodity production.
The supply of commodities will be directly affected by the amount of rainfall. Therefore
commodity supply is adjusted for the amount of 'rain affected output' (GBRAIN) and the long
run relationship is given by:
ln(XC + SFM - GBRAIN) = ln(GSTAR - XC - SFM + GBRAIN)
PXC (1 RTXC)
+ c 0 + c 1 ln
c 2 QTIME
PNC
To capture the lags between changes in relative prices and changes in output, an error correction
format was specified, with the dynamics being driven by a lagged dependent variable.
c
ln(XC + SFM - GBRAIN) = (1- a 0 ) GR + 2
4
a1 ln[(XC(-1) + SFM(-1)) - GBRAIN(-1)]
XC(-1) + SFM(-1) - GBRAIN(-1)
ln
GSTAR(-1) - XC(-1) - SFM(-1) + GBRAIN(-1)
PXC(-1) (1- RTXC(-1)
- a 0 c0 c1 ln
PNC(-1)
c2 QTIME(-1)
4.11
The Goods Market
Results (from joint estimation of commodity demand and supply equations)
Sample: 1975(1) to 1995(3)
Parameter
Interpretation
a1
a0
c0
c1
c2
lagged dependent
error correction
long run constant
long run relative price
time trend
Estimate
t-Statistic
-0.287
0.197
-1.547
0.447
0.035
-2.70
2.73
-107.14
7.04
constrained
Diagnostic Statistics (based on single equation estimates)
R2 = 0.35
SE = 4.78%
DW = 2.08
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
9.51
16.91*
0.63
1.96
5.17*
0.36
5.45
* Indicates the test has failed at the 5% confidence level.
Interpretation
The difficulties associated with modelling commodity export supply are shown by the fact that
only around 35 per cent of the variation in this supply is explained by the estimated equation.
A re-examination of this equation will be required in the future.
In the meantime, the estimated equation indicates that any disequilibrium between the actual and
desired level of supply is removed by 20 per cent per quarter. The mean lag of adjustment is
just over a year.
The price elasticity of supply is 0.45 in the long run. While this coefficient is the correct sign,
it implies that supply is very inelastic, even in the long run. This could indicate that fixed
factors of production (such as land or resources) are important in determining commodity
supply.
Non-Commodity Export Demand
Demand for non-commodity exports relative to the level of our major trading partners' GDP
(XNC/WGDP) is assumed to be driven in the long run by external competitiveness; that is, the
price of exports of non-commodities (PXNC) relative to world prices adjusted for the exchange
rate (RETWI/WPGDP). The adjustment of the quantities of non-commodities exported to
changes in external competitiveness is assumed to be sluggish and therefore a partial adjustment
equation has been specified:
4.12
The Goods Market
XNC(-1)
XNC
ln
= (1- a 1 - a 0 ) ln
WGDP
WGDP(-1)
XNC( 3)
a 1 ln
WGDP( 3)
PXNC RETWI
a 0 c 0 c 1 ln
WPGDP
Results
Sample: 1975(2) to 1995(3)
Parameter
Interpretation
a1
a0
c0
c1
~ weight on third lag
partial adjustment coeff
constant
external competitiveness
Estimate
0.238
0.050
8.342
2.889
t-Statistic
2.98
2.16
4.58
2.87
Diagnostic Statistics
R2 = 0.98
SE = 3.16%
DW = 1.99
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
5.91
0.79
3.23*
0.00
7.08*
5.96*
7.08*
* Indicates the test has failed at the 5% confidence level.
Interpretation
The estimated long run elasticity of demand is about 2.9: a fall in the real exchange rate
(an increase in external competitiveness) of 1 per cent will eventually lead to an increase of
2.9 per cent in the volume of non-commodities exported. However, the adjustment towards the
long run equilibrium is slow, with around half of the impact of changes in the real exchange rate
taking about 5 years to be fully reflected in export volumes.
Non-Commodity Export Supply
A supply curve for non-commodities is not estimated because the supply curve is assumed to be
perfectly elastic. Therefore, the price of non-commodity exports is linked to the domestic price
of non-commodities (PNC) using an exogenous ratio (XRPXNC).
XRPXNC =
PXNC
PNC
The estimated equation for PNC is discussed in Section 1 (the Private Business Sector).
4.13
The Goods Market
4.3 The Market for Imports
The importation of goods and services is also modelled using a demand and supply framework,
with the demand equation determining quantity and the supply equation determining prices.
The primary factors determining aggregate demand for imports are domestic income and the
import competitiveness of the domestic tradeable goods sector (the price of imports relative to
domestic substitutes).
Australia is assumed to be a small economy that cannot influence world prices, so the world
supply of imports is infinitely elastic in the long run. The price of imports is thus modelled as a
function of the world price of goods that Australia imports, the exchange rate, the price of oil
and domestic prices in Australia (to capture the effect of domestic price pressures on the speed
of pass through of price increases by foreign suppliers). A logistical growth curve is used to
capture the downward trend in import prices evident since the early 1980s.
4.3.1 Measuring Income
The income variable (DDMGS) which is used to explain import demand is constructed as an
imports-weighted average of the components of domestic expenditure. The weights used reflect
the differing import penetration ratios of the various types of domestic expenditure and are based
on information from PRISMOD.
Results from PRISMOD indicate that imports account for 33 per cent of government market
demand (DGM), which is equal to government final demand less government expenditure on
depreciation and labour; 29 per cent of business investment (IB), which includes plant and
equipment investment and investment in other building and construction; 25 per cent of exports
of non-commodities (XNC); 20 per cent of the statistical discrepancy (DISA); 17 per cent of
private non-rental consumption (CNR); 13 per cent of dwelling investment (IDW); 10 per cent
of exports of commodities (XC); and 3 per cent of consumption of rental services (CRE).
The measure of imports-weighted income is therefore constructed as follows:
DDMGS 0.17 CNR 0. 03 CRE 0. 29 IB 0.13 IDW
0. 25 XNC 0.10 XCOM 0. 33 DGM 0. 20 DISA
4.3.2 Measuring Relative Price
The measure of the price of domestic tradeables (PDDMGS) is calculated using a weighted
average of various current price expenditure components (at factor cost), divided by DDMGS.
The weights used are the same as those used in DDMGS.
The implicit price deflators used in the construction of PDDMGS are constructed at market
prices and need to have the indirect tax component removed to ensure that PDDMGS is
constructed on a factor cost basis. This ensures that PDDMGS is consistent with the ABS
measurement of import prices at factor cost.
PDDMGS is defined below. The suffix 'Z' and the prefix 'RT' stand for current price and indirect
tax rate, respectively.
4.14
The Goods Market
. CNRZ 1 RTCNR 0.03 CREZ 1 RTCRE
017
. IDWZ 1 RTIDW
0.29 IBZ 1 RTIB 013
. XCZ 1 RTXC
0.25 XNCZ 1 RTXNC 010
0.33 DGMZ 1 RTDGM 0.20 DISAZ
PDDMGS
DDMGS
Import prices need to take account of customs duty (RTMGS) in order to correctly measure the
relative price of imports. Therefore, the price of imports (PMGS) relative to the price of
domestic tradeable goods (PDDMGS) is defined as follows:
Relative Price of Imports =
PMGS (1+ RTMGS)
PDDMGS
4.3.3 Equation Specification, Results and Interpretation
The equations for the demand and supply for imports are estimated jointly.
Import Demand
In the long run, the ratio of goods and services imported (MGS) to income (DDMGS), or the
import penetration ratio, is assumed to be a function of the relative price of imports
(PMGS*(1+RTMGS)/PDDMGS) and a time trend. This formulation imposes a long run
income elasticity of one on import demand, while increasing import penetration over the
estimation period through the time trend.
MGS
PMGS (1+ RTMGS)
ln
=
c
c
ln
0
1
+ c 2 *QTIME
PDDMGS
DDMGS
Actual import demand will not instantaneously match desired import demand because of
informational lags, delivery times and existing contractual obligations. Therefore, in the short
run, changes in import demand are a function of changes in income, changes in the relative price
of imports, lagged changes in import demand (reflecting some inertia) and some adjustment
towards the desired long run level. The relative price term has been constrained to adjust
imports towards the long run outcome at the same rate throughout the adjustment period.
The equation is specified so that there is no steady state bias. Accordingly, real variables have
been appropriately adjusted for steady state growth (GR) and the interaction with lagged
dependent variables.
The resulting error correction equation is as follows:
4.15
The Goods Market
c2
]
4
+ a 1 [ ln(DDMGS) - GR]
ln(MGS) = (1- a 2 ) [GR +
PMGS (1 + RTMGS)
- a 0 c 1 ln
PDDMGS
+ a 2 ln[MGS(-2)]
MGS(-1)
ln
DDMGS(-1)
-a0
PMGS(-1) (1 + RTMGS(-1))
- c 2 QTIME(-1)
- c 0 + c 1 ln
PDDMGS(-1)
Joint Estimation Results: Import Demand
Sample: 1978(1) to 1995(3)
Parameter
Interpretation
a1
a2
a0
c0
c1
c2
income growth
lagged dependent
error correction
long run constant
relative prices
time trend
Estimate
1.670
0.184
0.364
0.099
0.766
0.007
t-Statistic
8.96
2.40
5.55
3.29
4.68
2.99
Diagnostic Statistics (based on single equation estimates)
R2 =0.57
SE =2.91%
DW =1.89
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
3.28
0.63
2.00
0.91
10.92*
6.80*
14.86*
* Indicates the test has failed at the 5% confidence level.
Interpretation
The import demand equation passes the diagnostic tests with the exception of the
heteroscedasticity test. This may reflect the introduction, and the subsequent removal, of
import quotas in the early part of the estimation period (which is difficult to model) or the use by
market participants of import orders/deliveries as a crude means of foreign currency speculation
prior to the floating of the exchange rate.
The estimated import demand equation has the following key elasticities and adjustment lags.
4.16
The Goods Market
•
The income elasticity of import demand is constrained to be one in the long run and is
estimated to be around 1.7 in the short run. This is consistent with imports growing faster
(slower) than demand as the economy booms (slows), while in the long run growing in line
with the economy as a whole.
•
The price elasticity of import demand is estimated to be around -0.77 in the long run and
around -0.28 in the short run.
•
The time trend suggests a secular increase of 0.7 per cent per annum in the import
penetration ratio over the sample period.
•
The error correction term of around 0.36 suggests that imports have a mean lag of around
3 quarters in their adjustment to long run equilibrium.
Import Supply
As a small open economy, Australia is assumed to have no influence on the world price of
imports. That is, import supply is assumed to be perfectly elastic: Australia can import any
amount at a given price. Import prices are modelled as a function of world prices and the
exchange rate - with the addition of some effects of domestic competition on the short term
pass through (to the domestic landed price of imports) by foreign suppliers of movements in the
exchange rate and world price of imports.
Import prices are estimated in an error correction equation. Strictly speaking, the world price of
imports (denominated in Australian dollars) should be the same as the f.o.b. price of imports.
Nevertheless, the ratio of the two price series displays a distinct downward trend from about the
early 1980s. Investigations into the series suggested that this trend was likely to be due to
problems in the data (eg index number problems). For example, the methodologies used to
construct the world price of imports index and the Australian dollar price of imports are
different. Nevertheless, the two series were chosen because they are believed to provide the
most accurate representation of the prices of the goods that Australia has imported over the last
20 years, and this feature could be lost if the series were constructed using a more consistent
methodology. Further details are contained in Edge (1995).
The long run part of the import equation is therefore specified using a logistic growth function,
which allows further reduction in the ratio over the projection period while at the same time
ensuring a stable ratio in the long run. In the long run, the price of imports is given by the
following equation. The second component of the equation is the logistic trend linking world
prices and f.o.b. import prices:
ln( PMGS ) ln( RETWIM ) c 0 LGF
where
c1
LGF
1 c 2 exp( c 3 QTIME )
The ratio of the world price of imports (denominated in Australian dollars) to the f.o.b. price of
imports, together with the fitted logistic growth curve is shown in Figure 5.
4.17
The Goods Market
Figure 5: World Price in $A over F.O.B. Price of Imports
1.25
Index (1989-90=1)
1.20
1.15
1.10
1.05
1.00
0.95
0.90
0.85
0.80
Mar-75
Logistical Growth
Function
Fob Import Prices
Mar-78
Mar-81
Mar-84
Mar-87
Mar-90
Mar-93
In the short run, changes in import prices depend on the contemporaneous change in the world
price of imports, the exchange rate, oil prices, domestic prices (as reflected in wages growth) and
the adjustment to the long run desired level.
The change in world prices and the change in the exchange rate are not constrained to have the
same coefficient. Exchange rates are constantly fluctuating while world prices are less volatile.
As a result, a change in world prices is more likely to be regarded as permanent than a similar
movement in the exchange rate (which could be regarded as 'noise'). Consequently, foreign
import suppliers pass through changes in the world price of imports more readily than they
would for a similar change in the exchange rate.
The change in wages is included to capture domestic price pressures and their effect on pass
through in the short term. That is, foreign suppliers may pass through (to the domestic landed
price of imports) increases in world prices or depreciations in the exchange rate more rapidly
when other domestic prices are rising.
Oil prices are included in the short run dynamics as they appear to have a magnified effect on
import prices. This problem is possibly associated with the use of manufacturing prices as the
conceptual base of the world price of import series. Movements in oil prices are only reflected
indirectly in this series.
4.18
The Goods Market
The price of imports equation is therefore given by:
ln( PMGS ) LGF
a 1 lnWPMGS
a 2 lnWPMPE
1 a 1 a 2 ln RWH / 4
a 3 ln RETWIM
PMGS ( 1) RTWIM ( 1)
c1
a 0 ln
c 0
WPMGS ( 1)
1 c 2 exp( c 3 QTIME )
Joint Estimation Results: Import Supply
Sample: 1978(1) to 1995(3)
Parameter
Interpretation
a1
a2
a3
a0
c0
c1
c2
c3
world price of imports
world oil prices
exchange rate
error correction
long run constant
lgf parameter#
lgf parameter#
lgf parameter#
Estimate
0.737
0.036
0.695
0.252
0.228
-0.661
1.870
0.177
t-Statistic
8.99
3.76
23.10
4.94
43.19
-2.37
2.26
2.68
Diagnostics Statistics (based on single equation estimates)
R2 =0.87
SE = 1.18%
DW = 2.12
Box-Pierce Q (1-8th order autocorrelation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
7.46
2.25
2.93*
0.37
6.83*
5.55*
9.16*
* Indicates the test has failed at the 5% confidence level.
# The logistical growth parameters are estimated using a single equation over the sample period from 1975:1 to 1995:3. The standard errors
were calculated using a heteroskedastic-consistent matrix.
Interpretation
All coefficients are plausible and the equation's goodness of fit is high. Around 25 per cent of
any divergence between actual and desired import prices is dissipated each quarter. The effect
of both exchange rate and world price shocks are almost entirely passed through to import prices
within three years of the shock.
The estimated import supply equation has the following key elasticities and adjustment lags.
4.19
The Goods Market
•
World price and exchange rate movements flow fully into $A import prices in the long
run.
•
A one per cent decrease in the exchange rate will lead to a 0.70 per cent increase in $A
import prices in the same quarter.
•
A one per cent increase in world import prices leads to a 0.74 per cent increase in $A
import prices in the same quarter.
4.20
Section 5
5. The Labour Market
5.1 Background
The labour market balances decisions about the demand and supply for labour made by the
business, public and household sectors. The specification of the labour market includes a
Beveridge curve equation (showing the relationship between the unemployment rate and the
vacancy rate) to endogenise unfilled vacancy data. The Beveridge curve is introduced to
provide a summary indicator of the state of the labour market into the wage equation, as well as
to help to identify the labour demand equation. The Beveridge curve equation uses a logistical
growth function to capture the outward movement in the unemployment/vacancy relationship
which occurred in the 1970s.
Labour demand by private firms is discussed in Section 1 (the Private Business Sector). Total
labour demand consists of labour demand by private firms plus public sector labour demand
(from both the general government and public enterprise sector - see Section 3).
An expectations augmented Phillip's curve is used to explain the behaviour of wages. In
equilibrium, wages are mainly determined by the underlying level of labour productivity. The
difference between the actual rate of unemployment and the non-accelerating inflation rate of
unemployment (NAIRU) is the main determinant of short term movements in wages. A
homogeneity constraint is applied to the wage-price relationship to ensure that a change in prices
leads to an equivalent long-run percentage change in wages. The estimated results suggest that
growth in prices and wages have a fair degree of inertia. A variable has also been included to
account for the varying degree of centralisation of the wages system since 1970.
Labour supply is assumed to be a function of the level of employment on an hours worked basis
(reflecting the so-called encouraged worker effect), a variable time trend and demographic
effects. The equation is specified using the participation rate on an hours worked basis as the
dependent variable. The ABS measure of hours worked reflects both demand and supply of
labour services. Therefore, to ensure that the labour supply equation is identified, the demand
influences have been removed from the measured hours worked series. This was accomplished
by fitting a trend - called the desired level of hours worked (NHLR) - through the average hours
worked (NH) series published by the ABS. A logistical growth function that reflects the sharp
decline in trend average hours worked since 1973 has been fitted to NH. A time trend is used to
capture a general upward trend in the participation rate in the 1980s and a demographic dummy
is used to capture baby boomer effects and increased longevity.
5.2.1 Labour Supply
The participation rate is measured as the ratio of the labour force (NLF) to the population aged
15 to 64 years (NPADA). Total labour supply also depends on hours supplied per person.
Hence, to test for wage effects on labour supply, the measure is adjusted by a proxy for the
desired level of hours worked (NHLR). In the long run, the participation rate is also a function
of:
5.1
The Labour Market
The encouraged worker effect — the employment (NET) to population (NPADA) ratio
adjusted for NHLR to capture this effect in the long run;
A variable time trend — this is included to capture the upward movement in the labour
participation rate over time due to social factors and changing preferences.
-
Over the period (1966 to 1998) this has largely been due to the rise in female
participation, which has more than offset the effect of a decline in male participation,
and increases in education retention rates.
-
The time trend allows for variations in the rate of increase over time and in particular
for the fact that the upward trend must have an upper limit. Obviously the trend
cannot lift the participation rate above 100 per cent. While the functional form of
the trend is assumed, the timing and extent of any changes in trend are driven by the
data; and
Demographic effects — there are two parts to the demographic effects. One is the effect
of the baby boom working through the age cohorts over time leading to variations in the
participation rate. The second is the effect of increased longevity, particularly since the
mid 1970s.2
-
The baby boomer effect is captured by QDEML which is an index generated by
taking participation rates by age cohort in 1996-97 and applying them to population
proportions over time. Population projections from the Retirement Income
Modelling (RIM) group’s POPMOD are used to project QDEML into the future.
-
To abstract from longevity effects, the participation rate is estimated on a 15-64 year
old basis. Increased longevity has raised the proportion of the population in the over
70 age cohort of the population over time. As this cohort has almost no
participation, the increase in longevity has tended to bias down the upward trend in
the participation rate as measured as a proportion of the adult population 15 and
over.
This gives the following equilibrium relationship for the participation rate.
NLF NHLR
NET NHLR
ln
c 0 c1 ln
NPADA
NPADA
TREND ln QDEML
Where:
TREND c0 c1 /(1 exp( c 2 (QTIME c3 )))
c 4 {QTIME [abs(QTIME c5 ) QTIME c5 ] / 2
c6 [4 /(1 exp( abs(QTIME c5 ) QTIME c5 ] /( 2 c6 ))) 2}
In the short run, changes in the participation rate are also assumed to depend on changes in
private business sector and government employment.
2
For a more detailed explanation of the demographic effects see Downes and Bernie (1999).
5.2
The Labour Market
The measure for private business sector employment includes unfilled vacancies (i.e.
labour demand NEBD) rather than employment.
-
This reflects the point made by Debelle and Vickery (1998) that encouraged workers
should be responding to available jobs rather than employment per se.
-
A further problem is that the employment and labour force measures are drawn from
the same survey. Hence the survey error on the employment measure (in this case
private employment demand) will be correlated with the survey error on the labour
force measure leading to overestimation of the encouraged worker effect. To
compensate for this and avoid overestimation of the encouraged worker effect, a
proportion of the residual from the NEBD equation is subtracted from the
employment measure (refer to Downes and Bernie (1999) for a more detailed
discussion).
-
An additional complication is that the encouraged worker effect is likely to have
increased over time as a greater proportion of females have entered the workforce.
To allow for this a dummy variable QNLF based on the proportion of female to total
employment and the difference between the female and male responsiveness is
introduced into the equation.
The response to changes in public employment (NEG=NEGE+NEGG) is allowed to vary
from that of private business employment reflecting possible effects of different
recruitment practices between the public and private sectors. The public sector
employment estimates are drawn from the Survey of Employment and Earnings (SEE
Survey) and hence do not suffer from the survey error problem referred to above.
An error correction specification is used to bring together the dynamic and long-run responses.
The estimated equation is as follows:
NLF (1) NHLR (1)
ln
NPADA(1)
NEBD
a
QNLF
ln
a
6
ld
QPRIV
u_nebd
/
4
NLF NHLR
1
ln
a0
NPADA
NPADA
a QNLF ln NEGG NEGE a 6ld QPRIV
2
NPADA
TREND ln( QDEML ) ln( NHLR )
TREND ln( QDEML ) ln( NHLR )
(1 a 0 )
NET
c7 ln NPADA u _ nebd / 5
Where:
5.3
The Labour Market
TREND c0 c1 /(1 exp( c 2 (QTIME c3 )))
c 4 {QTIME [abs(QTIME c5 ) QTIME c5 ] / 2
c6 [4 /(1 exp( abs(QTIME c5 ) QTIME c5 ] /( 2 c6 ))) 2}
Results
Sample: 1971(2) to 1999(2)
Parameter
Interpretation
a0
a1
partial adjustment
a2
c7
c0
c1
c2
c3
c4
c5
c6
public employment
Employment to population
Constant
LGF lower bound
LGF transitional slope
LGF inflexion
Secondary trend
decay inflexion
pace of decay
private employment demand (NEBD)
Estimate
0.611
0.341
0.145
0.400
1.01
-0.99
-0.15
18.42
0.04
14.47
5.0
t-Statistic
7.03
4.93
1.99
8.27
4.49
-4.95
-10.13
37.06
5.36
12.27
Imposed
Diagnostic Statistics
R2 = 0.985
SE = 0.32%
DW = 1.75
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
*
10.14
7.14*
0.89
0.00
3.11
0.006
3.19
Indicates the test has failed at the 5% confidence level.
Economic Interpretation
The adjustment towards equilibrium is fairly quick, with any difference between the actual and
desired participation rates being closed by about 39 per cent per quarter. The dynamics of the
equation also imply that:
A 1 per cent increase in employment growth relative to population growth will lead to an
increase in labour supply of around a half of a percent in the first quarter falling to 0.4 in
the long run.
Labour supply is roughly twice as responsive to private sector employment growth than to
public sector employment growth in the short run.
Real wage and additional worker effects were tested for in a variety of forms in both the short
run and long run but on the current specification were found to be insignificant.
5.4
The Labour Market
5.2.2 Hours Worked
Hours worked is modelled as a function of output growth, the dwelling cycle (reflecting the high
level of overtime per worker in the construction industry) and the vacancy rate. In the long run,
hours worked move to a supply equilibrium estimated by a logistical growth function. This
gives the long-run equilibrium level of hours worked (NHLR) and reflects trends over time due
to increased part-time work and the sharp movement down in the mid-1970s due to the move to
35 hours as the standard work week.
The estimated hours worked equation is:
a1 GBA GR a 2 GBA 1 GR 1
a 3 ln IDW / IDW 10 GR GR 10
10
2
ln NH a 0
a ln NVA / NLF 100 ln RNU U _ NVA LRNUB
4
c1 bc
LGF
1 a 0 c 0 LGF
Logistical Growth Function: LGF c1 /1 exp QTIME 1 c 2 / c3
Results
Sample: 1971(1) to 1999(2)
Parameter
Interpretation
a0
a1
a2
a3
a4
c0
c1
Partial adjustment
Change in output
Change in output lagged 1qtr
Dwelling Cycle
Vacancy rate
LGF parameter
LGF constant
LGF parameter
LGF parameter
c2
c3
*
Estimate
0.474
0.076
0.163
0.112
0.010
0.134
-0.133
-0.387
24.51
t-Statistic
5.73
1.03*
2.07
1.49*
1.38*
3.15
-3.09
-4.31
13.72
Indicates the test has failed at the 5% confidence level.
Diagnostic Statistics
R2 =0.96
SE =0.52%
DW =2.03
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
17.61*
0.61
0.76
0.00
1.37
5.5
The Labour Market
Y-Hat
Joint
*
0.41
1.53
Indicates the test has failed at the 5% confidence level.
Economic Interpretation
The dynamics of the equation also imply that a 1 per cent rise in output growth will increase
hours worked by 0.23 per cent in the short run although a cyclical pick up in dwelling
construction and vacancies will add to this effect.
5.2.3 Beveridge Curve
Traditionally, unemployment has been decomposed into frictional, structural and cyclical
components. The cyclical fluctuations in unemployment can be mainly attributed to cyclical
fluctuations in employment demand and labour supply, whilst the frictional and structural
elements can be attributed to either wage setting/price setting (insider) factors or search
effectiveness (outsider) factors. Search effectiveness can be effected by a range of factors
including changes in the benefits system, structural changes in industry and occupational
demand relative to supply, the responsiveness of occupational and industry wage relativities to
changes in the pattern of demand, the responsiveness of the education and training system, the
skill composition and level of immigration, the degree of turnover in the labour market and the
duration structure of unemployment. Any reduction in search effectiveness of the unemployed
for whatever reason should be reflected in a rise in unfilled vacancies for a given number of
unemployed and hence an outward movement in the unemployment/vacancy (U/V) relationship
or Beveridge curve.
Hansen (1970) provided the most widely used justification for the existence of the inverse
relationship between the unemployment rate and the vacancy rate. According to Hansen, the
convex shape of the Beveridge curve is caused by the effect that excess supply or excess demand
for labour has on the matching of the unemployed to vacancies. He assumes that there are
always, in a given short period, some employers who do not succeed in finding sufficient labour
to satisfy their demands completely, even though total supply exceeds total demand.
Furthermore, there will always be some members of the labour force who do not succeed in
getting a job even though there is more than a sufficient number of jobs to employ the total
supply. In terms of ordinary demand and supply theory, this means that observed employment
is less than employment demand.
The TRYM approach to estimating the Beveridge curve is based around a dynamic error
correction specification, including a logistical function to capture the structural shift in the
Beveridge curve thought to have occurred in the early 1970s. The logistical function is an
S-shaped curve which acts as a structural break ‘dummy’ variable where the data determines the
size and timing of any shift in the relationship. The inflection point of the function, or timing of
the break, is captured by the c3 parameter, which is estimated rather than imposed. Similarly, c2
reflects the size of the shift while c4 determines the slope of the function (whether the shift is
sudden or gradual). The function indicates a significant outward movement in 1974 in the
unemployment/vacancy relationship (i.e. reduction in search effectiveness). Tests for a
structural break in the 1980s and early 1990s were unsuccessful.
5.6
The Labour Market
The estimated Beveridge curve equation is:
NVA
ln( RNU ) a1 ln
NLF
a 2 LGF
NVA 1
a0 ln RNU 1 c0 LGF 1 c1 ln
NLF
1
Logistical Function: LGF c2 /1 exp QTIME c3 / c4
Results
Sample: 1967(3) to 1999(2)
Parameter
Interpretation
a0
a1
a2
c0
c1
error correction
change in vacancy rate
change in LGF
constant
vacancy rate
LGF parameter
LGF parameter
LGF parameter
c2
c3
c4
Estimate
0.157
-0.206
2.271
1.304
-0.773
0.419
-23.276
0.198
t-Statistic
4.59
-4.96
2.56
12.31
-7.17
2.86
-415.49
4.83
Diagnostic Statistics
R2 = 0.55
SE = 4.78%
DW = 2.2
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
12.06
93.07* #
1.51
0.00
8.48* ##
0.016
8.80* ##
*
Indicates the test has failed at the 5% confidence level.
#
The failure of the test at the 5% confidence level was thought to be attributable to a number of outliers in the residuals in
the first half of the sample period.
##
Inspection of the vacancy data revealed greater volatility in the period prior to 1980. The failure of the test at the 5%
confidence level may therefore be data related. The vacancy data used were from the ABS spliced vacancy series (see data base
documentation for the TRYM model).
5.2.4 Wages
The TRYM approach to modelling Australian wage behaviour, in common with other Australian
models, takes the form of an expectations augmented Phillip’s curve (wage inflation is the
dependant variable and the unemployment level is introduced in a non-linear fashion). Wage
inflation in the equation is adjusted for productivity growth and conditioned on expected
5.7
The Labour Market
consumer price inflation (proxied by lags of past inflation) and the degree of excess demand in
the labour market (measured by the deviation in the level of the unemployment rate from some
NAIRU level). At the heart of the equation is the assumption that those people outside
employment (the unemployed or outsiders) will place downward pressure on wages in periods of
excess supply, and this will restore equilibrium in the labour market.
The TRYM wage equation augments the basic Phillips curve specification with modifications to
allow for the wage behaviour of those inside employment or insiders. Outsiders may be viewed
as imperfect substitutes for insiders for a variety of reasons, including labour market rigidities or
regulations, imperfect information, on-the-job training or significant transaction costs involved
in hiring/firing decisions. In this world, insiders may find their jobs relatively more secure and,
therefore, be less sensitive to the level of the unemployment rate in determining wage claims.
In TRYM, this effect is modelled by a change in the unemployment rate term (RNU), where
the changing risk of unemployment influences insiders' wage claims. When the economy is in
equilibrium and the unemployment rate is stable, wage inflation will be equal to price inflation
plus increases in efficiency. In the long run, real wages are assumed to be primarily determined
by labour productivity. In the short term, wage adjustments to labour market imbalances play a
critical role in determining how the model responds to various shocks.
The difference between the actual rate of unemployment (RNU) and the NAIRU is used as the
main explanator of wage pressure. The NAIRU has been estimated from the wage equation
using historical data commencing in the early 1970s. A dummy variable (Q741) has been
included to account for an apparent shift up in the level of the NAIRU in 1974. Q741 takes a
value of one prior to 1974 and zero thereafter.
There are number of competing theories about why the NAIRU appears to have increased over
time. Working at a highly aggregative level, TRYM does not contain the detail to be able to
precisely distinguish between the different theories. It does appear to be possible, however, to
broadly distinguish between search effectiveness and wage setting explanations of the increase
in NAIRU by the use of unfilled vacancy data. 3 This is done by introducing into the wage
equation a variable (RNUST) which can be viewed as a summary measure of the search
effectiveness of the unemployed. 4 This attempts to capture the impact on equilibrium
unemployment (where the unemployment rate equals the unfilled vacancy rate) of changes in
search effectiveness as evidenced by shifts in the Beveridge curve. This, in combination with
two wage setting parameters (WS and WSo), determine the level of the NAIRU in the model.
The wage setting parameters capture the effect on the NAIRU of factors other than search
effectiveness, associated with the wage bargaining process. For example, they may reflect
insider/outsider factors in combination with institutional features of the wage bargaining system
and/or changes in the reservation wage due to changes in the unemployment benefit system.
This approach gives the following NAIRU term:
3
The specification of the labour market (in particular the incorporation of unfilled vacancies data) enables TRYM to be used to explore a wide
range of issues relating to the link between labour market imbalances and other areas of the economy. For example, if the user establishes or
judges that the NAIRU may move in a given set of circumstances (changes in search effectiveness of the unemployed or wage setting factors)
the model can be used to examine the macroeconomic implications of these movements. For an example of the use of the model in this way
see Stacey and Downes (1995).
4
The Hansen equilibrium unemployment variable (RNUST) is calculated from the long run part of the Beveridge curve equation by setting the
vacancy rate (NVA/NLF) equal to the unemployment rate (RNU) - and solving for RNU (whose value over time then depends on the
logistical growth function). If search effectiveness falls, the level of the vacancy rate for any given unemployment rate will rise and the
equilibrium unemployment rate (where RNU equals NVA/NLF) will increase. The residuals from the Beveridge curve equation are included
in the equilibrium measure so that any unexplained movements in search effectiveness have an immediate effect on the NAIRU.
5.8
The Labour Market
NAIRU ( RNUST WS ) 1 Q741 ( RNUST WSo) Q741
In the short run, changes in average wages per hour worked, ln(RWT/NH), are a function of:
changes in the price of total consumption (PCON) — a homogeneity constraint is imposed
so that changes in prices will eventually be fully reflected in wages; and
changes in the unemployment rate (RNU), weighted for the proportion of employees who
are union members (RUM) to reflect the influence of insiders on wages behaviour.
-
To allow for the possibility of an asymmetric response to unemployment changes,
the coefficient on the change in unemployment term is allowed to vary between
positive and negative changes in unemployment — RNUP and RNUN
respectively. As a result, a sharp increase in unemployment due to an unanticipated
negative shock will have less effect on wages than an equivalent fall so that the
unemployment rate will tend to ratchet up in a recession — consistent with the
observation that unemployment rises sharply during a downturn but takes a long
time to fall in a recovery.
A through-the-year change in an institutional variable (QCC) has also been included to capture
the effects of the varying degrees of centralisation of the wage determination system since 1970.
Accordingly, changes in the institutional arrangements for wage setting directly influence wages.
QCC is assumed to be exogenous. Allowance has also been made for the metal trades wage
decision in the third quarter of 1974 by introducing a dummy variable (Q743).
These features result in the following estimated equation:
1
RWT
ln
ln PCON (1)
2
3
NH 4 (1 a 1 a1 a1 )
a1
ln PCON 2
(1 a 1 a12 a13 )
a12
ln PCON 3
(1 a 1 a12 a13 )
a13
ln PCON 4
(1 a 1 a12 a13 )
a 2 RUM [c1 RNUP 1 (1 c1 ) RNUN 1]
a 3 QCC QCC 4
[( RNUST (1) WS ) 1 Q741 ( RNUST (1) WSo ) Q741 RNU (1)]
RNU (1)
a 5 Q743
a4
5.9
The Labour Market
Results
Sample: 1971(1) to 1999(2)
Parameter
Interpretation
Estimate
t-Statistic
a1
a2
c1
a3
a4
NAIRU
change in prices
change in RNU
asymmetry on RNU
change in centralisation
unemployment level
after 1974
1971 to 1973
0.798
0.0004
0.367
0.018
0.011
6.45
4.05
2.99
2.70
1.51*
2.95
2.85
3.46#
1.94*#
a5
metal decision 74(3)
0.075
#
(WS 3.70)
(Wso 1.83)
#
5.42
t-statistics refer to the estimates of WS and WSo.
Diagnostic Statistics
R2 = 0.61
SE = 1.2%
DW = 2.14
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
*
15.57*
1.79
0.79
0.001
1.41
0.05
2.16
Indicates the test has failed at the 5% confidence level.
Economic Interpretation
The estimated equation implies that:
A permanent reduction in the level of the NAIRU of 1 percentage point would lead to a
temporary reduction in nominal wage growth of about 0.17 of a percentage point per
quarter, other things being equal. Put another way, if the unemployment rate was
1 percentage point higher than the level of the NAIRU, this would result in wage deflation
of between ½ and ¾ of a percentage point per year. However, this is a temporary effect as
nominal wage growth would return to prior levels once the unemployment rate had fallen
to the new NAIRU. As the change in wage levels fed into prices (see the PNC equation
above) the real wage would return to around its initial level.
-
5.10
Similar logic applies to a 1 percentage point increase in labour supply due to say an
increase in female participation or an increase in immigration. In this case
unemployment would rise initially by 1 percentage point generating a temporary
reduction in nominal wage growth of around the same magnitude.
A fall in the unemployment rate of 1 percentage point in one year would imply an increase
in wage growth of around 0.8 percentage points via the change in unemployment term
(again other things being equal).
The Labour Market
-
Therefore, given a one per cent fall in the NAIRU, the speed of adjustment of the
unemployment rate (to the new NAIRU) must be at least one year in order to leave
wage growth unchanged and therefore be non-inflationary. (That is the non
increasing inflation adjustment path for unemployment would stretch over several
years.) This follows from the comparison of the effect from the level term (noted in
the first dot point) with the effect from the change term. 5
An increase in prices of 1 per cent will increase wages by 1 per cent after five quarters
with a 0.3 per cent increase in the second quarter. (Note the coefficient on the
contemporaneous price term is restricted to zero.) An inflation expectations term (FIE)
was tested in the equation but found to be insignificant.6
Wages respond to changes in the institutional environment in the wage determination
system.
The equation implies that the NAIRU has increased from around 2 per cent in the 1960s (the
above equation does not extend back into the sixties but 2 per cent can be inferred from the data
and is the level the Kalman filter estimates arrive at) to around 4 per cent in the early to mid
1970s to around 6.4 per cent in the 1980s and 1990s. The estimate derived from the equation is
shown in Figure 7 below. As discussed above, the change in the NAIRU can be decomposed, at
least from a conceptual point of view, into a change in the search effectiveness of the
unemployed (captured by changes in RNUST) and a change in other factors associated with the
wage setting process (captured by WS and WSo). The estimated value of RNUST (implied by
the estimates of WS and WSo) has risen from levels of around 2.1 per cent prior to 1974 to
around 2.6 per cent more recently. That is, reductions in the search effectiveness of the
unemployed appear to have increased the unemployment rate by around half of a percentage
point since the 1960s. This compares with the estimated increase in the NAIRU of around 4 ½
percentage points since the 1960s and 2 ½ percentage points since the mid 1970s. However,
given the measurement and estimation difficulties involved and the general uncertainty
surrounding the NAIRU estimate itself, this decomposition of the change in the NAIRU should
be treated with caution.
5
Debelle and Vickery (1987) also test for a change in unemployment term in their wage equation but find it insignificant although the form is
RNU/RNU (i.e. decreasing effect with higher unemployment) rather than RNU above. Gruen, Pagan, and Thompson (1999) with slightly
different assumptions do find a significant effect on the speed limit term.
6
A simple modification to the coding in TSP allows the user to specify simulations where wage bargainers are forward looking if a particular
shock or policy change is thought likely to be anticipated.— see Downes and Louis (1996)
5.11
Section 6
6. Financial Markets
6.1 Background
Deregulation of the financial market, the floating of the exchange rate and removal of exchange
controls in the 1980s have all increased the importance of financial markets to economic
behaviour and led to simpler financial market specifications in macroeconomic models.
In comparison with earlier NIF models, NIF88 contained a relatively small financial sector.
This trend has been continued in the development of the TRYM model7, resulting in a simpler
and more transparent treatment of the interaction of the financial market with the real economy.
The financial market as modelled consists of an estimated money demand equation, a money
supply growth rule (these two are combined to form an interest rate reaction function which
gives the 90 day bill rate) and three behavioural identities for:
•
a term structure equation (linking 10 year bond rates with 90 day bill rates and world 10
year bond rates);
•
a forward looking inflationary expectations identity which determines current inflationary
expectations from the equilibrium price level (assumed to be the price level obtained from
the steady state version of the model 10 years hence), and
•
an uncovered interest rate parity condition for the current exchange rate (RETWI), which
determines RETWI from the future equilibrium exchange rate (RETWIX) derived from
the steady state version of the model.
6.1.1 Money Demand and Short Term Interest Rates
The literature on money demand equations over the last two decades is extensive. It has
concentrated on the instability of relationships, the impact of financial deregulation and
innovation and the relevance of particular definitions of money aggregates. These studies have
tended to highlight the unsatisfactory nature of most existing money demand equations.
However, from a modelling viewpoint, some tool is required to link the financial sector to the
real economy and a money demand equation as an interest rate reaction function has traditionally
achieved this purpose. Money is also required in a macroeconomic model in the analysis of
fiscal policy. Transparent analysis of the interrelationships between the various macroeconomic
markets is an important requirement of such models.
A simple money demand function is estimated. The inverted long run component of this
equation is used as a simple, transparent and stable default monetary policy rule to determine the
7
Some of the reduction in size is illusory. The TRYM financial sector is not necessarily less detailed. It is, in fact, more detailed in terms of
portfolio allocation of foreign liabilities. The difference mainly comes from behavioural equations in NIF being replaced by identities in
TRYM. The identities, in turn, are normally driven by simplifying assumptions, eg that risk adjusted rates of return are equalised across
sectors.
6.1
The Financial Market
current level of short run nominal interest rates. A rise in nominal demand relative to money
supply will increase the 90 day bill rate.
The money supply measure used is that of transaction balances (FM1Z). This aggregate is
defined as currency in the hands of the non-bank private sector plus non-interest bearing current
deposits of all banks plus a fixed proportion of interest bearing current accounts of all banks.
•
This aggregate attempts to measure money balances held for transactions purposes rather
than as a store of wealth. It therefore concentrates upon traditional payment instruments
such as cash and cheques. The spread of EFTPOS facilities will gradually render this
definition less relevant.
•
The weighting on interest bearing current accounts reflects the fact that these
accounts earn a return and therefore may be held as a store of value rather than for
transactions purposes. Weighting these deposits takes some account of their own interest
rate effect, without adding additional interest rates to the model. An appropriate weight on
current accounts may be one minus the ratio of their own yield to the yield on a benchmark
non-monetary asset. The fixed weight has consequently been estimated at 1/3 based upon
the average yield on interest bearing cheque accounts (own yield) and 90 day bank bills
(benchmark yield).
6.2 Equation Specification, Results and Interpretation
6.2.1 Money Demand
In the long run, the ratio of money to nominal transactions (FM1Z/GNEAZ), or the inverse of
money velocity, is modelled as a function of nominal short term interest rates (RI90) and a shift
dummy for structural change (Q813). The shift dummy allows for the reduction in the
minimum maturity requirement on trading bank certificates of deposit from 90 to 30 days in
August 1981 (Q813 set to 0 from 1981(3) and 1 before). This change is likely to have made
money substitutes more attractive (lowering money demand). Q813 should, therefore, have a
positive coefficient.
FM1Z
ln
= - c 0 - c 1 RI90 + c 2 Q813
GNEAZ
This relationship will not hold instantaneously due to adjustment lags. An error correction
dynamic specification was estimated relating changes in real transaction balances to changes in
real demand, adjusted for steady state bias, and the deviation from the long run desired level.
The following equation was estimated:
FM1Z
ln
= a 1 [ ln(GNEA) - GR]
PGNEA
FM1Z(-1)
- a 0 ln
- c 0 - c 1 RI90(-1) + c 2 Q813(-1)
GNEAZ(-1)
6.2
The Financial Market
Results
Sample: 1975(3) to 1995(3)
Parameter
Interpretation
a1
a0
c0
c1
c2
nominal transactions
error correction
long run constant
interest rates
dummy variable
Estimate
0.673
0.322
0.815
0.014
0.074
t-Statistic
3.84
5.38
26.53
6.25
4.92
Diagnostic Statistics
R2 =0.43
SE = 1.86%
DW = 2.01
Box-Pierce Q (1-8th order auto correlation)
Jarque-Bera Test for Normality
Chow Test for Parameter Stability
Ramsey's Reset Test
Breusch-Pagan Heteroscedasticity Tests:
Trend
Y-Hat
Joint
6.07
0.25
2.06
1.09
1.21
0.87
2.92
* Indicates the test has failed at the 5% confidence level.
Interpretation
The equation passes all of the diagnostic tests, and seems to have well determined coefficients.
The coefficient on the dummy term is significant, suggesting that the demand for money
structurally shifted in the September quarter 1981. The timing of this shift may not be precisely
determined by the dummy variable as many structural changes took place in the early 1980s,
such as deregulation of bank deposit rates (December quarter 1980), the move to a tender system
for Treasury bonds (September quarter 1982), the more widespread use of ATMs and changes to
various regulations on financial institutions' borrowing practices.
The interest sensitivity of real money balances is significant and would seem to be a plausible
estimate, suggesting that increasing short term interest rates by 1 percentage point would
decrease the demand for money by around 1.4 per cent in the long run.
Estimation results did not support the inclusion of changes in short term nominal interest rates
(RI90). The term was usually implausibly signed and insignificant, perhaps reflecting lags in
the translation of changes on short term rates to rates available to small investors.
6.2.2 The Default Monetary Policy Response Mechanism
TRYM has a default monetary policy reaction function, but it is just one possible way of dealing
with monetary policy in the model. Whether the default reaction function is used depends on
the purpose for which the model is being used. As outlined in the Macroeconomics of TRYM,
the default reaction functions are rarely used within Treasury. For example, when TRYM is
being used in the Treasury forecasting process the default policy reaction functions are switched
6.3
The Financial Market
off and the profiles for monetary and fiscal policy variables are specified by assumption.
Similarly, when TRYM is used in policy analysis, it is possible to use an optimal control
algorithm to determine policy responses rather than the default functions.8
The default interest rate reaction function determines how interest rates respond to developments
in the economy and interventions by the monetary authorities where interest rates have not been
specified by assumption and where the optimal control algorithm is not being used. Interest
rates change in response to fluctuations in demand, saving and investment, and injections or
withdrawal of funds by the monetary authorities, among other things. The way in which
interest rates react to a demand shock, for example, depends or whether the monetary authorities
accommodate the shock (maintain a stable short term interest rate through a change in liquidity)
or whether the authorities take a non-accommodating stance (maintain the money supply,
allowing short term interest rates to adjust).
•
Traditionally, macroeconomic models invert a money demand function to form an interest
rate reaction function. The absence of a contemporaneous interest rate change term in the
TRYM equation precluded inverting the money demand function to obtain the short run
reaction of interest rates to changes in nominal transactions (essentially nominal GNE)
relative to money.
•
In the TRYM model, it is assumed that the money supply is set to grow at a rate that
accommodates the underlying supply growth of the economy plus an exogenous inflation
target. This money supply level is substituted into the long run component of the money
demand equation which is then inverted to form an interest rate reaction function.
This results in the interest rate reaction function:
RI90X =
1 FM1Z
- ln
- c0 + c2 Q813
c1 GNEAZ
The crucial parameter in this function is the interest sensitivity of money demand (c1). The
function implies that a decrease in nominal transactions of 1 per cent would decrease short term
interest rates by 0.68 percentage points.
This formulation of the interest rate reaction function means that, in the absence of exogenously
imposed changes to the money supply, the default in model simulations is for monetary policy to
be non-accommodating. That is, money supply grows at a constant rate equal to the underlying
growth of supply plus an exogenous inflation target. The interaction of this money supply
growth rule (corresponding to a medium to long run inflation target of the monetary authorities)
and the quarter-by-quarter demand for money determined by the model determines the
quarter-by-quarter level of short term interest rates. The money growth rule does not
necessarily represent how monetary policy would or should be conducted. The main
uncertainties surrounding the interest rate reaction relate to how the monetary authorities will
react and whether the inverted money demand equation adequately captures the interest rate
response to non-accommodated fluctuations in demand.
There is a significant degree of uncertainty over the short term response of interest rates to a
demand shock. This follows from the fact that the interest rate reaction function is derived from
8
Refer to Louis (1995) for a more deatiled discussion of the optimal control algorithm.
6.4
The Financial Market
the long run component of the money demand function. It might, thus, be regarded as a desired,
or full information, response.
Moreover, while monetary policy is assumed to be
non-accommodating over the medium term, it might be accommodating in the short term due to
lags in recognising movements in demand. Given this uncertainty over the timing of the
response, a parameter called MPOLRES has been included to allow partial adjustment of short
term interest rates (RI90) towards full information short term rates (RI90X). If MPOLRES is
set to one then interest rates respond immediately to changes in demand. If MPOLRES is set to
around 0.5, RI90 lags RI90X by one to two quarters. Values close to zero would be consistent
with accommodating monetary policy in the short to medium term but with interest rates
adjusting consistent with non-accommodating policy in the long run.
RI 90 = (1- MPOLRES) RI 90(-1)+MPOLRES RI 90X
6.2.3 Inflationary Expectations and the Exchange Rate
Inflationary expectations and the exchange rate are modelled assuming that economic agents
display some form of forward looking behaviour. The model assumes that:
•
agents in the financial market know enough about the fundamental structure of the
economy to form judgements about the equilibrium exchange rate. In the short run they
may make errors (sometimes systematic) but in the long run they are model consistent; and
•
agents assume that the equilibrium exchange rate will be achieved within the next ten
years.
6.2.4 Exchange Rate Identity
The exchange rate is modelled using a long run uncovered interest parity identity. This identity
relates the deviation in the quarterly exchange rate from its long run equilibrium level
(RETWIX) to the differential between the Australian ten year bond rate (RIGL) and that for the
rest of the world (WRIGL), adjusted for the average differential between Australian and world
real interest rates since the 1980s (RIP).
RIGL -WRIGL - RIP
RETWI = RETWIX(+40) exp10 ln 1
100
•
In the short run, the exchange rate will jump in line with changes in ‘fundamental factors‘
that affect the equilibrium exchange rate or with changes in interest rate differentials. In
the absence of further shocks, it will then move in parallel with movements in long term
interest differentials towards its new equilibrium level (RETWIX). Real long term bond
yields, in turn, move in accordance with changes in real short term rates (as discussed in
Section 6.2.6).
•
In the long run, interest differentials are zero (adjusted for the historical real interest
differential) and the exchange rate is at its equilibrium level.
6.5
The Financial Market
6.2.5 Inflationary Expectations Identity
Inflationary expectations are formed by agents looking forward ten years to the equilibrium price
level (PGNEAX), comparing this with the current level of prices (PGNEA) and then evaluating
the average rate of inflation over the coming ten years.
1
PGNEAX(+40)
FIEX = exp ln
100 -100
PGNEA
10
As with the interest rate reaction, actual inflationary expectations FIE are linked to FIEX using a
partial adjustment mechanism:
FIE (1 a1 ) FIE (1) a1 FIEX
The default value for a1 is set at 0.9.
6.2.6 Ten Year Bond Yield Equation
The final component of TRYM’s financial sector is the long term bond yield equation. It relates
the yield on domestic ten year bonds (RIGL) to world ten year bond rates (WRIGL), world
inflationary expectations (WFIE), local inflationary expectations (FIE) and domestic short term
interest rates (RI90).
As a small open economy with free capital flows the world real long bond rate is the main
determinant of the domestic real long bond rate in the medium to long run. For modelling
purposes, an interest rate differential (RIP) has been included to take account of the apparent
persistent differential between the model’s measures of domestic and world real interests rates
since the early 1980s. This should not be interpreted as a risk premium since the differential is
likely to have been influenced by measurement problems involved in deriving world bond rates
and inflationary expectations data, as well as differences in the tax on the differing inflationary
component of the bond rate here and overseas. The differential has been set at 2 per cent, which
is close to the average differential between the model’s before tax domestic and world real bond
measures since 1980.
RIGL = RIGL(-1)+ FIE - FIE(-1)
+ a1 [ RI 90 - FIE - ( RI 90(-1) - FIE(-1))]
+ a2 [WRIGL - WFIE - (WRIGL(-1) - WFIE(-1))]
+ a3 { c1 RI 90( 1) (1 c1 )
[WRIGL( 1) WFIE( 1) RIP FIE( 1)]
RIGL( 1) }
The equation is possibly clearer when rearranged to be expressed in real interest rate terms:
RRIGL = a1 RRI 90
+ a2 WRRIGL
+ a3 [(WRRIGL ( 1) RRIGL ( 1) RIP )
c1 (WRRIGL ( 1) RRI 90 X( 1) RIP )]
6.6
The Financial Market
where:
RRIGL
RRI90
WRRIGL
=
=
=
RIGL - FIE
RI90 - FIE
WRIGL - WFIE
The equation is specified in an error correction format, explaining the real component of the
nominal bond rate. Contemporaneous changes in the equilibrium domestic real short term
interest rate (RI90X-FIE) and in world real bond rates have been included to provide an
instantaneous reaction to any variation in domestic monetary policy and developments in world
financial markets.
The equation is calibrated to emulate movements in bonds in history while preserving sensible
simulation properties for the model as a whole. The error correction structure ensures that, in
the absence of monetary shocks, the domestic real bond rate converges on the world real bond
rate (adjusted for the differential observed in history). The model is particularly sensitive to
values chosen for a3 and c1. The long run parameter c1 determines the extent to which 10 year
bonds respond to movements in 90 day bill rates, while a3 determines the speed of the response.
Both could be expected to vary depending on market expectations of the likely duration of short
term interest rate movements and views about the cause of any particular movement. The
difficulty in modelling long bond rates stems from the fact that these views are unobserved in
history. It is difficult to gauge what information is shaping market expectations. As the long
term interest rate has direct effects in the equations dealing with dwelling investment, business
investment, private consumption and the exchange rate, values chosen for a3 and c1 (as well as
those for MPOLRES above) are important for how the model responds to changes in monetary
policy. Assumptions about these variables play a significant role in determining the interest
rate reaction to a given shock.
Results
Parameter
a1
a2
a3
c1
Interpretation
change in RRI90X
change in world bonds
error correction
weight on RRI90X in long run
Estimate
0.150
0.256
0.15
0.2
t-Statistic
3.67
2.74
N/A
N/A
The treatment of the long term interest rate in TRYM is an important focus for further
development. Recent work (not yet incorporated) has concentrated on improving the measures
used for world bond rates and inflationary expectations and experimenting with various
expectational assumptions in simulating the model.
6.7
References
7. References
Access Economics (1995), The Access Economics Macro Model (Version 3.0 for Windows),
Manual and User’s Guide.
Ando, A. and Modigliani, F. (1963), ‘The Life-Cycle Hypothesis of Saving:
Implications and Tests’, American Economic Review, Vol 53, 55-84.
Aggregate
Antioch, L. and Taplin, B. (1993), Savings, Dwelling Investment and the Labour Market:
Decisions by Households, TRYM Paper No 5, Commonwealth Treasury.
Commonwealth Treasury (1981), The NIF-10 Model of the Australian Economy, AGPS,
Canberra.
Commonwealth Treasury (1984), Proceedings of the Conference on the NIF-10 Model, AGPS,
Canberra.
Edge, R. (1995), ‘Modelling Import Prices in the Treasury Macroeconomic (TRYM) Model’,
Paper presented at the Econometrics Conference at Monash University, 13-14 July.
Henry, K.R. and Wright, J.S. (1992), ‘PRISMOD — Development, Database and Methodology’,
Paper presented at the Australasian Economic Modelling Conference, 2-5 September.
Higgins, C.I. and FitzGerald, V.W. (1973), ‘An Econometric Model of the Australian Economy’,
Journal of Econometrics, Vol 1, 229-265.
Jilek, P., Johnson, A. and Taplin, B. (1993), Exports, Imports and the Trade Balance, TRYM
Paper No 4, Commonwealth Treasury.
Lay, C. and Johnson, A. (1995), ‘The Relative Price Block in the Treasury Macroeconomic
(TRYM) Model’, Paper presented at the Econometrics Conference at Monash University,
13-14 July.
Louis, C. (1995) ‘Control Applications of the TRYM Model’, paper presented to the
International Federation of Automatic Control (IFAC) Symposium, Modelling and Control of
National and Regional Economies, Gold Coast, July.
Murphy, C.W., Bright, I.A., Brooker, R.J., Geeves, W.D. and Taplin, B. (1986), A
Macroeconomic Model of the Australian Economy for Medium-Term Policy Analysis, Office of
the Economic Planning Advisory Council, AGPS, Canberra.
Ryder, B., Johnson, A., Taplin, B. and Jilek, P. (1993), Australia's Trade Linkages with the
World, TRYM Paper No 6, Commonwealth Treasury.
Simes, R.M., Horn, P.M. and Kouparitsas, M. (1988), ‘Equation Listing for the NIF88 Model’,
mimeo. Background Paper No.2 for the Conference, The Australian Macroeconomy and the
NIF88 Model, 14-15 March 1988, CEPR, ANU.
7.1
References
Stacey, G. and Downes, P.M. (1995), ‘Wage Determination and the Labour Market in the
Treasury Macroeconomic (TRYM) Model’, paper to the Economic Society of Australia, Annual
Conference of Economists.
Taplin, B. and Louis, C. (1993), The Macroeconomic Effects of Higher Productivity, TRYM
Paper No 7, Commonwealth Treasury.
Taplin, B., Jilek, P., Antioch, L., Johnson, A., Parameswaran, P. and Louis C. (1993),
An Introduction to the Treasury Macroeconomic (TRYM) Model of the Australian Economy,
TRYM Paper No 1, Commonwealth Treasury.
Taplin, B., Jilek, P., Antioch, L., Johnson, A., Parameswaran, P. and Louis, C. (1993),
Documentation of the Treasury Macroeconomic (TRYM) Model of the Australian Economy,
TRYM Paper No 2, Commonwealth Treasury.
Taplin, B. and Parameswaran, P. (1993), Employment, Investment, Inflation and Productivity:
Decisions by the Firm, TRYM Paper No 3, Commonwealth Treasury.
7.2
