Download Macroeconomic Fluctuations in the UK Economy

Macroeconomic Fluctuations in the UK Economy1
Keshab Bhattarai
Department of Economics
University of Hull
Cottingham Road, Hull, HU6 7RX
and
Basil Jones, Ph.D. (Hull).
Abstract:
We present a simple model that can be useful in analysing the impacts official,
monetary and exchange rate policies over the years. This model can take account of
backward and forward looking expectations on prices and shocks in the demand and
supply side of the economy. We implement the model with a simple VAR model
using quarterly macroeconomic time series data of the UK economy from 1975 to
1999 and find persistency in unemployment and inflation and that the shocks in
demand and supply tend to prolong up to ten periods.
JEL Classification: E12, E30
Key words: Business cycle, UK economy
July 2002
1
Correspondence regarding this paper should be directed to [email protected]. Phone:
44-1482-466483; Fax: 44-1482-466216. We appreciate the Social Science Faculty of the Hull
University for the research grant #500R054. We are also grateful to the University of Essex for
providing us an access to the quarterly macro time series data set for the UK economy in the Essex data
archive through Navidata and MIMAS. Earlier version of this paper has appeared as Hull Advances in
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I. Introduction
Economies tend to grow over time but in an uneven fashion. The aggregate
output, employment, investment, consumption and net exports all grow in good times
and economy is close to full utilisation of resources. Consumers, producers and
government are optimistic about future course of the economy. Such confidence
propels economy forward. However, such optimism suddenly dies out when
pessimistic outlook predominates in bad times. Downward swing continues until
some confidence is restored among investors and consumers. Business planners,
economic forecasters and policy planners study the economic scene intensely to detect
signals of future macroeconomic developments. They are especially concerned with
turning points, those times when the economic cycle reaches a peak or a trough in the
cycle of economic activity. To that end, they watch a number of variables that tend to
move systematically with, or even anticipate the business cycle. Business cycles are
usually studied using quarterly data, in order to reveal enough details within the time
period. In theory these macro time series tend to fluctuate around their long-term
trends. Popular discussions in the UK emphasise the short run fluctuations associated
with business cycles, to the point that cycles can have significant economic and
political repercussions including bringing the governments down at home and a set of
chains of upward or downward movement in the global economy.
II. Literature Review
The classical methods of business cycle analysis pioneered by Burns and
Michell (1946) were successful at uncovering a broad set of statistical regularities
observed among hundreds of economic time series. This massive body of empirical
work provides the dominant background to most academic and policy discussion of
macroeconomic developments. The qualitative features of business cycle behaviour
they described have become the empirical foundation for modern business theory and
set the standards for measuring and defining the business cycle characteristics of
economic data. Insights provided by their methods of business cycle analysis serve as
the foundation for theoretical models of business cycle behaviour and provide a
standard to evaluate those models. For example, early Keynesian models were judged
to be quantitatively successful in part because of the Adelman (1959) finding that
Economic Policy working paper no. 5. All errors and omissions are of our own.
2
these models produced artificial time series whose cyclical properties could not be
distinguished from historical business cycle behaviour on the basis of results
generated using Burns and Mitchell methods.
In order to investigate and measure the characteristics of different series over the
business cycle, it is necessary to define the business cycle and to determine when it
occurs. Burns and Mitchell adopted the working definition proposed earlier by
Mitchell:
Business cycles are a type of fluctuations found in the aggregate economic
activity of nations that organise their work mainly in business enterprises:
A cycle consists of expansions occurring at about the same time in many
economic activities, followed by similar general recessions, contractions, and
revivals which merge into the expansion phase of the next cycle; this sequence
of changes is recurrent but not periodic; in duration business cycles vary from
more than one year to ten or twelve years; they are not divisible into shorter
cycles of similar character with amplitudes approximating their own.
Burns and Mitchell investigated the business cycle characteristics of economic time
series by constructing reference cycle patterns of cyclical behaviour, which compactly
summarise business cycle properties of the data. These reference cycle pattern are the
major tool used to describe the cyclical behaviour of economic time series within the ir
framework. The first step in constructing these reference cycle patterns is to delineate
periods of economic expansion and contraction and determine the specific dates of
business cycle peaks and troughs. This is complicated by the fact that business cycles
are characterised by the co- movements of many economic time series which are not
perfectly in phase. However, peaks and troughs in a majority of individual series tend
to cluster together with some regularity. Reference business cycle peaks and troughs
are selected at the dates of these clusters.
As McCallum (1989) points out, the real business cycle approach to modelling
business cycle behaviour has gained attention for both theoretical and quantitative
reasons. Growing dissatisfaction with the theoretical monetary misperception models
of Lucas (1972) and Barro (1976) led to greater emphasis being placed on alternative
equilibrium type models which could account for business cycle fluctuations. Further,
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the real business cycle approach was indirectly supported by the empirical results of
Sims (1980) and Nelson and Plosser (1982) which implied that nominal (monetary)
shocks are incapable of generating typical business cycle behaviour in economic time
series.
However the most influential and quantitative support for real business cycle models
was presented by Kydland and Prescott (1982). Their work was the first to show
explicitly that a real business cycle model, driven by exogenous shocks to technology,
was capable of generating time series with statistical properties characteristic of post
war U.S. business cycles. Variability in the model’s data as well as co-variability
between output and other variables in the model appeared to “match-up” well with the
corresponding summary measure for similar U.S. economic time series. Kydland and
Prescott (1982) showed that one could account for two-thirds of the U.S. economic
fluctuations with a dynamic stochastic general equilibrium model from which nominal
variables were totally absent, (Kydland and Zarazaga (1997)). They obtained this
result using a variation of the same basic theoretical model economists had been using
time and time again to study economic growth issues. What many economists found
attractive about the Real Business Cycle (RBC) theory proposed by Kydland and
Prescott was that for the first time, a business-cycle theory pointed to the possibility
that the same analytical tools used to address economic growth issues could be used to
address business-cycle questions as well.
Beginning with Kydland and Prescott (1982), RBC theory initially concentrated on
the closed economy and tried to construct a dynamic general equilibrium model,
which mimics the business cycle features of the real economy. These models generate
artificial data, such as output, consumption, investment and employment, from a
model economy whose fluctuations are driven solely by technological shocks.
Researchers then compare the co- movements and volatility of these simulated series
with those present in actual data. Many proponents of RBC models tend to judge the
success or failure of a model on how close these two measures calculated from
artificial data are to their real counterparts.
The real business cycle model is an extraordinarily bold conjecture in that it describes
each stage of the business cycle – the troughs as well as the peaks as an equilibrium,
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Hartley et al. (1997). The real business cycle model does not present a descriptively
realistic account of the economic process, but a highly stylised or idealised account.
This is a common feature of many economic models, but real business cycle
practitioners are bold in their conjecture that such models nevertheless provide useful
quantification of the actual economy. In particular, the real business cycle programme
is part of a larger new classical economics, which is argued to provide satisfactory
microfoundations for macroeconomics in a way that Keynesian models conspicuously
failed to do (e.g. Lucas and Sargeant. (1979)). The claims that new classical models in
general, and real business cycle models in particular provide microfoundations that is
largely based on their use of a representative agent who solve a single dynamic
optimisation problem on behalf of all the consumers, workers, and firms in the
economy. Economic agent s maximize their objective function (utility or profits)
subject to the resource and technology constrains and supply and demand in all
markets are equal.
What are the facts about business cycles
Before the real business cycle models can be tested, we must know precisely what
they are meant to explain. Following Prescott (1986), advocates of real business cycle
models have defined the explanandum of business cycles. Business cycle theory has
traditionally tried to explain what causes output to fall and then rise again. To be sure
when output declines one expects employment, income, and trade to decline as well.
Nevertheless the central fact to be explained was believed to be the decline and the
subsequent recovery and not the co- movements of the aggregate time series.
Two important measures of business cycles are the co-movement of economic
variables and their volatility. While volatility issues, such as the smoothness of
consumption and the magnitude of investment fluctuations, have been continuously
subject to examination within business cycle research, co- movement issues have not
always been emphasised. For example, time series studies often concentrate on single
variable, such as output, to characterise the business cycle – e.g Hamilton (1989) and
Hess and Iwata (1997). An early definition by Burns and Mitchell (1946) highlighted
that expansions occurred simultaneously across many economic activities, followed
by similar general recessions, contractions and revivals, which then merge into the
expansion phase of the next cycle. Fortunately, the early tradition of emphasising the
co-movement of economic variables at business cycle frequencies has been revived in
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real business cycle (RBC) theory.
Interest in the long run effect of business cycles was revived recently as part of the
interest in endogenous growth. While the long run growth was traditionally thought
of as entirely driven by some unexplained trend of technical progress, endogenous
growth theory explicitly takes into account the fact that technical progress itself has
economic determinants and depends on the incentives to innovate, to acquire
education and on the acquisition of knowledge as a by-product of economic activity.
Since the basic claim is that the business cycle is potentially as much a product of the
permanent as the transitory component, it will be apparent that the “policy” one needs
to discuss is not just monetary and fiscal settings but also actions designed to
influence the longer-term performance of the economy. From the perspective of this
paper, there seem to be three things that policy might aim at in influencing the cycle –
the long-term growth rate, the degree of persistence of shocks, and the standard
deviation of growth rates.
The long-term growth rate is generally regarded as a phenomenon associated with
growth in output and technical change. While growth in capital and labour is
predictable, arrival of technology is stochastic. While increased R&D may be
important for it, creating an environment that is conducive to the adoption of
innovations, such as various micro-economic reform measures, is possibly just as
important. A study of the factors that lead to an improvement in long-run growth
potential is therefore a study of how to mitigate the classical cycle, although it would
do nothing for the growth cycle.
A second dimension along which policy may modify the cycle is through the degree
of persistence of shocks. Whether policy can influence this depends a great deal upon
the source of the persistence and it is here that one needs to be able to flesh out the
parametric statistical model with some economic structure. If the RBC is correct, the
persistence derives from the fact that a TFP shock remains for a long time.
One way of categorising the post war literature on models of business cycles is into
deterministic approach and stochastic approach, Muellbauer (1997). In the former
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category come simple multiplier/accelerator models of aggregate demand, typically
ignoring the supply side, which generate second-order differencing equations. Less
convincing are the aggregate demand models with floor and ceilings (Barnett et al.,
1989 and Grandmont 1991, 1993). Real business cycle (RBC) models can be seen as
the attempt to account for observed fluctuations without appeal to phenomena such as
sticky wages or lagged responses, which characterise approaches emphasising the
demand side.
The process of verifying, sharpening or refuting the real-shock account of business
cycles has generated a large body of theoretical and empirical research. This
framework assumes that the macroeconomy usually obeys simple behavioural
relationships but is occasionally disrupted by large “shocks,” which force it
temporarily away from these relationships and into a recession. The behavioural
relationships then guide the orderly recovery of the economy back to full
employment, where the economy remains until another significant shock upsets it.
There are controversies about the methods used in empirical research on business
cycles Quah (1997), Gregory and Smith (1997). There are criticisms of business cycle
research made by econometricians and criticisms of econometricians made by
business cycle researchers who use calibration and the heavy use of simulations to
derive operating characteristics of models. These researchers argue that such practice
allow one to focus on the important economic issues and makes transparent the
economic significance of particular failures of their models.
III. UK Macroeconomic Series
In this section, we look at certain key statistics of the UK economy over a period of
time so as to gain some insights into the performance of the UK economy. It is useful
to examine the performance of the UK economy against the background of the aims
of government’s macroeconomic policy. They are full employment, stable prices, and
economic growth and balance of payments equilibrium. Apart from these four
objectives, we can also recognise that the government may also have additional
objectives concerned with such factors as for example the distribution of income, the
size of the government sector within the economy ect.
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We commence by looking at the extent to which the four policy objectives noted
above were achieved. Table 1 shows the growth of national income, the rate of
inflation and the current account balance over the period 1975-1999. The measure of
national income is the growth of real GDP at market prices. Inflation is the retail price
index excluding mortgage interest payment (underlying rate of inflation). From Table
1, it can be seen that national income has increased and growth rates were positive
throughout the period. There are however considerable variations in individuals years
ranging from –2.2 percent in 1980 to 5.1 percent in 1988 and 2.1 percent in 1999.
These growth rates provide one measure of the trade cycle (stop/go phenomenon)
which existed during this period, (see figure 1).
Fig.1 - Deviation of output from equilibrium
8
6
4
2
0
19
7
19 0Q2
7
19 1Q3
72
19 Q4
7
19 4Q1
75
19 Q2
7
19 6Q3
7
19 7Q4
79
19 Q1
8
19 0Q2
8
19 1Q3
82
19 Q4
8
19 4Q1
85
19 Q2
8
19 6Q3
8
19 7Q4
89
19 Q1
9
19 0Q2
91
19 Q3
9
19 2Q4
9
19 4Q1
95
19 Q2
9
19 6Q3
9
19 7Q4
99
Q1
-2
-4
-6
-8
Source: Navidata 3.1, ONS, 2000
8
Turning to the rate of inflation, this averaged to 4.70 percent per annum over the full
period. In general rates of inflation tended to trend upwards throughout the period up
to about 1980 although there were notable variations from this trend for individual
years for example the rate of inflation rose and then dropped by approximately eight
percentage points for the years 1977-1978. The highest figure recorded was 16.9
Fig. 2: RPI
30
25
20
15
10
5
19
98
Q4
19
97
Q2
19
95
Q4
19
94
Q2
19
92
Q4
19
91
Q2
19
89
Q4
19
88
Q2
19
86
Q4
19
85
Q2
19
83
Q4
19
82
Q2
19
80
Q4
19
79
Q2
19
77
Q4
19
76
Q2
19
74
Q4
19
73
Q2
19
71
Q4
19
70
Q2
0
Source: Navidata 3.1, ONS, 2000.
percent for the years 1980 and the lowest figure 2.3 percent in 1999. The highest
sequence of inflation was for the period 1975-1981 where inflation for each year
exceeded 10 percent per annum. It is also notable that the swings in the rate of
inflation did not precisely synchronise with cycles in the growth rate of national
income, (see figure 2).
The third key macroeconomic statistics refers to the terms of trade. For the whole
period, the current account of the balance of payments showed a mean deficit of
£…… million. The size of the deficits increased over time. One reason for the poor
trade performance of the UK economy is the relatively higher rates of inflation
experienced in the UK compared with other developed nations. The Pound sterling
remained slightly overvalued which resulted in a loss of competitiveness of UK
exports with the ROW. This is evidenced by the pattern of the sterling exchange rate
compared with two other major currencies, i.e. the $ and Dmark. It is evident from
Table 2 that the period 1970-1999 is characterised by a marked depreciation of the
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pound against the two currencies.
Table 1: Key Statistics of the UK Economy 1975-1999
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Growth of
Inflation
national income
% per annum
% per annum
2.8
2.3
3.5
2.7
-2.2
-1.3
1.9
3.8
2.4
3.7
4.2
4.5
5.1
2.1
0.7
-1.5
0.1
2.3
4.4
2.8
2.6
3.5
2.6
2.1
16.7
15.9
8.6
12.6
16.9
12.2
8.5
5.2
4.5
5.2
3.6
3.7
4.6
5.9
8.1
6.7
4.7
3
2.3
2.9
3
2.8
2.6
2.3
Terms of Trade 2
-0.0029
0.0056
0.0165
0.0074
0.0174
-0.0004
-0.0015
-0.0004
-0.0026
0.0030
-0.0119
-0.0007
-0.0001
-0.0045
-0.0006
0.0030
0.0041
0.0016
-0.0065
-0.0074
0.0038
This is particularly evident against the Dmark, the rate of exchange for which
declined from 5.45 Dmark per £1 in 1975 to 2.91 per £1 in 1998. The broad trend in
the movement of the effective exchange rate since 1975 can be divided conveniently
into four phases. The first phase from 1975 to 1977 was a period of steady decline and
rapid depreciation. In the second phase between 1978 to 1981 the effective exchange
rate generally appreciated in value. The third stage from 1982 to 1996 was one in
which the effective exchange rate has once again trended downwards reaching a low
2
Terms of trade = (percentage change in export prices)*exports/GDP – (percentage change in import
10
in 1996. Although the trend since 1982 has been downwards, it is evident from Fig3.
That there has been considerable movement about the trend, with spells of
depreciation followed by partial appreciation and this cycle repeated, though not with
any degree of regularity. The movement of sterling relative to individual currencies,
particularly the dollar, has been more volatile although until 1981 the broad trends
were very similar to that of the effective exchange rate.
Fig. 3 - Deviation of real exchange rate from
trend
4
3
2
1
0
19
70
Q
19 2
72
Q
19 1
73
Q
19 4
75
Q
19 3
77
Q
19 2
79
Q
19 1
80
Q
19 4
82
Q
19 3
84
Q
19 2
86
Q
19 1
87
Q
19 4
89
Q
19 3
91
Q
19 2
93
Q1
19
94
Q
19 4
96
Q
19 3
98
Q2
-1
-2
-3
Source: Navidata 3.1, ONS, 2000
After 1972, the UK balance of payments position underwent important changes, most
of which can be traced back to three major factors. First, the floating of sterling in
1972 following the end of the Bretton Woods era to the general international move
towards floating exchange rates by the major industrial countries. This in turn
changed considerably the role and constraints associated with balance of payments
management. The second factor was the UK’s accession to the EC in January 1973
which it is estimated, resulted in the UK running a large balance of payments deficit
or a small surplus than would other wise have been the case. The third and probably
the most immediate importance for the UK, was the discovery and subsequent
exploitation of North Sea oil. Accompanying these developments were the dramatic
changes in the world price of oil: sharp increases in 1973 and 1979, followed by a
prices)*imports/GDP taken from the World Bank database.
11
precipitate slump in 1985-86.
Table 2: Key Exchange Rates 1975-1999
Year
$ per £1 Dmark per £1
1975 2.2198
5.447
1976 1.8046
4.551
1977 1.7455
4.051
1978 1.9197
3.85
1979 2.1225
3.888
1980 2.3281
4.227
1981 2.0254
4.556
1982 1.7489
4.243
1983 1.5158
3.87
1984 1.3364
3.79
1985 1.2976
3.784
1986 1.4672
3.183
1987 1.6392
2.941
1988 1.7796
3.124
1989 1.6383
3.079
1990 1.7864
2.876
1991 1.7685
2.925
1992 1.7665
2.751
1993 1.5015
2.483
1994 1.5329
2.481
1995 1.5783
2.26
1996 1.5617
2.35
1997 1.6382
2.84
1998 1.6574
2.914
1999 1.618
Source: Navidata, ONS 2000
3
£ Sterling
effective exchange
rate 3
129.6
112.3
106.8
107.2
113.1
124.4
127.8
123.3
115.6
111.4
111.3
101.4
99.3
105.3
102.3
100
100.7
96.9
88.9
89.2
84.8
86.3
100.6
103.9
103.8
Formula for the real exchange rate: cpi(local)/cpi(USA)*official ER
12
16
14
12
10
8
6
4
2
0
19
70
Q2
19
71
Q4
19
73
Q2
19
74
Q4
19
76
Q2
19
77
Q4
19
79
Q2
19
80
Q4
19
82
Q2
19
83
Q4
19
85
Q2
19
86
Q4
19
88
Q2
19
89
Q4
19
91
Q2
19
92
Q4
19
94
Q2
19
95
Q4
19
97
Q2
19
98
Q4
%
Fig. 4: - URATE
Year
Source: Navidata 3.1, ONS, 2000.
Following the floating of sterling, 1973-76 saw a worsening of the current account
deficits of unprecedented magnitudes because the pound remained overvalued.
Overall, however, the UK was able to absorb a sequence of large current account
deficits without having to undertake a major adjustment program as would otherwise
have been required in the absence of North Sea oil. The turnaround in the balance of
payments was eve n more dramatic than its earlier deterioration. The oil price rise of
1979 coincided with the coming on stream of major North Sea oil fields. Oil exports
doubled between 1979 and 1981, even as non-oil visible balance deteriorated further.
The net result was a shrinking of the overall deficit on visible trade which together
with the continued strengthening of the invisible balance, combined to produce a
sequence of record current account surpluses from 1980-85. The fall in the price of oil
in 1985-86 coincided with the levelling-off of output from the North Sea and resulted
in a fall in the net oil contribution to the visible balance. From 1986 onwards, the UK
returned to the situation of the mid-seventies in experiencing significant current
account deficits despite having substantial reserves of North Sea oil.
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The fourth major objective discussed above is the target for full employment. To do
this we present a graph for quarterly unemployment rate for the period 1970-1999,
Fig. 4. Unemployment has a high cost. Individuals who are unemployed feel a loss of
self-esteem and dignity, which cannot be measured. They are more prone to illness
and their families suffer from poverty. The output that the unemployed would have
produced and the income they would have earned are lost forever. By being
unemployed the worker’s career development is damaged and their skills deteriorate.
The civilian labour force has grown by over two million since 1971 and that this was
almost entirely due to the increase in the number of fe males. The male labour force
has remained virtually static throughout this period. In 1971 females accounted for
37.3 percent of the workforce, by 1999 this figure rose to 42.5 percent. Much of the
increase in the female activity rate has been associated with an increase in part time
employment particularly for married women. Between 1948 and 1968, the annual
unemployment rate in the UK averaged 1.8 percent and never rose above 2.6 percent.
The following two decades witnessed a remarkably different story, especially during
the 1980s when a new dimension was added to the post war history of UK
unemployment. The unemployment rose from 2 percent in 1974 to 4 percent in 1979.
This was followed by a remarkably sustained increase to over 11 percent in 1986,
when more than 3 million people were recorded as claiming unemployment benefit.
During the 1980s all industrialised countries were experiencing high unemployment.
The fact that there was an upward shift in unemployment was wide spread across the
world’s major trading nations suggests the existence of a common cause. First the
sharp and unprecedented increase in the price of oil in 1973/74 from three dollars to
ten dollars a barrel had serious adverse effects on the balance of payments of the
major industrial nations, all of which relied heavily on imported oil. This oil price
increase also provided an unwelcome impetus to inflation. In order to rectify their
balance of payments deficits on current account and to deal with the threat of
inflation, these nations deflated their economies by operating more restrictive fiscal
and monetary policies. Similar polices were introduced in the aftermath of the
increase in oil prices in 1979/80, this time from 13 dollars to 29 dollars a barrel.
The effect of these two world-wide recession on the UK unemployment rate was an
increase from 2.2 percent in 1974 to 5.2 percent in 1977; and an increase from 4.5
14
percent in 1979 to 11.3 percent in 1983. The UK’s balance of payments and
unemployment rate should have been helped by the oil price increase of 1979/80 at a
time when it became a net exporter of oil. The answer is that the sudden increase in
the value of the UK’s oil reserves coupled with the tight monetary policy during
1980/81 caused a substantial appreciation of the sterling exchange rate. The dollar
sterling exchange rate rose from 1.74 dollars per pound in 1977 to 2.33 dollars per
pound in 1980. This led to a sharp decline in the UK’s export competitiveness, which
had particularly harmful effects on the manufacturing sector.
Different components of GDP including consumption, investment, government
expenditures and revenues, exports and imports account for its cyclical volatility. For
virtually all countries and time periods, all components of GDP except consumption
generally are more volatile than GDP. This finding is consistent with the presence of a
consumption smoothing motive, that is the desire for consumers to maintain a
relatively smooth stream of consumption over time in the face of volatility in their
income and wealth.
Table 3: Variability of Key Macro Variables over the Cyclea
output
consumption
Investment
Gov.
Exports
Imports
spending
Prices (GDP
deflator)
% standard deviation relative to standard deviation of output
EU
1.12
0.87
2.23
0.47
2.35
3.26
1.00
Japan
1.66
0.73
2.80
3.76
6.99
11.67
1.98
USA
1.73
0.71
3.01
1.18
6.82
5.17
0.90
a
Variability is measured as the standard deviation of seasonally adjusted and detrended values using the Hodrick-
Prescott filter.
Source: Burda and Wyplosz (1997) Ch. 14 2 pp. 361
Prices are less variable than output, which is consistent with the keynesian
assumption. Consumption is smoother than output, output is smoother than
investment expenditure and trade (exports and imports) represents the most unstable
component of GDP.
15
IV. The Model
Though the research on business cycle, either in the traditional sense or in the
multiplier accelerator settings or in the real business cycle tradition has gone much
further, a very simple model based on IS-LM or AS-AD analysis is still more popular
among the business cycle practitioners. A very simple business cycle model should be
able to show the effect of fiscal policy (budget deficit), movements in the real
exchange rates and real interest rates on output. Beside these demand side effects it
should also explain formation of prices and its effect on outputs in the supply side.
Furthermore, some macroeconomists have advanced the idea that shocks to the supply
side or “real” factors cause many, if not most of the ups and downs in the economy.
This idea could be captured by shocks either on the demand or the supply sides in this
simple model. Thus this simple models takes account of the traditional
macroeconomic notion that changes in the aggregate demand causes most of the
fluctuations on the one hand and effect of real disturbances through shocks on the
other. While some shocks are amenable to policy corrections others are immediate
and have no clear policy options before such shocks hit the economy.
Our simple business cycle model consists of four equations that explain
aggregate supply, aggregate demand, prices, and unemployment rate. We also
incorporate four policy variables: budget deficit, real exchange rate, real interest rate
and core inflation. Thus this model is a useful frame work to study the impacts of
fiscal, monetary, exchange rate policies and effect of trade unions and labour market
conditions in price setting process. A formal presentation of the model underlying the
AD-AS framework with underlying IS and LM analysis is as following.
We assume an autoregressive process for aggregate prices Pt = Pt −1 (1 + π t ) . This
∗
implies that the real exchange rate to be λt =
∗
Et Pt
(1 + ε t )(1 + π t )
, λt =
where ε is
Pt
1+πt
the (expected and actual) rate of change of the exchange rate and π * is the foreign rate
of inflation assumed to be constant. Thus equilibrium value of the real exchange rate
λ, is influenced by the nominal as well as the domestic and foreign inflation. Given
these relations between the past and current prices as well as the relation between
domestic and foreign inflation in determining the competitiveness of the economy the
16
aggregate demand consistent with goods market equilibrium given by the regular IS
curve, under the Mundell Flemming assumptions is:
−
−
−
−
Yt − Y = βFPt + γ ( λt − λ t ) − ϕ ( i − π t ) + s1t
(1)
where a bar above a variable denotes its long run value, ie Y . These long run trends
are given by Yt = β 0 + β1t t + ut , linearised around the long-run equilibrium. Here,
−
FPt is a fiscal policy variable (budget deficit), λt − λ t represents the exchange rate
−
−
policy, and i − π t represents monetary policy. Finally the last term in (1) s1t
represents shocks in aggregate demand. Thus fiscal, monetary and exchange rate
policies can influence aggregate demand. Parameters β, γ, and ϕ explain the relation
of fiscal, exchange rate and monetary policy on aggregate demand their expected
signs are β >0, γ <0, and ϕ <0. Thus this model summarises the effects of internal and
external balances on demand side of the economy in the short run.
Analysis of supply side of the economy is different in the short and long run. Output
is determined by the amount of capital and labour inputs as well as the level of
technology in the long run. However, business cycle models emphasise short run
factors in determining the supply side of the economy. The short run supply side
factors are often represented by the deviation of general prices from the core
underlying price structure. In this tradition a simple aggregate supply is given by the
combination of inflation and output as explained by the following equation
−
−
π t =π t +b1 (Yt − Y ) +s 2t
(2)
where π t = core inflation rate in t, Y = trend (equilibrium) level of output
s 2,t = one-off supply shock (e.g. oil price increases, indirect tax increases). It is
assumed that b1 > 0 and that s 2,t represents a random shock with expected value equal
to zero.
Core inflation depends on wage bargaining along with increase in productivity and
mark-up practices among the monopolistic firms. In simple form it captures both past
experience and future expectations. A simple price setting process that incorporates
17
both backward and forward looking expectations can be summarised by the following
equation:
−
π t = απ t+1 + (1 − α )π t −1
(3)
where π t = core inflation in t. The constant α is restricted between zero and one so
that 0 < α < 1. Thus the core inflation is higher when the economy is overheating as
given by (2) and lagged term π t −1 in equation (3) and the expectation of future by the
economic agents (particularly the employers and employees) in the economy as
captured by π t +1 .
Our last equation relates to the relation of unemployment rate with the output
gap on the one hand and difference between foreign and domestic inflation on the
other. It can be presented as following.
.
−
Ut
= γ 1 (Yt − Y t ) − γ 2 (π t − π t∗ ) + s 3t
Ut
Here
(4)
U& t
is the change in the unemployment rate which depends upon the output gap
Ut
Yt − Y (in line with the Okun’s
(1962)) and differences between domestic and
foreign prices (Phillips (1958)), π t − π t∗ and shocks in the labour market, s 3, t . We
expect unemployment to be lower when the current output, Yt , is higher than the trend
output, Y and vice-versa. Similarly, higher domestic prices relative to foreign prices
due to an expansion in aggregate demand implies lower unemployment rate. Labour
market shocks can either be positive or negative.
V. Results
This section discusses empirical findings on aggregate demand and aggregate supply
and unemployment- inflation tradeoffs using simple macroeconomic model as outlined
above. First we use unit root tests to assure stationarity of variables included in the
model. Then we present empirical results on aggregate demand and aggregate supply
using single equation estimation methods. Finally we study simultaneous relation
between unemployment and inflation using impulse response anlaysis.
18
a) Unit root tests
The first step in applying the cointgration techniques is to determine the order of
integration of each variable. The standard practice is to use the Augmented Dickey
Fuller (ADF) tests. For our analysis, we use quarterly data from the Office of National
Statistics. Before the estimating the cointegration paramenters, ADF test statistic was
calculated to indicate the order of integration in each univariate time series. Table 4.
gives the results of the ADF test for non stationarity in the variables. The usual
selection criteria based on the AIC or SBC determines the lag length used. The null
hypothesis is that the series are non stationary against the alternative that they are
stationary. An acceptance of the null hypothesis will imply that the corresponding
series are non stationary and have unit roots, while a rejection of the hypothesis will
imply that the series are stationary with a well-defined mean and variance. As the
results indicate, the null hypothesis of non stationarity cannot be rejected in all the
case for the level of deviation of the series from trend but can be rejected for the other
variables with intercept and trend suggesting stationarity.
Table 4: Unit Root Test
Intercept only
Level
Intercept with trend
Lag length
Level
Lag length
YY
-0.80
0
-0.90
0
DEF
-3.67
6
-3.80
6
RR
-3.12
4
-3.76
5
RIR
-2.89
0
-3.49
0
INF
-2.44
1
-3.96
1
URATE
-4.09
0
-4.19
0
An asterisk denotes ADF with trend. CV = -2.88 without trend and –3.45 with trend.
a) Aggregate Demand :
The results for the aggregate demand is
19
YYt = −0.01 − 0.06 DEFt − 0.09 RRt + 0.03RIR + 1.00YYt −1
t-ratio
-0.31
2.01**
-1.22
39.65***
R2 = 0.94; F (4,113) = 463.1*** ; DW-stat. = 1.81
b) Aggregate Supply:
The results for the aggregate supply is
RPI t = −0.05 + 1.00CINFt + 0.07YYt
t-ratio
82.43***
2.97***
R2 = 0.98; F (2,116) = 3398.0*** ; DW-stat. = 1.06
c) Unemployment Equation
The results for unemployment is
URATEt = −7.98 − 0.87YYt − 0.15DINFt
t-ratio
-11.4*** -2.57***
R2 = 0.53; F (2,116) = 65.24*** ; DW-stat. = 1.05
The results obtained using a simple model of aggregate supply/demand equation is
plausible. For the aggregate demand YYt-1 term is included to account for serial
correlation. The deviation of output is inversely related to the budget deficit (DEF)
and the deviation of real exchange rate from equilibrium (RR) and positively related
to the real interest rate. The aggregate supply equation shows that inflation is
positively related to core inflation and deviation of output from its equilibrium level.
In the unemployment equation, U.K. unemployment is negatively related to the
deviation of output from its equilibrium level and negatively related to the inflation
differential between the domestic and foreign inflation (DINF). High inflation will
cause a reduction in output as in the stagflation period of the 1980s. In the 1990s the
20
U.K. economy has been growing steadily with the lowest levels of unemployment
recorded.
Impulse response to shocks in supply and demand.
Above results show that output level is influenced by budget deficit and prices. We
also expect a negative relation between output and inflation at least in the short run.
However the hysterisis hypothesis in the labour market implies persistency of
unemployment rates over period. Such hysteresis effect also spills over to the
unemployment rate. We use a simple VAR model to trade out the impulse response
to both a supply shock and a demand shock in unemployment an inflation in this
model. The equations for these shocks can be represented as follows
OLS Estimation of a single equation in the Unrestricted VAR
Dependent variable is URATE
Regressor
Coefficient
T-Ratio [Prob]
RPI(-1)
0.37
2.19 [0.30]
RPI(-2)
-0.56
-2.09 [0.39]
RPI(-3)
0.33
2.07 [0.04]
URATE(-1)
0.42
4.39 [0.00]
URATE(-2)
0.19
1.92 [0.06]
URATE(-3)
0.18
1.92 [0.06]
CONSTANT
0.14
0.17 [0.86]
DEF
-0.49E-4
-0.89 [0.37]
DEF(-1)
-0.49E-4
-0.59 [0.56]
RIR
0.13
1.74 [0.08]
R2 = 0.66
F-stat.
DW-statistic = 2.05
F(9,106)=22.8 [0.0]
Diagnostic Tests:
LM Version
Serial Correlation
CHSQ (4) = 5.96 [0.202]
Functional Form
CHSQ (1) = 0.37 [5.43]
Normality
CHSQ (2) = 886.6 [0.00]
Heteroskedasticity
CHSQ (1) = 1.02 [0.313]
21
Supply Shock: uit =β11 Luit + β12 Lπ it + ε it
Demand Shock: π it = β 21 Luit + β 22 Lπ it + ε2t
Where π and u are inflation rate and unemployment rate respectively and L is the lag
operator. We can estimate these two equations and make short term forecasts for
changes in fiscal policy, deviation of real exchange rates and real interest rates for
policy simulation. Using Microfit 4.0 we otain the following VAR estimates.
OLS Estimation of a single equation in the Unrestricted VAR
Dependent variable is RIP
Regressor
Coefficient
T-Ratio [Prob]
RPI(-1)
1.39
13.82 [0.00]
RPI(-2)
-0.58
-3.62 [0.00]
RPI(-3)
0.06
0.64 [0.53]
URATE(-1)
-0.02
-0.28 [0.78]
URATE(-2)
-0.11
-1.79 [0.08]
URATE(-3)
0.06
1.02 [0.31]
CONSTANT
1.58
3.25 [0.00]
DEF
0.68E-5
0.21 [0.84]
DEF(-1)
0.12E-4
0.34 [0.73]
RIR
-0.08
-1.73 [0.09]
R2 = 0.96
F-stat.
DW-statistic= 1.96
F(9,106) = 252.3 [0.0]
Diagnostic Tests:
LM Version
Serial Correlation
CHSQ (4) = 19.81 [0.001]
Functional Form
CHSQ (1) = 4.74 [0.03]
Normality
CHSQ (2) = 76.69 [0.00]
Heteroskedasticity
CHSQ (1) = 19.56 [0.00]
These results show the business cycle effect on both prices (inflation) and
unemployment. There is evidence of the Phillips curve in the dataset. Past
22
unemployment is important in the determination of current inflation rate. When past
unemployment is high, unions bargain for higher wages, as there is a slack in the
labour market. Furthermore unemployment is persistent over a period and supports
the unemployment hysterisis. The result is consistent across the two models. It takes
time for inflation to adjust following government intervention to reduce the rate of
inflation. In the result, positive inflation coefficient is followed by a negative inflation
coefficient. Economic operators closely watch the inflation rate especially the Bank of
England Monetary Policy Committee. A fall in inflation is felt throughout the
financial market.
Impulse Response
These two impulse response diagrams show the short run fluctuation in the long run
steady state. It displays the Philips curve phenomenon, showing the trade off between
unemployment rate and inflation. A unit shock to the inflation rate in period one will
cause expectations to build up in the next period. As adjustment happens, inflation
and unemployment returns to their long run natural level after 20 quarters.
Generalised Impulse Responses to one SE shock in the equation for RPI
2.0
1.5
1.0
RPI
0.5
0.0
-0.5
0
5
10
15
20
25
30
35
40
45
5050
Horizon
Generalised Impulse Responses to one SE shock in the equation for
URATE
2.5
2.0
1.5
URATE
1.0
0.5
0.0
-0.5
0
5
10
15 20
25
30
35
40 45
5050
Horizon
23
VI. Conclusion:
We present a brief review of literature and some stylised facts about business
cycle in the UK economy. We discuss a simple model to study the cyclical fluctuation
of output, employment and prices. This model can take account of fiscal, exchange
rate and monetary policy. We show a structure where the forward and backward
looking expectations in prices influence aggregate demand and aggregate supply in
the short run. Evidence suggests persistency of unemployment and inflation in line
with the hysteresis hypothesis and trade off between unemployment and inflation
during our study period (1st quarter of 1975 to the last quarter of 1999). We find
shocks arising either in demand or supply side tends to prolong up to 10 quarters in
the future.
24
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