Download modelling core inflation in ukraine in 2003-2012

ISSN 1561-2422
MODELLING CORE INFLATION IN
UKRAINE IN 2003-2012
Natalia Novikova, Dmitry Volkov
Working paper No 12/12E
This project (No R105792) was supported
by the Economics Education and Research Consortium
and funded by GDN
All opinions expressed here are those of the authors
and not those of the EERC, GDN and Government of Sweden
Research dissemination by the EERC may include views on policy,
but the EERC itself takes no institutional policy positions
1
Abstract
High volatility of Ukrainian inflation can partially be explained by large share of food and
energy. The structure of Ukrainian CPI basket makes it difficult to distinguish between the
underlining and temporary changes in inflation, which complicates the reaction of monetary
policy. In this study we consider different measures of consumer price growth and examine
moving to core inflation as an intermediate target for NBU monetary policy. We employ VEC
framework to explore the difference between reaction of headline and core inflation to macro
fundamentals in 2003-2012. Two approaches - exclusion-based method and trimmed mean – are
used to construct alternative core indices. Although our estimation results do not allow making
unambiguous choice of the inflation target for monetary policy in Ukraine, they suggest that core
inflation should be monitored and used in communicating monetary policy decisions. We found
that both headline and core inflation have high degree of persistence, fit well within the mark-up
model, and that exchange rate pass-through is high in both cases. Meanwhile, core indices could
help distinguish shocks coming from regulated prices and nominal wages.
Key words: inflation modelling, Ukraine, mark-up, core inflation, monetary policy
JEL Code: E31, E41, E52
Authors
Natalia Novikova, Lecturer and Research fellow, National Research University Higher School
of Economics, tel: +74957729590 *26128, email: [email protected]
Dmitry Volkov, MSc, Imperial College London, UK, email: [email protected]
Acknowledgements
We would like to thank Gary Krueger, Elina Ribakova, Olga Ponomarenko, Robert Kirchner and the
participants of the EERC Research Workshops for providing valuable advice.
2
Contents
1. Introduction .................................................................................................................................4
2. The evolution of inflation in Ukraine in 2003-2012 ...................................................................6
3. Determinants of price growth and inflation models ....................................................................7
4. The choice of core inflation measures.......................................................................................11
5. Data............................................................................................................................................15
6. Estimation results ......................................................................................................................17
7. Discussion of the results and policy implications .....................................................................23
Appendix 1. Major trends in inflation in Ukraine in 2002 - 2011.................................................26
Appendix 2. Structure of CPI basket in Ukraine, %......................................................................30
Appendix 3. Previous studies on headline inflation in Ukraine ....................................................31
Appendix 4. Core inflation indices................................................................................................32
Appendix 5. Data...........................................................................................................................34
Appendix 6. Impulse response functions and variance decomposition.........................................36
References .....................................................................................................................................38
3
1. Introduction
Swings of Ukrainian consumer price inflation reached thirty percentage points over the
last decade. Annual inflation accelerated from zero in 2002 to 31% in the middle of 2008, and
fell to negative zone in the middle of 2012. High volatility is one of the features of Ukrainian
inflation, which can partially be explained by high share of food and energy - together they
account for about 60% of the CPI basket. The structure of Ukrainian CPI basket makes it
difficult to distinguish between the underlining and temporary changes in inflation, which
complicates the reaction of monetary policy.
High variability of price movements and large share of items subject to external shocks
can result in bias towards higher inflation. When developments in inflation are strongly
influenced by a small number of large changes in a few CPI components, the risk exists that the
aggregate measure of inflation deviates significantly from the underlining trend (Ball and
Mankiw (1994), Pujol and Giffiths (1998)). Given that monetary policy affects inflation with a
lag, reacting to changes of headline inflation could lead to larger output volatility.
Therefore, in order to provide adequate monetary response policymakers need to
distinguish between the acceleration of inflation due to, for example, indexation of stateregulated prices and second-round effects of such changes or changes in inflation due to factors
under the influence of the monetary policy. Core inflation indices excluding most volatile and
administratively controlled items of the consumer basket could be used as a measure of general
inflationary pressure in the economy. And therefore core measured are sometimes viewed as a
more appropriate indicator of inflation for the purpose of monetary policy. Core inflation
indicators can also be used to explain policy decisions to the public and minimize risks of sharp
changes in inflation expectations in response to temporary price shocks.
In 2008 the National bank of Ukraine (NBU) started publishing core inflation excluding
utilities and unprocessed food; however the headline inflation of about 5% in the medium term
4
remained an ultimate goal of the monetary policy as stated in the Memorandum of economic and
financial policy.
In this study we consider different measures of consumer price growth and examine if
moving to core inflation as an intermediate target is beneficial to monetary policy in Ukraine.
Two approaches - exclusion-based method and trimmed mean – are employed to construct
alternative core indices and analyse the underlining inflation in 2003-2012. We employ
VAR/VEC framework to explore the difference in reaction to macro fundamentals between
headline and core inflation and to test if using core indicators can improve monetary policy over
headline inflation.
Although our estimation results do not allow making unambiguous choice of the inflation
target for monetary policy in Ukraine, they suggest that core inflation should be monitored and
used in communicating monetary policy decisions. In line with earlier studies, we found that
headline inflation has high degree of persistence and fits well with the mark-up model. Core CPI
was found to have similar to headline drivers. However our estimates confirmed that shocks
coming from regulated changes in state-regulated relative prices (such as utilities) have little
effect on the core inflation and therefore core indices could help distinguish these shocks. Also
core inflation appeared to react faster to growth in nominal wages, and in this case could be used
as a forward-looking indicator.
The paper is structured as follows. We start with an overview of major trends in the
Ukrainian inflation over the last decade. Section 2 discusses the determinants of inflation in
emerging market economies and provides an overview of econometric approaches to modeling
and forecasting inflation. It also takes a closer look at particular features of the Ukrainian
inflationary process revealed by previous research. The choice of core inflation indicators is
described in Section 3. Sections 4 and 5 provide data description and summary of estimation
results. We conclude with policy implications in Section 6.
5
2. The evolution of inflation in Ukraine in 2003-2012
Ukrainian consumer price inflation fluctuated from zero to 30% since early 2000s
(Figures A1-A2), and in most of periods stayed above the level of peer countries. Consumer
prices decelerated sharply in 2001–2002 as a result of tight monetary policy and structural
reforms in the previous years (Aslund (2009)). But inflation returned to the upward trend in
2003-2008 driven by pick up of the economic activity, credit growth and unfavorable shocks to
energy and food prices (Table А1). In addition to that liberalization of the banking sector in
2004-2006 helped acceleration of the money supply to 60-70%YoY or three times faster than
growth in nominal spending. In 2007-2008 nominal GDP growth accelerated to around 30% with
average real growth of household consumption of about 12% pa. On this background Ukraine
faced major commodity price shocks in 2005-2010: the price of natural gas imported by Ukraine
more than doubled over the period 2005-2007 and then increased by another 30% in 2009-2011.
In addition to that, in the beginning of 2007 and in the end of 2010 food prices have risen along
with global food prices, the effect of which was magnified in Ukraine by a contraction of
agriculture production due to unfavorable climatic conditions.
While inflation was gradually accelerating in 2003-2008 the NBU continued to peg
UAH/USD exchange rate and was smoothing hryvnia appreciation during that period. From the
end of 2001 till mid-2008 the cumulative appreciation of the nominal effective exchange rate
(NEER) reached about 20%. Accumulation of FX reserves was the major contributor to base
money growth over that period (Figure A3).
Consumer price growth increased sharply during 2008 and peaked at just below 30% as a
result of growing inflation expectations, worries of hryvnia depreciation and fall in money
demand. The financial crises led to sharp drop in export volumes and capital inflow, which
resulted in slump of the economy and widening output gap. In 2009 GDP and private
consumption fell more than 15% in real terms. Bank lending activity stalled and broad money
growth turned negative. As a result despite sharp hryvnia depreciation inflation declined fast and
6
stood at around 12% in the end of 2009. At the same time as a part of bank recapitalization
program the holdings of government bonds by NBU started to grow rapidly and became the
major source of money base growth.
In 2008 the National Bank of Ukraine started to publish core inflation indicator, which
excludes raw food, energy and administratively regulated prices and covers about 55% of the
CPI (see Appendix 2 for detailed structure of the Ukrainian CPI). According to this measure
average core inflation in 2009 was above the growth of headline CPI (19.4%YoY and
15.9%YoY), probably suggesting the need for tighter monetary policy.
A few external shocks kept the CPI inflation close to 10% in 2010 – spike in the global
food prices, as well as hikes in gas tariffs and excise taxes. Disinflation continued once the effect
of these shocks was absorbed and the central bank started to tighten monetary policy. Headline
CPI inflation in Ukraine has decelerated to less than 5%YoY in 2011 and fell below zero in 1H
2012 as tight monetary policy was supported by lack of adjustment in administrative tariffs and
falling global food prices. Meanwhile core inflation appeared to be more resistant and remained
close to 8% in 2011 and 3-4% in the mid of 2012.
3. Determinants of price growth and inflation models
The scope of traditional approaches used for modelling inflation processes is dominated
by three major theories. The mark-up theory assumes that prices are set as the sum of the cost of
all production inputs and a fixed mark-up which covers fixed costs and profits. The set of items
to include into the mark-up is rather flexible with the intention to capture all the causality
channels influencing the price level — exchange rate, labour costs, import prices, energy costs.
According to the monetary theory, the change in monetary aggregate is the only factor
determining the rate of inflation. In addition to the direct effect of the increase in monetary
aggregate, monetary theory predicts that it is the excess supply of money that accelerates
inflation. To model the “excess” supply one needs to estimate money demand and money supply
simultaneously. The resulting “monetary overhang” is usually incorporated into the inflation
7
equation. Phillips curve models explain the inflation rate in the short run, or over the course of
the business cycle, as being determined by three main variables—economic activity in the
economy (e.g. output gap or unemployment), When there is a negative output gap (or a high
unemployment rate), there is downward pressure on prices (and wage demands are lower), then
the inflation-rate tends to fall.
While conventional determinants of inflation, such as the output gap, excess money
supply and labor costs were shown to have significant influence on consumer price growth in
emerging markets (Mohanty and Klau (1999)), a few features of inflationary process should be
considered in modelling inflation in such economies. First, inflation inertia is a key feature of
inflationary process. It could be a result of backward-looking inflation expectations, indexation
of wages or adjustment of relative prices (Rossi (2005)). Moriyama (2011) also looked at the
role of fiscal dominance and output volatility in inflation persistence.
Second, the exchange rate pass through is usually higher in emerging market economies
than in advanced economies. While some studies showed that inflation could be lower under
pegged exchange rate regimes (Ghosh et all (1997)), Mann (1986) argued that low exchange rate
volatility likely to result in higher exchange rate pass through, as importers would prefer to alter
prices rather than to adjust profit margins in case of rare changes in the exchange rate. On the
other hand, analysis by McCarty (1999) also implied that lack of competitiveness could be
associated with higher response of prices to changes in the exchange rate probably because in
less competitive environment importers face little pressure to adjust profit margins.
Third, changes in relative prices - particularly those arising from large supply shocks, or
adjustment of the administrated prices are an important factor of inflation in many emerging
market economies (Cotarelli, Griffiths and Maghadam (1998)). Relative price adjustments can
drive inflation irrespective of whether these are accompanied by money supply growth. At that
the size of the overall impact of the shock depends on the importance of the sector for the overall
consumer inflation (Fisher (1981)).
8
Forth, instability of money demand is also a common feature of economies in transition
as institutional reforms, development of the financial sector, emergence of new types of financial
assets affect money-price relationship (Jonas and Mishkin (2003)). In this case inflation
forecasting based on money growth and monetary policy relying solely on targeting money
supply become ineffective.
Vector autoregressive models and factor models are two competitive econometric
approaches that are most often used to analyse the effect of the abovementioned factors and to
build inflation forecasts. Under VAR/VEC framework inflation and major macroeconomic
factors are treated as endogenous variables being functions of the lagged values of all
endogenous variables and exogenous variables in some cases. In VEC it is also expected that
non-stationary expected to converge to some co-integrating relationship in the long run. The
deviation from the long-run equilibrium is corrected gradually through a series of partial shortrun adjustments. The forecast is derived as a one-step ahead forecast. Factor models are based on
the assumption that the behaviour of most macroeconomic variables can be well described by
several unobservable factors, which are often interpreted as the driving factors in the economy
(Stock & Watson (1998)). An approximate dynamic factor model serves as the statistical
framework for the estimation of the indexes and construction of the forecasts. In factor models
the forecast at moment t+h determined directly on the basis of information available in period t.
While the use of wide range of indicators could help to improve the accuracy of forecasts
derived from factor models as compared to VAR (Stock & Watson (2002), Kotlowski (2008),
Cristadoro et all (2008)), VAR/VEC models provide good framework for analyses of
interrelations between different variables as well as to look at different causality channels.
Previous studies on Ukraine considered headline inflation in 1993-2008 under VAR/VEC
framework (Lissovolik (2003), Leheyda (2005), and Kirchner, Weber and Giucci (2008),
Siliverstov and Bilan (2005)) or as a part of cross country studies (Korhonen and Wachtel
(2005)), Agayev (2009) and Cotarelli, Griffiths and Maghadam (1998)). They showed that
Ukrainian inflation had large degree of persistence and was well described by the mark-up
9
model. Rossi (2005) also showed that Ukraine, together with Russia, Poland and Romania, had
the highest inflation persistence among the CEE countries in 2002-2005. Nominal wages and
exchange rate were found to have strong effect on inflation (summary of pass-through
coefficients are provided in Appendix 3).
However the difference between earlier studies lies in the choice of specific indicators to
estimate the effect of macroeconomic factors on inflation. Lissovolik(2003) in the mark-up for
Ukraine included the wages, exchange rate and paid services as a proxy for administered prices.
Kirchner, Weber and Giucci (2008), Siliverstov and Bilan (2005) also used nominal wages but
included commodity price index as a measure of energy costs. Meanwhile introduction of unit
labour costs instead of nominal wages in the mark-up equation is also common (De Brouwer and
Ericsson (1998) in the case of Australia, Oomes and Ohnsorge (2005) in the case of Russia) and
allows taking into account change in productivity. Exchange rate pass-through coefficients,
while being large compared to other economies, differ significantly depending on the period of
analysis and the measure of the exchange rate – hryvnia’s exchange rates vis-à-vis US dollar and
Euro, and nominal effective exchange rate, expected rate of devaluation.
Studies on of the link between inflation and the growth of monetary aggregates came to
varying conclusions depending on the period under consideration. Ukrainian inflation did not
confirm the presence except. Lissovolik (2003) stated that the relationship existed during
hyperinflation in 1993-1996, but no long-term relationship between money and inflation could
be identified in 1996-2002. However, he noted that the linkage similar to the one observed in
1993-1996 could re-established later. No evidence was found that the excess money supply
affects inflation was found by Leheyda (2005). At the same time, Kirchner, Weber, Giucci
(2008), who analyzed headline inflation in 1999-2008, found that both M1 and M3 have
significant impact on inflation with a lag of about 4-6 month.
Output gap model in the case of Ukraine was tested by Kirchner, Weber and Giucci
(2008), who found cointegration relationship between inflation and unemployment and revealed
strong negative effect of unemployment on inflation.
10
In this study we would like to explore the influence of macroeconomic factors on both
headline and core inflation. Similar to previous researches we employ VAR/VEC framework
which allows taking into account different causality channels and interrelations between
macroeconomic variables. We focus on the mark-up model and assume that in the long-run the
level of inflation is defined by the equation below:
Inflation = f(growth of labor costs, depreciation of the exchange rate, inflation of utilities/energy
prices, mark-up),
(1)
where depreciation of the exchange rate is used as a proxy for inflation of imported goods and
the growth of utilities index is a proxy for internal state regulated energy and utilities costs. In
the short run, consumer price growth could also be growth affected by the exogenous shocks, for
example monetary shocks (excess money supply) or shocks coming from commodity prices (like
food and energy) that are determined on the global markets and have little influence from the
local inflation.
We would like to test whether there is significant difference in the reaction of headline
and core inflation measured to various shocks. Lower dependence on regulated prices or
exogenous shocks, as well as stronger/shorter links from monetary aggregates would support the
idea of using core inflation as an intermediate target for monetary policy.
The choice of core inflation indicator is discussed below. Description of alternative
indicator tested as macroeconomic factors is provided in Section 5.
4. The choice of core inflation measures
Core inflation indices measuring general inflationary pressure in the economy are
sometimes viewed as a more appropriate indicator of inflation for the purpose of monetary
policy. Widely used definitions of core inflation consists of measures that exclude certain
components on a period-by-period basis (exclusion based core inflation, trimmed mean methods)
11
and measures that downplay the more volatile components (variance-weighted core inflaiton)1.
The choice of core inflation indicator is determined by a number of desired properties
(Roger (1997), Wynne (1999)). Core inflation indicators should be (i) significantly less volatile
than headline inflation; (ii) available on a timely basis; (iii) unbiased, in the sense that the
difference between the historical average of headline and core measure should be small; and (iv)
easily reproducible; (v) understandable by the public; (vii) not be subject to major revisions;
(viii) be forward-looking, in the sense of helping to project inflation developments.
To construct core inflation indices for the purpose of this study we employ two
approaches - exclusion-based method and trimmed mean. These approaches were found to
perform well on a wide range of criteria in the Ukrainian case (Moulin (2008) and Jakubiak et all
(2005)). Since 2008 the National Bank of Ukraine officially publishes core inflation indicator,
which excludes raw food, energy and administratively regulated prices and covers about 56% of
the CPI. At the same time, studies for the US, Australia and Ukraine showed that trimmed mean
measured core inflation indicators tend to be more consistent in terms of Granger causality and
average inflation forecast.
Our exclusion-based core inflation index (CORE) covers approximately 59% of the CPI
basket. We applied approach similar to the one used by the NBU, and excluded items with
administratively regulated prices (utilities, energy and petrol etc.) and unprocessed food
(Appendix 4).
Two measures of trimmed mean inflation were constructed using standard deviation and
fixed weights approaches. The CPI index was split into 48 items with average weight of about
2%. According to standard deviation approach we excluded those items, which had monthly
growth rates certain number of standard deviations above or below from the average across items
in each month. Under fixed weight approach a certain percentage of weights from each tail of the
distribution of monthly growth rates were dropped. In both cases the trimming parameters were
allowed to be asymmetric.
1
For overview of approaches to measure core inflation see Silver (2006), and Bermingham (2006)
12
A set of core inflation indices were ranked using following criteria (Bryan and Checchetti
(1994); Hogan et all (2001)):
•
deviation from the trend measured with root mean squared error (RMSE) and
mean absolute deviation (MAD);
•
stability criteria, suggesting lower volatility for core inflation as compared to
headline and measured by standard deviation.
•
and unbiasness with respect to the CPI, which requires that core inflation should
be an unbiased predictor of headline inflation over 9-18 months2.
We chose two trimmed mean measures of core inflation. One excluding items with
monthly growth rates above (or below) 0.3 (0.4) standard deviations for each month (TRIMSD).
And the second, based on fixed weights approach excluding 11% from the left tail and 14% from
the right tail (TRIMFW).
Figures A4-A5 (Appendix 1) and Table 3 below provide comparison of different inflation
measures. Except for TRIMFW, core inflation measures appeared to provide worse estimate of
the average inflation in 12-18 months than the headline. But our trimmed core inflation indices
appear to provide a better indication of underlining inflation than the official NBU core measure.
None of the year-over-year core inflation indicators (including the one used by the NBU)
passed the unbiasedness test described above for headline inflation forecasts ranging from 6 to
18 month. However, the hypothesis of unbiasedness was confirmed for month-over-month
inflation rates.
2
This could be tested by the following regression (Hogan et all (2001)):
CPI t + h − CPI t = α + β (COREt − CPI t ) + ξt + h , where CPI t + h is the h-months ahead headline inflation,
CPI t is current rate of headline inflation, and CORE t denotes current rate of core inflation. If α = 0 and
β = 1 the current core inflation is an unbiased predictor of future headline inflation.
13
TABLE 3. Summary statistics for inflation measures (2004-2011)
Inflation
index, YoY
Headline
Weight,
% of
headline
CPI*
Mean
Max
Min
Standard
deviation
Correlation
with
headline
inflation
Deviation from n-month centered
moving average of headline inflation
MAD
RMSE
N=8
12
18
8
12
18
100%
12.0
31.1
0.9
6.1
1.0
1.0
1.4
1.7
1.4
1.8
2.3
59%
10.9
27.3
0.3
6.1
0.93
2.2
2.4
2.7
2.8
3.0
3.4
Trimmed
mean
(standard
deviation)
57%
11.4
27.6
1.1
5.6
0.95
1.6
1.8
2.2
2.5
Trimmed
mean (fixed
weights)
75%
12.1
28.0
1.5
5.7
0.94
0.9
2.2
1.8
2.7
NBU core**
56%
12.2
23.6
7.0
6.0
0.91
2.2
2.3
2.6
2.6
Exclusionbased
(this
study)
2.1
2.6
Source: authors’ calculations. *) Average across periods for trimmed mean measures. **) For 2008-2011
Granger tests of causality between core inflation measures and headline CPI also gave
mixed results. We found that there is either bi-directional Granger causality between our core
inflation measures and headline or causality from core to headline (as expected) depending on
the time span and inclusion of producer price index (PPI).
TABLE 4. Granger test summary (2 variable VAR, 12 lags, 2004M01—2012M03)
from/to
Exclusion based core
inflation (this study)
Trimmed mean
(fixed weights)
Trimmed mean (standard
deviation)
to
YES
NO
YES
from
NO
NO
YES
Headline CPI
Lack of stability in causality between core and headline measures is consistent with
historical patterns. This could indicate high speed of transmission of inflation expectations into
core inflation or suggest there are second round effects to core from headline to exclusion-based
14
core inflation. Possible reasons: (1) too wide definition of core inflation; (2) high speed of
transmission of inflation expectations into core inflation as a result of accommodative monetary
policy, lack of confidence or flexible prices. Similar phenomenon (wrong/unstable Granger
causality between core inflation measures based on exclusion method and headline inflation) was
found in other countries as well (for example in the US, Turkey, Australia). In case of the US,
Mehra and Reilly (2009) showed that the speed and direction of adjustment between core and
headline inflation appear to depend on the monetary regime. They found that the gap between
core and headline measures closed mainly through adjustment of core, when monetary policy
was accommodating shocks to inflation. Unfortunately, due to the lack of long data series for
Ukraine it is impossible to look at periods with different monetary regimes to check if Mehra’s
hypothesis is true for Ukraine as well.
5. Data
We use data series from Jan 2003 to March 2012 which allows us to get about 90-100
observations in terms of year-on-year growth rates. The list of variables and acronyms used in
the estimation results tables, as well as summary of Augmented Dickey-Fuller test statistics are
provided in Appendix 4.
Inflation rates are calculated as year-on-year growth rates of the headline CPI index
(CPI), Producer Price Index (PPI) and the core inflation indices (CORE, TRIMSD and
TRIMFW) defined above. All YoY inflation series were found to be nonstationary and
integrated of order 1. This result does not look unusual and is consistent with previous studies of
inflation in Ukraine as well as other emerging markets (Mohanty and Klau (1998), Onsorger and
Oomes (2005).) Month-over-months growth rates were found to be I(0) process, which again is
consistent with other studies. However we stick to usage of YoY growth rates, which are
consistent with the definition of the monetary policy target set by the National Bank of Ukraine,
more appropriate indicator for long-term equations, and also allow avoiding introduction of
additional variables to capture seasonality.
15
Two indicators of labor costs are considered: (1) YoY growth of nominal wages
(WAGE) and (2) YoY growth of unit labor cost index (ULC, which is as a linear combination of
year-on-year growth rates of the nominal wage (WAGE), industrial production index (IP), and
the number of employed. Owing to data limitations number of employed is calculated using
monthly data on unemployment rate and average annual number of employed.
The growth of exchange rate is commonly used if there is no reliable data on inflation of
imported goods. Two indicators are tested in this study hryvnia exchange rate vis-à-vis the US
dollar (USD = hryvnias for 1 USD) and the nominal effective exchange rate (NEER). The use of
USD/UAH exchange rate could be justified by significant dollarization of the Ukrainian
economy and the fact that NBU uses it as a nominal anchor. At the same time, growing trade
Europe and work migration between Europe/Russia and Ukraine suggest that Euro and Ruble
should also be taken into account. Therefore NEER could appear to be relevant indicator
(Korhonen and Wachtel (2005), Kirchner, Weber and Giucci (2008)).
As a proxy for state regulated prices and energy costs we chose utilities index, being
about 8% of the CPI basket. Utilities index includes such items like housing costs, water supply,
gas, electricity and fuels other than petrol. Most of these prices are set/controlled at the state or
local levels. Their indexation is often driven by political events and was not always linked to
general inflation level.
Output gap (GAP) is approximated using Hodrick-Prescott filter from monthly series of
industrial production (seasonally adjusted using X12 procedure) as a percentage deviation from
the trend (FIGURE 1). To deal with the end-sample bias which is an important drawback of the
HP filtering (Canova (1998)) we exclude the last three observations from the sample if model
specification uses GAP. Lack of long enough data series in emerging market economies
sometime is overcome by taking into account the level of factor utilization (St-Amant and van
Norden (1997); Oomes and Dynnikova (2006)). Here we ignore this adjustment given that HPfiltered series appeared to provide at least reasonable estimate. According to this measure
economy looked significantly overheated in the beginning of 2008 and probably had negative
16
output gap in the beginning of 2005 and 2009 (Figure A6).
Two monetary aggregates are tested – M1, which is supposed to be better controlled by
the central bank, and M3 as a measure of broad money. These aggregates are used to estimate
excess money supply (OVERHANG) as a residual from the long run money demand equation:
 = Intercept + α * LOG ( IP) − β * DEPOSIT
LOG Monetary _ aggregate
CPI 

(2),
where IP is industrial production index used as a proxy for the level of economic activity and
DEPOSITS is the interest rate on deposits. Tests confirmed the presence of the cointegrating
equation. According to our estimates the deposit rate was found to have only marginal effect on
money demand, growth in industrial output leads to 1.3-3% increase in real broad money (M3)
and 1.2-2% increase in M1 depending on the period and model specification.
We also consider two indicators of external prices – index of the world food prices
(FOOD) and index of petroleum prices (OIL) calculated by the World bank
6. Estimation results
Three sets of results are considered to explore the difference between reaction of headline
and core inflation to macro fundamentals and to test the hypothesis that using core inflation can
improve monetary policy over headline inflation. First, we examine Granger causality between
different inflation measures and other macroeconomic variables. Second, we compare variance
decomposition and impulse response functions of headline and core inflation from labor costs
and the exchange rate. And finally, we provide a range of quantitative estimates of the passthrough coefficients.
We start with examining Granger causality between different inflation measures and
other macroeconomic factors (Table 5). Our estimates suggest that the exchange rate (NEER or
USD) and labor costs (nominal wage or ULC) could be Granger causes of both headline inflation
and core inflation. We could not reject hypothesis that inflation (independent of the measure)
also could be a cause of the exchange rate. This is consistent with the theory that inflation
17
expectations could be a driver of the exchange rate. On the contrary, we found that wages have
one-way effect on inflation. This could be explained by the fact that increases of minimal wage
in Ukraine are set in the beginning of the fiscal year and take into account official inflation
forecast, not the actual inflation rate.
Utilities prices appear to affect only headline inflation and trimmed mean core inflation
with fixed weights. That is what we aimed to achieve by constructing core indices. The link from
inflation to growth in utilities prices was rejected, supporting the fact that utilities prices are
subject to regulation.
TABLE 5. Granger test summary (3-variable VAR, 12 lags, 2004M01—2012M03, P-values in
[])
to/from
from
Headline CPI
Exclusion based core
inflation (this study)
Trimmed mean
(fixed weights)
Trimmed mean
(standard
deviation)
YES
YES
YES
YES
[0.0064]
[0.0351]
[0.0036]
[0.0262]
NO
NO
NO
NO
[0.941]
[0.4139]
[0.7369]
[0.8633]
YES
YES
YES
YES
[0.0000]
[0.0447]
[0.0150]
[0.0004]
YES
YES
YES
YES
[0.0007]
[0.0047]
[0.0003]
[0.0000]
YES
NO
YES
NO
[0.0072]
[0.1878]
[0.0000]
[0.0784]
NO
NO
NO
NO
[0.1049]
[0.5219]
[0.6531]
[0.5978]
Wage
to
from
Exchange
rate
to
from
Utilities
to
Granger tests for causality between our measure of output gap and inflation indicators
gave mixed results. Most of the tests supported the hypothesis that output gap could be a Granger
cause for consumer price inflation in a multivariable VAR if nominal wage was included, or
showed bi-directional relationship in a two-variable VAR. The presence of link from gap to
18
inflation was rejected if unit labor costs were included instead of the nominal wage. This looks
reasonable given that unit labor costs take into account productivity.
Hypothesis that money supply could be a Granger cause of consumer price growth was
rejected for all inflation indicators.
To compare the reaction of headline and core inflation to labor costs and the exchange
rate 3-variable VAR models were estimated for changes in nominal wage growth (DWAGE),
exchange rate growth (DUSD) and inflation rates (Table 6 and Appendix 5). Analysis of impulse
response functions (Appendix 5) showed little difference between headline and core inflation.
The initial effect of currency depreciation comes within 2-3 months leading to acceleration of
inflation. We also identified second-round effects of changes in the exchange rate within 5-7
months for headline and fixed-weights trimmed mean and exclusion-based core inflation.
Acceleration of nominal wage growth leads to acceleration of inflation, and this effect peaks
after about 3-4 months. Meanwhile, headline CPI seems to react to with a lag of about 2 months
as compared to exclusion-based core inflation fixed-weights trimmed mean, which accelerate
right after the increase in wage growth.
According to variance decomposition analyses, both headline and core inflation have
very high degree of persistence (Appendix 5). Error variance is mostly explained by its own
shock: 65-80% depending on the model. Except for fixed weights trimmed mean about 20% of
variance is explained by the exchange rate shock, while the power of wage shock appeared to be
weaker, especially in the case of standard deviation trimmed mean.
19
TABLE 6. Impulse response functions (3-variable VAR, 12 lags, 2004M01—2012M03)
From WAGE to inflation
From USD to inflation
Response of D(CPIY) to Cholesky
One S.D. D(USDY) Innovation
Response of D(CPIY) to Cholesky
One S.D. D(WAGEY) Innovation
.006
.006
.004
.004
.002
.002
.000
.000
-.002
-.002
-.004
-.004
1
2
3
4
5
6
7
8
9
10
1
2
Response of D(COREY) to Cholesky
One S.D. D(WAGEY) Innovation
3
4
5
6
7
8
9
10
9
10
9
10
9
10
Response of D(COREY) to Cholesky
One S.D. D(USDY) Innovation
.006
.006
.005
.004
.004
.003
.002
.002
.000
.001
.000
-.002
-.001
-.004
-.002
1
2
3
4
5
6
7
8
9
10
1
2
Response of D(TRIMFWY) to Cholesky
One S.D. D(WAGEY) Innovation
3
4
5
6
7
8
Response of D(TRIMFWY) to Cholesky
One S.D. D(USDY) Innovation
.005
.004
.004
.003
.003
.002
.002
.001
.001
.000
.000
-.001
-.001
-.002
-.002
-.003
-.003
1
2
3
4
5
6
7
8
9
10
1
2
Response of D(TRIMSDY) to Cholesky
One S.D. D(WAGEY) Innovation
3
4
5
6
7
8
Response of D(TRIMSDY) to Cholesky
One S.D. D(USDY) Innovation
.004
.006
.005
.003
.004
.002
.003
.001
.002
.000
.001
.000
-.001
-.001
-.002
-.002
-.003
-.003
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
20
To estimate pass-through coefficients for each of the indicators of inflation we built VEC
models, with individual equation taking the form below (CPI inflation equation is taken as an
example):
∆CPI = Bec * (CPI t −1 − b1 * Labort −1 − b2 * USDt −1 −b3 * Utilitiest −1 − c) +
+ ∑ B1i ∆CPI t −i + ∑ B2i ∆Labort −i + ∑ B3i ∆USDt −i + ∑ B 4i ∆Utilities t −i + ∑ B mj ∆EXO t − j (2),
where CPI, Labor, Utilities and USD (exchange rate) are treated as endogenous, while EXO
denotes exogenous factors effecting inflation only in the short-run, and four equations of the
system (i.e. ∆CPI , ∆Labor , ∆USD and ∆Utilities ) are estimated simultaneously. Vector of
exogenous variables includes growth world commodity prices (FOOD, OIL) and monetary
aggregates (M1, M3, and OVERHANG).
We found that the long term level of headline CPI is driven by unit labor costs, the
exchange rate and utility prices. The exchange rat pass-through appeared to be strong at about
0.35-0.4, suggesting that 10% devaluation of hryvnia would lead to increase in inflation rate by
3.5-4% in YoY terms (Table 7). This result appeared to be robust independent of the model
specification and looks close to estimates in the earlier studies. Long-term pass-through
coefficient for labor costs indicate that 10% increase in ULC leads to about 1.5-2pp increase in
YoY inflation. The effect of UT is spread over time with long -run pass-through coefficient of
about 0.15.
Exogenous global food and energy prices appeared to be insignificant, as well as our
measure of excess money. At the same time, M3 aggregate YoY growth was found to be
significant, when added as exogenous variable with 6-7 months lag.
TABLE 7. Long run pass-through effects for headline inflation
Variable
CPI: Long run pass-through coeffitients
Basic mark-up model
Mark-up model with utilities
prices
Mark-up model with exogenous
money growth (M3)
ULC
0.45
0.14 (0.19)
0.18
USD (NEER)
0.40
0.36 (0.35)
0.37
---
0.15 (0.16)
0.14
UT
21
Similar models were applied to exclusion based and trimmed mean measures of core
inflation. For exclusion-based core inflation (CORE) utilities and food prices were found to be
insignificant in the long-run as well as in the short run. The long- run exchange rate pass-through
was estimated at about 0.37-0.45. The increase in nominal wages translates into 0.44pp increases
in core inflation rate. In contrast to headline inflation we could not confirm presence of
cointegration between CORE and unit labor costs, however we found cointegrating equation
between core inflation, nominal wage, exchange rate and our proxy of output gap. So the longrun equation was also modified to:
CORE = Intercept+a*GAP+b*USD+c*WAGE.
Introduction of output gap into the mark-up model can be justified as follows. Producers
demand a higher markup over costs when the economy is overheated (since they are working
close to full capacity) and can sacrifice part of it in order to stay in business during crisis.
Meanwhile the economic theory does not provide clear prediction of mark-up behaviour over the
cycle. There are empirical evidence of both counter-cyclicality (USA, South Africa) and procyclicality (UK, Canada) of mark-up behaviour. See Rotenberg and Woodford (1999) and Klien
(2011) for a discussion and literature overview.
Similar to the case of headline inflation, OVERHANG was found insignificant, but
growth in monetary aggregates M1Y and M3Y appeared to affect the inflation dynamics with a
lag of 3-6 months.
TABLE 8. Long run pass-through effects for core inflation (exclusion-based method)
CORE: Long run pass-through coeffitients
Variable
Basic mark-up model
Mark-up model with GAP
Mark-up model with exogenous
money growth (M1)
WAGE
0.42
0.45
0.44
USD
0.45
0.39
0.46
GAP
---
0.68
---
For core inflation measures based on trimmed mean approach mark-up model showed
good fit as well. We also found that nominal wages work better than unit labor costs. At the
22
same time exchange rate pas-through (USD) appeared to be even stronger – close to 0.5. Utility
and food prices were found insignificant. Money supply growth measures were significant with
4-6 months lag.
TABLE 9. Long run pass-through effects for core inflation (trimmed mean)
Variable
TRIM (fixed weights)
Mark-up model
Mark-up model with
with GAP
exo money (M1)
TRIM (standard deviation)
Basic mark-up
Mark-up model with
model
exo money (M3)
WAGE
0.35
0.33
0.17
0.27
USD
0.51
0.48
0.50
0.57
GAP
1.35
1.21
---
---
7. Discussion of the results and policy implications
In this study we compare headline CPI with thee measures of core inflation and test if
using core indicators can improve monetary policy over headline inflation.
Although our estimation results do not allow making unambiguous choice of the inflation
target for monetary policy in Ukraine, they suggest that core inflation should be monitored and
used in communicating monetary policy decisions. On the one hand, we found that headline and
core inflation have common drivers and have similar reaction to changes in nominal wages and
the exchange rate. We also could not prove that using core inflation would improve medium
term forecast of inflation. On the other hand, our estimates confirmed that shocks coming from
regulated changes in relative prices (such as utilities) have little effect on core inflation and
therefore core inflation could help to distinguish these shocks. Also core inflation appeared to
react faster to growth in nominal wages, and in this case could be used as a forward-looking
indicator.
From this perspective, exclusion-based core inflation is preferable indicator. It provides
more stable results across the tests as compared to trimmed mean measures, and is easily
understandable by the public.
At the same time, according to our analysis Ukrainian inflation process shares major
features common to other emerging market economies and these properties have some policy
23
implications. First of all, both headline and core inflation have high degree of persistence, which
implies significant role of inflationary expectations and high costs of disinflation. Therefore clear
communication related to monetary policy, as well as transparent and predictable government
policy on administrative price changes are needed to avoid undermining the NBU’s credibility if
the inflation target is missed due to administrative price adjustments. Gradual and but reliable
adjustment of state regulated prices and move to market-based indexation of administrated prices
could mitigate the effect of changes in relative prices on inflation inertia. Even if it is not used as
a monetary policy target, core inflation remains important part of policy formulation,
communication and management of inflation expectations.
We also found that the exchange rate is the key explanatory variable. Depending on the
model the exchange rate pass-through was estimated at about 0.35-0.5 (10% devaluation of
hryvnia leads to about 3.5-5pp increase in yoy inflation rate) in the long run. The coefficient
looks high even compared to other emerging market economies. As we discussed in section 3,
this could be a consequence of tightly managed exchange rate regime and/or high share of
imports in the CPI basket. Given relatively high exchange rate pass-through, the exchange rate
seems to be the most efficient tool of monetary policy in the short run. However, gradual move
to more flexible exchange rate is required in order to reduce the effect of the external shocks on
inflation in the future and to improve efficiency of other tools of monetary policy such as interest
rates.
The cost of labor appeared to have strong and relatively fast effect on both headline and
core inflation. Therefore the central bank should avoid accomodative monetary policy, when
there is exogenous shock to wages (e.g. increase in minimal wages before the elections in the
excess of the average inflation level). As noted by Moriyama (2011) pro-cyclical monetary
policy and lack of timely response to demand shocks could propagate larger fluctuations of
output gap and higher inflation persistency. Here core inflation could be used to justify
tightening of monetary policy before growing wages lead to acceleration of headline CPI.
24
Meanwhile, the relationships between money growth and different inflation measures
require further research. The hypothesis that money growth could be a Granger cause of
inflation, as well as the presence of cointegration between growth of money aggregates and
inflation were rejected for all inflation measures. We found cointegrating relationship between
money aggregates, headline inflation and industrial production, which could be interpreted as
long-term money demand. However excess money appeared to have no effect on the CPI growth
in the short run. On the other hand, we found that short run effect of changes in money supply
have stronger and faster effect on core inflation (3-4 months) than on headline inflation (6-7
months) if money growth is treated as exogenous variable. These results could indicate
instability of the money demand function and suggest that monetary aggregates should not be
used as a policy target in Ukraine. Also, money velocity should be taken into account in order to
identify inflation indicator that have closer links to monetary policy tools.
25
Appendix 1. Major trends in inflation in Ukraine in 2002 - 2011
TABLE A1. Ukraine key macroeconomic indicators in 2002-2011
Real GDP,
% yoy
Real consumption
growth,
% yoy
Nominal
wages,
% yoy
Broad
money
growth,
%YoY
Credit extension
to private sector,
% change
Policy interest
rate,
%, eop
Average
UAH/US$,
%YoY
9.6
5.2
NA
20.7
42.4
49.8
7.00
-0.8
18.4
9.1
9.5
9.4
23.0
46.5
64.9
7.00
0.1
9.0
29.1
8.6
12.1
8.7
27.7
31.8
32.2
9.00
-0.3
2005
13.5
27.9
7.2
3.0
14.0
36.5
54.1
63.6
9.50
-3.7
2006
9.1
23.3
6.9
7.4
12.2
29.4
34.3
69.8
8.50
-1.5
2007
12.8
32.5
6.4
7.6
13.1
29.7
51.7
73.7
8.00
0.0
2008
25.2
31.5
6.4
2.3
10.1
33.7
30.4
67.1
12.00
4.2
2009
15.9
-3.7
8.8
-14.7
-12.3
5.5
-5.3
2.8
10.25
48.0
2010
9.4
19.8
8.1
4.2
7.0
17.7
23.0
0.7
7.75
1.9
2011
8.0
21.6
7.9
5.2
15
17.5
16.4
8.9
7.75
0.4
CPI,
% avg
Nominal
GDP,
%YoY
Unemployment,
% of labour
force
2002
0.0
10.6
2003
5.2
2004
26
FIGURE A1. Ukraine CPI inflation vs peers
Average CPI, % YoY
30
25
20
15
10
5
Ukraine
Kazakhstan
Russia
Czech Republic
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
0
Poland
Sources: Haver, State statistical committee of Ukraine
FIGURE A2. Highly volatile items account for more than 50% of the basket.
40
YoY, %
35
30
25
20
15
10
5
0
Headline CPI
Food
Utility Services
Jan-12
Oct-11
Jul-11
Apr-11
Jan-11
Oct-10
Jul-10
Apr-10
Jan-10
Oct-09
Jul-09
Apr-09
Jan-09
-5
T ransport
Sources: Haver, State statistical committee of Ukraine
27
FIGURE A3. Sources of base money growth
100%
80%
60%
40%
20%
0%
-20%
-40%
Aug-03
Nov-03
Feb-04
May-04
Aug-04
Nov-04
Feb-05
May-05
Aug-05
Nov-05
Feb-06
May-06
Aug-06
Nov-06
Feb-07
May-07
Aug-07
Nov-07
Feb-08
May-08
Aug-08
Nov-08
Feb-09
May-09
Aug-09
Nov-09
Feb-10
May-10
Aug-10
Nov-10
Feb-11
May-11
Aug-11
-60%
Contribution to Money base growth, %YoY
NFA
Net claims on gov
Claims on other residents
OIN
Monetary Base
FIGURES A4-A5. Core and headline inflation in Ukraine in 2004-2011
35%
30%
25%
20%
15%
10%
5%
Headline CPI
Exclusion-based core inflation (CORE)
Jul-11
Jan-11
Jul-10
Jan-10
Jul-09
Jan-09
Jul-08
Jan-08
Jul-07
Jan-07
Jul-06
Jan-06
Jul-05
Jan-05
Jul-04
Jan-04
Jul-03
Jan-03
0%
NBU core inflation
28
35%
30%
25%
20%
15%
10%
5%
Jul-11
Jan-11
Jul-10
Jan-10
Jul-09
Jan-09
Jul-08
Jan-08
Jul-07
Jan-07
Jul-06
Jan-06
Jul-05
Jan-05
Jul-04
Jan-04
Jul-03
Jan-03
0%
Trimmed mean fixed weights (TRIMFW)
Trimmed mean standard deviation (TRIMSD)
Headline inflation (CPI)
Sources: Haver, Authors’ estimates
FIGURE A6. Output gap approximation base on HP-filtered seasonally adjusted IP
GAP
20%
15%
10%
5%
0%
-5%
-10%
-15%
-20%
2011M07
2011M01
2010M07
2010M01
2009M07
2009M01
2008M07
2008M01
2007M07
2007M01
2006M07
2006M01
2005M07
2005M01
2004M07
2004M01
2003M07
2003M01
-25%
Source: Authors’ estimates.
29
Appendix 2. Structure of CPI basket in Ukraine, %
Source: National bank of Ukraine
30
Appendix 3. Previous studies on headline inflation in Ukraine
TABLE A1. Estimated coefficients in the long-run inflation model (headline CPI)
Study / period
Leheyda
(2005)
Lissovolik
(2003)
1997-2003
1993-2002
Kirchner, Weber,
Giucci (2008)
1999-2008
0.33-0.34
(1993-2002)
0.45
(1996-2002)
0.25-0.45
(1993-2002)
Wage growth
0.28
0.32
(1996-2002)
*Coefficients in the restricted model, where the sum of the coefficients in the eq. (3) is set to equal 1.
Administered prices
(paid services)
0.34
TABLE A2. Estimates of the exchange rate pass-through effect on inflation (headline CPI).
Study / period
Leheyda
(2005)
Lissovolik
(2003)
1997-2003
1996-2002
0.11-0.17
(1993-2002)
0.08
(1996-2002)
US dollar
Euro
Korhonen and
Wachtel
(2005)
1999-2004
Kirchner,
Weber, Giucci
(2008)
1999-2008
0.63-0.64
0.24-0.28
0.3-0.5
NEER(IMF)
0.41
*Coefficients in the restricted model, where the sum of the coefficients in the eq. (3) is set to equal 1.
TABLE A3. Comparison of estimated coefficients in the long-run money demand equation.
Study / period
Kirchner, Weber,
Giucci (2008)
1999-2008
M3
M1
Lissovolik
(2003)
1996-2002
M3
Leheyda
(2005)
1997-2003
M2
0.94*
Output level
1.71*
1.17*
1.3-1.6
Deposit rate (lending rate for KWG)
-0.012*
-0.017*
-0.02—0.03
*Mean the coefficient was found to be significant.
31
Appendix 4. Exclusion-based core inflation index
Exclusion-based methods are built on the idea of removing certain categories of goods or
services from the index. While NBU’s components are not explicitly described, we apply similar
approach to build core inflation index for the period from 2002-2011 using publicly available
data on the weights and dynamic of particular items in the CPI basket. Our core inflation index
covers approximately 59% of the CPI basket. Exact replication of the NBU core inflation index
is also problematic due to the differences in classification of the CPI basket given by COICOP
(post 2007) and by national methodology (2003-2006). Weights of the excluded items (utilities,
transportation, fruits and vegetables and some other raw food) replicate the weights of the
categories in the headline CPI. Weights for the period of 2003-2006 were constant (national
methodology). Weights for 2007-2010 change in the middle of each year. The smoothening
technique employed by the State statistical committee to deal with the transitory points was not
available to the public. The weights of certain excluded components varied over the period 20062010. The most remarkable changes occurred to 1) utilities: their contribution to CPI basket was
building up from 2006(6.6%) to 2009(8.11%) but dropped below 2006 weight in 2010 (6.5%). 2)
sugar weight reduced almost twofold (from 2.2% in 2006 to 1.24% in 2010) 3) petrol weight
increased 1.5 times in 2009-2010.
TABLE A4. Weights of selected items excluded from core inflation index, %
Utilities
Fruit
Vegetables
Sugar
Petrol
Bread
Butter
Milk
Rail
2003
6.6
3.1
5.1
2.2
0.7
3.9
1.3
4.2
0.2
2004
6.6
3.1
5.1
2.2
0.7
3.9
1.3
4.2
0.2
2005
6.6
3.1
5.1
2.2
0.7
3.9
1.3
4.2
0.2
2006
6.6
3.1
5.1
2.2
0.7
3.9
1.3
4.2
0.2
2007
7.31
3.15
5.04
2.28
0.88
5.03
1.25
3.16
0.24
2008
8.11
3.48
4.88
1.47
0.99
4.81
1.26
3.34
0.24
2009
8.11
3.48
4.88
1.47
1.38
4.64
1.26
3.33
0.23
2010
6.5
3.49
4.79
1.24
1.57
4.81
1.25
3.34
0.27
32
TABLE A5. Headline and exclusion based core (this study) consumer price inflation in Ukraine.
Item
Weight
Headline CPI
100%
Exclusion - based core CPI (this study)
59%
Non-core CPI, including
-
Water, electricity & gas
7.5%
-
Unprocessed food
24%
-
Other items
9.5%
Source: Author’s estimates. Note: Weights here are averages. Given for indicative purposes
33
Appendix 5. Data
TABLE A6. List of variables and notations
Variable
Description
Measurement
Source
CORE
Exclusion based core CPI YoY
%YoY
Authors' calculations based on
Ukstat data
CPI
Inflation YoY
%YoY
Ukrstat
PPI
YoY PPI growth
%YoY
Ukrstat
IP
Industrial production
ULC
Unit labor cost
%YoY
Calculations based on Ukrstat data
TRIMSD
Trimmed mean core CPI, based on
standard deviation
Trimmed mean core CPI, based on
fixed weights
UAH/USD exchange rate
%YoY
%YoY
Authors' calculations based on
Ukstat data
Authors' calculations based on
Ukstat data
NBU
TRIMFW
USD
Ukrstat
%YoY
NEER
Hryvnia Nominal effective Exchange
Rate (increase means depreciation)
Index
Haver, Authors' calculations
NEERY
NEER 12 months change
%YoY
Haver, Authors' calculations
UWAGE
Average Wage/Industrial Output,
%YoY
Average depreciation of Hryvnia
against Euro and Ruble, YoY
%YoY
Ukrstat
%YoY
Calculations based on NBU data
Index
Ukrstat
BM /CPI
Utilities price index (component of
CPI)
Utilities price index (component of
CPI)
Real Broad Money
DEPOSIT
Average deposit rate
M1Y
BASKET
UT
UTY
%YoY
Index
NBU, Authors' calculations
%
NBU
M1 growth
%YoY
NBU
M3Y
Broad Money(M3) growth
%YoY
NBU
OVERHANG
DEPOSIT
Resudials from the long-run equation
of money demand
Average deposit rate
FOOD
World Food Price Index
Index
Haver, World Bank
FOODY
World Food Price Index
%YoY
Haver, World Bank
GAP
Approximated using HodrickPrescott filter from monthly series of
industrial production (seasonally
adjusted using X12 procedure)
Authors' calculations
%
Percentage deviation
from the trend
NBU
Haver, Authors' calculations
34
TABLE A7. ADF test statistics and [P-Value]. H0: series has unit root
Series
Lag length (selected
by Schwarz criterion)
1
Intercept
Intercept and Trend
-2.12 [0.2382]
-1.86 [0.6666]
0
-5.20 [0.0000]
-5.47 [0.0001]
CORE (exclusion-based core
inflation)
D(CORE)
1
-2.58 [0.0996]
-2.43 [0.3622]
0
-5.44 [0.0000]
-5.55 [0.0001]
TRIMFW
(trimmed, fixed weights)
D(TRIMFW)
1
-2.68 [0.0807]
-2.50 [0.3256]
0
-3.93 [0.0026]
-4.04 [0.0101]
TRIMSD
(trimmed, standard deviation)
D(TRIMSD)
2
-2.50 [0.1194]
-2.29 [0.4345]
0
-5.31 [0.0000]
-5.43 [0.0001]
USD
2
-2.23 [0.1972]
-2.24 [0.4604]
D(USD)
1
-6.76 [0.0000]
-6.73 [0.0000]
WAGE
0
-1.82 [0.3676]
-2.29 [0.4337]
D(WAGE)
0
-10.19 [0.0000]
-10.14 [0.0000]
ULC
1
-2.28 [0.1792]
-2.28 [0.4418]
D(ULC)
0
-8.31 [0.0000]
-8.28 [0.0000]
GAP
0
-2.13 [0.2339]
-2.12 [0.5300]
D(GAP)
0
-9.79 [0.0000]
-9.76 [0.0000]
M3Y
5
-2.01 [0.2806]
D(M3Y)
4
-3.08 [0.0306]
CPI
(headline inflation)
D(CPI)
35
Appendix 6. Impulse response functions and variance decomposition
TABLE A8. Impulse response functions (3-variable VAR, 12 lags, 2004M01—2012M03)
From inflation to WAGE
From inflation to USD
Response of D(USDY) to Cholesky
One S.D. D(CPIY) Innovation
Response of D(WAGEY) to Cholesky
One S.D. D(CPIY) Innovation
.012
.025
.020
.008
.015
.004
.010
.000
.005
.000
-.004
-.005
-.008
-.010
-.012
-.015
1
2
3
4
5
6
7
8
9
1
10
2
Response of D(USDY) to Cholesky
One S.D. D(COREY) Innovation
3
4
5
6
7
8
9
10
9
10
9
10
9
10
Response of D(WAGEY) to Cholesky
One S.D. D(COREY) Innovation
.012
.025
.020
.008
.015
.010
.004
.005
.000
.000
-.005
-.004
-.010
-.008
-.015
1
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
Response of D(WAGEY) to Cholesky
One S.D. D(TRIMFWY) Innovation
Response of D(USDY) to Cholesky
One S.D. D(TRIMFWY) Innovation
.012
.03
.008
.02
.004
.01
.000
.00
-.004
-.01
-.008
-.012
-.02
1
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
Response of D(WAGEY) to Cholesky
One S.D. D(TRIMSDY) Innovation
Response of D(USDY) to Cholesky
One S.D. D(TRIMSDY) Innovation
.03
.015
.02
.010
.01
.005
.00
.000
-.01
-.005
-.010
-.02
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
36
TABLE A9. Variance Decomposition (3-variable VAR, 12 lags, 2004M01—2012M03
Variance Decomposition of D(COREY)
Variance Decomposition of D(CPIY)
100
100
80
80
60
60
40
40
20
20
0
0
1
2
3
4
5
D(CPIY)
6
7
D(WAGEY)
8
9
1
10
2
3
4
5
D(COREY)
D(USDY)
Variance Decomposition of D(TRIMFWY)
6
7
D(WAGEY)
8
9
10
D(USDY)
Variance Decomposition of D(TRIMSDY)
120
100
100
80
80
60
60
40
40
20
20
0
1
2
3
4
5
6
7
8
9
10
0
1
D(TRIMFWY)
D(WAGEY)
2
3
4
5
6
7
8
9
10
D(USDY)
D(TRIMSDY)
D(WAGEY)
D(USDY)
37
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