We investigate in this paper the main factors which drive inflation in Romania: inflation persistence, inflation expectations and real economy variables. We estimate a reduced form hybrid New Keynesian Phillips Curve in order to determine the degree of inertia and the impact of forward looking expectations. As a proxy for real economic activity, we alternatively use the change in the real labour cost, output gap, the capacity utilization rate, the economic sentiment indicator and the unemployment gap. We find that the capacity utilization rate and the unemployment gap are good proxies for the real economic activity. Inflation inertia is more important in explaining CPI inflation than rational expectations confirming the fact that inflation expectations in Romania are still highly adaptive.New Keynesian Phillips Curve, inflation dynamics, GMM estimation, forward looking expectations

Bogdan MURARASU

Nicoleta CIURILA

English

Research Papers in Economics

155 
 
INFLATION DYNAMICS IN ROMANIA – A NEW 
KEYNESIAN PERSPECTIVE 
NICOLETA CIURILĂ, BOGDAN MURĂRAŞU 
 
Nicoleta CIURILĂ, Assistant Professor 
Academy of Economic Studies 
Bogdan MURĂRAŞU, Assistant Professor 
Academy of Economic Studies 
 
 Key  words: New Keynesian Phillips Curve, inflation dynamics, GMM estimation, forward 
looking expectations 
 
 Abstract:We investigate in this paper the main factors which drive inflation in Romania: 
inflation persistence, inflation expectations and real economy variables. We estimate a reduced 
form hybrid New Keynesian Phillips Curve in order to determine the degree of inertia and the 
impact of forward looking expectations. As a proxy for real economic activity, we alternatively 
use the change in the real labour cost, output gap, the capacity utilization rate, the economic 
sentiment indicator and the unemployment gap. We find that the capacity utilization rate and the 
unemployment gap are good proxies for the real economic activity. Inflation inertia is more 
important in explaining CPI inflation than rational expectations confirming the fact that inflation 
expectations in Romania are still highly adaptive. 
 
I.  Introduction 
  The analysis of inflation dynamics is extremely important as it aims to determine 
the variables that might exert inflationary pressures in the short run and in the long run 
and are also important in the view of forecasting the inflation rate and its components. 
The Phillips Curve has become a standard framework for the analysis of short and 
medium term inflation for at least three reasons. First of all, the Phillips curve explains 
inflation using variables describing real economic activity such as real marginal cost, 
excess demand, output gap, unemployment, capacity utilization, unit labour cost or the 
economic sentiment indicator. Secondly, all macroeconomic models used by central 
banks incorporate a certain type of Phillips Curve. Thirdly, as a wide range of empirical 
analyses show, the Phillips Curve can be successfully used as a tool for forecasting 
inflation (Stock and Watson (1999), Matheson (2008)).  
  The early specifications of the Phillips Curve (Phillips (1958), Samuelson and 
Solow (1960)) are based on an empirically found relationship between the inflation rate 
or the nominal wage growth rate and the unemployment rate. However, the statistical 
relationship between the variables was found to be instable and even to break down in 
the 1970’s. The development of the New Keynesian economics, based on micro-
foundations and rational expectations, proved the theoretical relationship between 
aggregate economic activity and inflation expectations. 
  The New Keynesian Phillips Curve (NKPC) is based on the seminal work of Taylor 
(1980) and Calvo (1983) and is explicitly derived from an optimization process, 
assuming staggered price setting by forward looking, monopolistically competing firms. 
As a result of the optimization process, current inflation is related to future expected 
inflation and real marginal cost. Also, the parameters of the NKPC are directly linked to 
the behavior of agents and are thus exempt from the Lucas critique. The hybrid version 156 
of the NKPC is due to Gali and Gertler (1999) and it additionally incorporates inflation 
inertia. 
  In the following sections, we will estimate a reduced-form hybrid NKPC for the 
Romanian economy. To this end, we selected a number of explanatory variables to be 
used alternatively as a measure of real economic activity: the output gap as a proxy for 
excess demand, the real labour cost, the unemployment gap and the economic sentiment 
indicator. Regarding the inflation measure, empirical studies have estimated Phillips 
curves for the GDP deflator, CPI inflation, import prices inflation or producer prices 
inflation. We decided to use headline CPI inflation as explained variable although it 
contains highly volatile components because this is the target inflation rate of the 
National Bank of Romania. However, as an instrument in our estimations we use Core2 
inflation which excludes from the headline consumption basket regulated prices, 
volatile food prices (vegetable, fruit and eggs) and fuels prices. It is the common view 
of practitioners and theoreticians that regulated prices, vegetables, fruit and eggs and 
fuels depend mainly on exogenous factors and, hence, distort CPI inflation especially if 
they have an important contribution to the basket. In Romania, regulated prices have a 
contribution of 21.4% to the CPI basket, fuels represent 5.42% of the consumption 
basket and vegetable, fruit and eggs 6.76%. Figure 1 presents the development of 
different quarterly year on year inflation measures in Romania in the period of 2001-
2007. 
 
20.0
40.0
60.0
Regulated prices
Fuels
Vegetable, fruit, eggs
Core2
 
 
Fig. 1. The development of quarterly inflation in Romania  
Source: National Bank of Romania and own calculations 
 
  The figure above shows the high volatility of the inflation for regulated prices, 
fuels and vegetables, fruit and eggs compared to the smooth development of Core2 
inflation. Taking this into consideration, we will estimate the NKPC for CPI inflation, 
but we will use as an instrument the Core2 inflation. Figure 2 depicts the evolution of 
quarterly Core 2 inflation in comparison with quarterly CPI inflation in the period of 
2002Q1-2007Q4. 157 
 
Fig. 2. The development of quarterly Core2 inflation 
and quarterly CPI inflation in Romania  
Source: National Bank of Romania and own calculations 
 
  The rest of the paper is organized as follows: section II presents the methodology 
used to conduct our analysis, section III presents the results of the estimations, while 
section IV concludes. 
 
II.  Methodology 
  In order to analyze CPI inflation dynamics, we employ the classic version of the 
reduced form hybrid NKPC developed by Gali and Gertler(1999) for closed economies 
and extended for open economies by Leith and Malley (2007): 
 
[] t t t f t b t mc E ε λ π γ π γ π + + + = + − 1 1   (1) 
where  t π  is inflation at time t,   [] 1 + t E π  is the rational expectation of inflation for the 
next period,  t mc is the real marginal cost and  t ε  is the error term. 
  The open economy version of (1) also includes the real cost of imported goods: 
 
[] t t
m
t t
l
t f t b t q a w E ε λ λ π γ π γ π + + − + + = + − ) ( 1 1   (2) 
where  ) ( t t a w − is the real wage corrected for labour augmented technology, while 
t q is the real cost of a unit of imported goods. 
 The  coefficient  b γ  shows the inflation persistence or inflation inertia, while  f γ  
shows the relative importance of forward looking expectations in the formation of 
current inflation. Because the real marginal cost is not statistically available at 
aggregate level, there is much controversy in the literature regarding the appropriate 
proxy for this variable. Gali and Gertler (1999) and Gali, Gertler and Lopez-Salido 
(2005) use the unit labour cost as a proxy, while Gwin and VanHoose (2007) and 
Matheson (2008) investigate the use of a number of variables as proxy for the real 
marginal cost.  We will determine the appropriate variable describing aggregate 
economic activity by estimating equation (1) with different proxies for the real marginal 158 
cost: the deviation of real GDP from a Hodrick-Prescott filter (the output gap), the 
change in real labour costs, the capacity of utilization and the economic sentiment 
indicator. We also check for a relationship between inflation and unemployment by 
including in equation (1) as a measure of real economic activity the deviation of 
unemployment from a Hodrick-Prescott filter (unemployment gap). 
  As we have previously mentioned, we will estimate the NKPC for the CPI 
inflation. The original NKPC was estimated in deviations from the steady state. As this 
is inappropriate for the Romanian economy, we will estimate the NKPC for the 
deviation of the CPI inflation from a Hodrick-Prescott filter.  
  The estimations are conducted on quarterly data from 2000Q1 to 2007Q3. The data 
series come from the NBR database, the Eurostat database and the European 
Commission. We seasonally adjusted CPI and Core2 inflation using the Census X12 
procedure.  
  Considering the presence of rational expectations in the two equations, we will 
estimate them using the GMM procedure. For each model estimated, we employed the 
following instruments: two lags of the deviation of Core2 inflation from a Hodrick-
Prescott filter (t-1, t-2), one lag of the real interest rate (t-4), three lags of the real 
exchange rate gap (t-1, t-2, t-3) and two lags of the endogenous variables. 
 
III.  Results  
  The results of the estimations for equation (1) are summarized in Table 1. We 
report the values of the estimated parameters and the p-value for each coefficient. Each 
equation estimated with a certain measure of real economic activity is labeled with a 
number from 1 to 5. 
 
Table 1 
The value and significance of estimated coefficients for equation (1) 
 
Proxy for real marginal cost 
b γ   f γ   λ  
1. Change in Real 
labour cost  
Coefficient 0.348257* 0.66023* -0.13351* 
p-value 0.0000 0.0000 0.0115 
2. Output gap  Coefficient  0.444348* 0.327658* 0.010958 
p-value 0.0000 0.0545 0.9625 
3.Capacity 
utilization 
Coefficient 0.770781* 0.3331* 0.005212 
p-value 0.0001 0.0017 0.0777 
4.Economic 
sentiment indicator 
Coefficient 0.312779* 0.440929* -0.00205 
p-value 0.002 0.0286 0.2137 
5.Unemployment 
gap 
Coefficient 0.627826* 0.477248* -1.33219* 
p-value 0.0000 0.0000 0.0634 
* denotes significance at 10% significance level 
 
  A few important conclusions emerge from the table above. The Phillips Curve 
estimated using the change in real labour cost has statistically significant coefficients, 
but the sign of the change in real labour cost is not consistent with economic theory. 
The output gap and the economic sentiment indicator are both statistically insignificant 
which means that they don’t explain inflation dynamics in Romania. However, the 
coefficient of the capacity utilization rate and the unemployment gap are significant 159 
which means that we must analyze only models 3 and 5 in the above table. In both 
equations, forward looking expectations and inflation inertia are statistically significant. 
The inflation inertia coefficient is in both cases bigger than the forward looking 
component showing the fact that in Romania persistence was more important than 
expectations in explaining inflation dynamics. This result is sustained by other analyses 
performed on transition countries. Lendvai (2005) for Hungary, Ribon (2004) for Israel 
and Arratibel et al. (2002) for a number of New member states find that the weights of 
persistence and expectations in explaining inflation are roughly equal. Because 
Romania has experienced high inflation rates and has started the disinflationary process 
only in 2000, inflation expectations are still highly adaptive. Other countries, 
characterized by low and stable inflation rates and where expectations are firmly 
anchored have a much smaller contribution of inertia to inflation (Gali, Gertler, Lopez-
Salido (2001, 2005)).  
  The coefficient of capacity utilization is positive. An increase in the percentage 
utilization of production capacity generates additional costs and fosters inflationary 
pressures. On the other hand, a positive deviation of unemployment from its trend 
signifies the existence of a supply deficit and, hence, a decrease in inflation. 
  We also tested the following restriction on the coefficients describing inflation 
inertia and inflation expectations:  1 = + f b γ γ . This restriction insures the fulfillment 
of the neutrality principle which suggests that real variables don’t influence nominal 
variables in the long run. Both in case of model 3 and 5 the data sustain the restriction. 
 
IV.  Conclusions 
  The present paper characterizes inflation dynamics in Romania in the period of 
2000Q1-2007Q4 using the reduced form of a closed economy hybrid New Keynesian 
Phillips Curve. We explain CPI inflation using a lag of its own, the expected CPI 
inflation for the next quarter and a measure of real economic activity. As a proxy for the 
latter variable we use the change in real labour cost, the output gap, the capacity 
utilization rate, the economic sentiment indicator and the unemployment gap. We find 
statistically significant coefficients only in case of the change in real labour cost, the 
capacity utilization rate and the unemployment gap. However, the sign of the 
coefficient estimated for the real labour cost is economically incorrect so we discard 
that model. We conclude that the capacity utilization rate and the unemployment gap 
are successful in explaining the deviation of CPI inflation from its trend. We find that in 
Romania inertia was more important than expectations in characterizing inflation 
dynamics. This is common in case of transition economies that experienced high rates 
of inflation and where inflation expectations have a high adaptive component. We find 
that CPI inflation has an inertia of 0.77 or 0.63 according to the estimated model. Also, 
the inflation persistence coefficient and the inflation expectation coefficient statistically 
sum to one proving the fact that the data sustain the principle of neutrality. 
 
 
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Inflation Dynamics in Romania – a New Keynesian Perspective

Inflation Dynamics in Romania – a New Keynesian Perspective

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