NAIRU Estimation in Romania (including a comparison with other transition countries)
Student: Otilia Iulia Ciotau
Supervisor: Professor Moisa Altar
THE ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING
BUCHAREST,2004
Doctoral School of Finance and Banking – June, 2004
Contents
The paper’s incentives Features of unemployment rate in
RomaniaEstimation methodsComparison of resultsConcluding remarks
Doctoral School of Finance and Banking – June, 2004
Natural Rate and NAIRUIs there any difference?
Natural rate of unemployment - Friedman (1968),
Phelps (1968): the level of unemployment to which the
economy would converge in the long run in the absence
of structural changes to the labor market; NAIRU (Non-Accelerating Inflation Rate of
Unemployment) - Modigliani and Papademos (1975): the rate of unemployment at which there is no tendency for inflation to increase or decrease
Doctoral School of Finance and Banking – June, 2004
Are NAIRU estimates useful?
“I have become convinced that the NAIRU is a useful analytic concept. It is useful as a theory to understand the causes of inflation. It is useful as an empirical basis for predicting changes in the inflation rate. And, it is useful as a general guideline for thinking about macroeconomic policy.”
Stiglitz, J. , Reflections on the Natural Rate Hypothesis
Doctoral School of Finance and Banking – June, 2004
Features of Unemployment Rate in Romania
The labor market have been strongly affected by the adjustment process from centrally planned to market-oriented economies;
Mass lay-offs; Issues about underestimation of unemployment rate
(masked unemployment, methodology); Labor force working in informal economy; Active measures for unemployment mitigation (Law
no76/2002).
Doctoral School of Finance and Banking – June, 2004
Unemployment Rate in Romania (1994:1 – 2004:1)
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
Unemployment rate
Seasonally adjustedunemployment rate
Doctoral School of Finance and Banking – June, 2004
Estimation methods
Statistical methods Hodrick-Prescott Filter Univariate UC Bivariate UC (Okun’s approach) Multivariate UC
Reduced-form methods Phillips curve with constant NAIRU Elmeskov method Phillips curve with time-varying NAIRU
Doctoral School of Finance and Banking – June, 2004
Hodrick-Prescott ( =1600)
2T
1t
1T
2t
1t*
t*
t*
1t*2
t*
t
1T
0tt )yy()yy()yy(minargy
6
7
8
9
10
11
12
13
94 95 96 97 98 99 00 01 02 03
Unemployment rate NAIRU (HP trend, 1600)
Doctoral School of Finance and Banking – June, 2004
Univariate UC for Romania
Fitted model:
);,0(~,)2( 21 NIDtttt
);,0(~,)1( 2 NIDwU ttttttt
;cossin
sincos**
1
1*
t
t
t
t
cc
cc
t
t
k
k
- is generated by the stochastic process:t
kt and kt* are uncorrelated w.n. with the same variance.
- and its reduced form is a restricted ARMA(2,1):*
122 sin)cos1()cos21( tctctc kkLLL
Doctoral School of Finance and Banking – June, 2004
Seasonal component and intervention variable
The seasonal pattern is the sum of [s/2] (two for quarterly data) cyclical components, with frequencies:
sjj /2
;cossin
sincos**
1,
1,*
jt
jt
tj
tj
jj
jjj
jt
jt
- same variance to each harmonic.
;]2/[
1
s
jjtt
tw is a pulse intervention variable: ;1:2002;,1
;,0
t
twt
Doctoral School of Finance and Banking – June, 2004
Estimated NAIRU (UC-1) - Romania
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
Unemployment
UC-1 Trend (NAIRU)
The maximum likelihood estimates are:
0000.1;102794.1ˆ
6844.0;10*7567.8ˆ
0012.0;10*5427.1ˆ
52
62
82
ratioq
ratioq
ratioq
95% confidence interval
for NAIRU:
(2003:2) 7.249-9.896%
(2003:3) 7.268-9.915%
(2003:4) 7.27 -9.918%
(2004:1) 7.045-9.693%
Back
Doctoral School of Finance and Banking – June, 2004
Unemployment gap (UC-1) - Romania
-3%
-2%
-1%
0%
1%
2%
3%
Estimated parameters for the cycle:
•Period: 25.9808 ( 6.49521 'years')•Amplitude: 0.0142053•Rho: 0.94072•Variance: 0.000111226
Doctoral School of Finance and Banking – June, 2004
Unemployment Rate Forecast
(2004:2) 6.201 - 8.296%
(2004:3) 5.15 - 8.224%
(2004:4) 5.208 - 9.055%
(2005:1) 6.202 - 10.68%
95% confidence interval
for unemployment rate
forecast:
Doctoral School of Finance and Banking – June, 2004
Univariate UC for Czech R.and Lithuania
Fitted model:
Intervention variables: Irr 2002. 1 & Irr 2003. 4
for Czech R.
`)2();,0(~,
;2
1
11
NIDtttt
ttt
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-1 trend) Czech R.
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UC-1 slope for Czech R.
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Unemployment gap Czech R.
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Bivariate UC: unemployment rate and real GDP (1994:1-2003:3)
Okun’s lawSUTSE (Seemingly Unrelated Time Series Equations):
;
;
1 ttt
ttttt cy
),( ttt UGDPy
).,0(~
);,0(~
NID
NID
t
t
Intervention variable:
•For unemployment series: irr 2002:1;
•For GDP: level 1997:1.
Doctoral School of Finance and Banking – June, 2004
Common cycles
Estimated parameters for the cycle:
•Period: 22.6553 ( 5.66383 'years');•Amplitude unemployment gap :0.02405;•Amplitude GDPgap:0.04185;•Rho: 0.9697843.
Doctoral School of Finance and Banking – June, 2004
NAIRU (trend UC-2) and unemployment gap (cycle UC-2)
95% confidence interval
for NAIRU:
(2003:1) 9.333-10.375%
(2003:2) 9.298-10.34%
(2003:3) 9.342 -10.384%
UC-1 NAIRU
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Potential Output (trend UC-2) and Output Gap
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Unemployment Rates in Transition Economies
0%
5%
10%
15%
20%
25%
Czech R. HungaryLithuania PolandRomania Slovakia
Doctoral School of Finance and Banking – June, 2004
Multivariate framework
SUTSE model for six countries:
;
;
1 ttt
ttttt cy
).,0(~
);,0(~
NID
NID
t
t
•Series are linked via the off diagonal elements in and ;
•This approach allows for detection of common features (Engle and Kozicki 1993): like trend, cycle, seasonal.
Estimated parameters for the similar cycle:•Rho = 0.96•Period = 21.56 (5.38987 ‘years’)
Doctoral School of Finance and Banking – June, 2004
Correlation between cyclical components Czech R. Hungary 0.983 Lithuania -0.244 -0.146 Polonia 0.041 0.137 0.958 Slovakia 0.176 0.104 0.459 0.523 Romania 0.548 0.441 -0.004 0.155 0.848
Doctoral School of Finance and Banking – June, 2004
Short-run commovements between unemployment rate in Czech R. and Hungary
Doctoral School of Finance and Banking – June, 2004
Correlation between seasonal components Czech R. Hungary 0.176 Lithuania -0.151 0.669 Polonia 0.218 0.799 0.504 Slovakia -0.019 0.888 0.826 0.673 Romania 0.049 0.806 0.655 0.942 0.791
Doctoral School of Finance and Banking – June, 2004
Seasonal comovements between unemployment rate in Poland and Romania
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Seasonal components in unemployment rate: Romania
Doctoral School of Finance and Banking – June, 2004
Seasonal components in unemployment rate: Poland
Doctoral School of Finance and Banking – June, 2004
Seasonal comovements between unemployment rate in Hungary and Slovakia
Doctoral School of Finance and Banking – June, 2004
Seasonal components in unemployment rate: Hungary
Doctoral School of Finance and Banking – June, 2004
Seasonal components in unemployment rate: Slovakia
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-2 trend) and unemployment gap in Romania
Amplitude: 0.5306
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-2 trend) and unemployment gap in Czech R.
Amplitude: 0.94145
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-2 trend) and unemployment gap in Lithuania
Amplitude: 0.74114
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-2 trend) and unemployment gap in Poland
Amplitude: 0.552935
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-2 trend) and unemployment gap in Slovakia
Amplitude: 0.1882
Doctoral School of Finance and Banking – June, 2004
NAIRU (UC-2 trend) and unemployment gap in Hungary
Amplitude: 0.32301
Doctoral School of Finance and Banking – June, 2004
Testing for hysteresis
ADF, PP: we cannot reject the unit root hypothesis for any of the six series;
Zivot and Andrews (1992) : unit root test with structural break endogenously determined (prg. EViews)
Doctoral School of Finance and Banking – June, 2004
Zivot, Andrews test results
CountryAIC
Model AAIC
Model B
AICModel
C
AICModel
D
Best model
Estimated for best model
TminUnit root test
outcome
Czech R. 1.14232 1.17235 1.09873 1.16907 C 0.594029 3.8940Not significant at
10%
Hungary 0.29012 -0.02973 0.01730 0.43079 B -0.60811 -18231 Significant at 5%
Poland 1.87665 1.32674 1.46456 1.79896 B -0.06758 4.7456Not significant at
10%
Slovakia 1.39449 1.85756 1.15908 1.33907 D 0.607344 7.3205 Significant at 1%
Lithuania 2.98971 2.62469 1.87074 2.87342 C 0.188624 4.3976Not significant at
10%
Romania 2.78114 2.76273 2.13581 3.21371 C -0.32009 8.2090 Significant at 1%
Doctoral School of Finance and Banking – June, 2004
Breakpoints endogenously determined by ZA test
Country Breakpoint Significance
Czech Republic 1998 q2 Not significant at 10%
Hungary 2001 q2 Significant at 5%
Poland 1998 q2 Not significant at 10%
Slovakia 1998 q4 Significant at 1%
Lithuania 2003 q3 Not significant at 10%
Romania 2001 q4 Significant at 1%
Doctoral School of Finance and Banking – June, 2004
Reduced-form methods
“Triangle model of inflation” (Gordon)
where *)1( u
ttttt ezLUULLA )())(()()1( *
•Estimation of a constant NAIRU requires the introduction of a constant in (1):
tttt zuLLA 1)()()2(
• For a time-varying NAIRU we use (1) as the measurement equation for a state space representation estimated with Kalman filter.
Doctoral School of Finance and Banking – June, 2004
Constant NAIRU (u* = 6.98%)
tt
ttttt
REEROILM
CFEUU
*33.0*17.0
*22.0*38.2*42.1*4.093.9
1
12
Method: Least Squares
Sample: 1994:1 2004:1
Variable Coefficient Std. Error t-Statistic Prob.
C 9.932965 5.102861 1.946548 0.0599
DINF(-2) -0.404529 0.104494 -3.871320 0.0005
SOM(-1) -1.423750 0.535690 -2.657787 0.0119
DSOM -2.379647 1.320497 -1.802084 0.0804
CFE 0.220746 0.038290 5.765139 0.0000
OILM(-1) 0.166008 0.063716 2.605430 0.0135
REER -0.330547 0.128512 -2.572106 0.0146
R-squared 0.651039
Adjusted R-squared 0.589458
Doctoral School of Finance and Banking – June, 2004
Elmeskov Method
simplified „accelerationist” version of Phillips curve:
An estimate of is obtained for any two consecutive
periods as which is substituted in (1) to give:
.0*),()1( ttt UU
t
tt U
2
ˆ
tt
ttt
UUU
)(*ˆ2
Doctoral School of Finance and Banking – June, 2004
Elmeskov Method
6
7
8
9
10
11
12
13
94 95 96 97 98 99 00 01 02 03
Unemployment rate NAIRU - Elmeskov method
Doctoral School of Finance and Banking – June, 2004
Time-varying NAIRU
The basic inflation equation:
is supplemented by a second equation that explicitly allows the NAIRU to vary with time:
The method of estimation is Kalman filter with a standard deviation of 0.2 for the state variable as a “smoothing prior” (Gordon 1997).
ttttt ezLUULLA )())(()( *
ttt UU *
1*
Doctoral School of Finance and Banking – June, 2004
Time-varying NAIRU
5.0
5.5
6.0
6.5
7.0
7.5
8.0
94 95 96 97 98 99 00 01 02 03
NAIRU (smoothed state estimate, KF, st.dev.=0.2)NAIRU (smoothed state estimate, KF, st.dev.=0.6)
Doctoral School of Finance and Banking – June, 2004
Comparison of results
HP Univar.UC Bivar.UC Multivar.UC Recursive Elmeskov Kalman1 Kalman2
2002.01 9.3396 9.1483 9.7034 9.0274 9.3396 9.5058 7.0909 3.6776
2002.02 9.192 9.4607 9.8096 9.0080 9.1919 9.3371 7.0898 3.6412
2002.03 9.0318 9.1727 9.8397 8.9093 9.0318 9.1498 7.0962 3.6078
2002.04 8.862 8.9239 9.9713 8.8573 8.8619 8.9064 7.0996 3.5797
2003.01 8.6849 8.7293 9.8537 8.6359 8.6848 8.6244 7.105 3.5547
2003.02 8.503 8.5727 9.8187 8.5837 8.5029 8.3205 7.1205 3.5241
2003.03 8.318 8.5915 9.8627 8.6024 8.3183 8.0061 7.1322 3.4987
2003.04 8.1327
8.5939
8.4646
8.1327
7.6892 7.1481 3.4794
2004.01 7.947
8.3691
8.4149
7.9469
7.3718 7.1481 3.4625
Doctoral School of Finance and Banking – June, 2004
Conclusion
The Romanian NAIRU is lower than in the other countries studied and also rather small comparing to Europe;
NAIRU in Romania is smooth comparing to the other five countries;
Uncertainty of the results
Doctoral School of Finance and Banking – June, 2004
Further direction for research
Estimating NAIRU based on unemployment rate calculated according to international accepted standard
Using the series from claimant count just for improving the accuracy in a bivariate UC model;
Harvey and Chung(2000), Estimating the underlying change in unemplyment in the Uk
Doctoral School of Finance and Banking – June, 2004
Predictive-testing (Romania UC-1)
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Predictive-testing (Romania UC-1)
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Auxiliary observation residuals (Romania UC-1)
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CUSUM test UC-1
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Bivariate UC – better fit for unemployment
than in univariate case
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Predictive testing: bivariate GDP
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Forecast for GDP and unemployment rate
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Predictive testing for multivariate UC-1
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Forecast multivariate UC