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THE ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING. NAIRU Estimation in Romania ( including a comparison with other transition countries). Student: Otilia Iulia Ciotau Supervisor: Professor Moisa Altar. BUCHAREST,2004. Contents. The paper’s incentives
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THE ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING NAIRU Estimation in Romania(including a comparison with other transition countries) Student: Otilia Iulia Ciotau Supervisor: Professor Moisa Altar BUCHAREST,2004
Contents • The paper’s incentives • Features of unemployment rate in Romania • Estimation methods • Comparison of results • Concluding remarks
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
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
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 aboutunderestimation of unemployment rate (masked unemployment, methodology); • Labor force working in informal economy; • Active measures for unemployment mitigation (Law no76/2002).
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
- is generated by the stochastic process: kt and kt* are uncorrelated w.n. with the same variance. - and its reduced form is a restricted ARMA(2,1): Univariate UC for Romania • Fitted model:
- same variance to each harmonic. is a pulse intervention variable: Seasonal component and intervention variable • The seasonal pattern is the sum of [s/2] (two for quarterly data) cyclical components, with frequencies:
The maximum likelihood estimates are: 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
Period: 25.9808 ( 6.49521 'years') • Amplitude: 0.0142053 • Rho: 0.94072 • Variance: 0.000111226 Estimated parameters for the cycle:
95% confidence interval for 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% Unemployment Rate Forecast
Univariate UC for Czech R.and Lithuania • Fitted model: • Intervention variables: Irr 2002. 1 & Irr 2003. 4 for Czech R.
Bivariate UC: unemployment rate and real GDP (1994:1-2003:3) • Okun’s law • SUTSE (Seemingly Unrelated Time Series Equations): • Intervention variable: • For unemployment series: irr 2002:1; • For GDP: level 1997:1.
Period: 22.6553 ( 5.66383 'years'); • Amplitude unemployment gap :0.02405; • Amplitude GDPgap:0.04185; • Rho: 0.9697843. Estimated parameters for the cycle: Common cycles
95% confidence interval for NAIRU: (2003:1) 9.333-10.375% (2003:2) 9.298-10.34% (2003:3) 9.342 -10.384% NAIRU (trend UC-2) and unemployment gap (cycle UC-2) UC-1 NAIRU
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. Multivariate framework • SUTSE model for six countries: • Estimated parameters for the similar cycle: • Rho = 0.96 • Period = 21.56 (5.38987 ‘years’)
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
Short-run commovements between unemployment rate in Czech R. and Hungary
Correlation between seasonal components • Czech R. • Hungary 0.176 • Lithuania -0.151 0.669 • Polonia 0.218 0.799 0.504 • Slovakia -0.019 0.8880.8260.673 • Romania 0.049 0.806 0.655 0.942 0.791
Seasonal comovements between unemployment rate in Poland and Romania
Seasonal comovements between unemployment rate in Hungary and Slovakia
NAIRU (UC-2 trend) and unemployment gap in Romania Amplitude: 0.5306
NAIRU (UC-2 trend) and unemployment gap in Czech R. Amplitude: 0.94145
NAIRU (UC-2 trend) and unemployment gap in Lithuania Amplitude: 0.74114
NAIRU (UC-2 trend) and unemployment gap in Poland Amplitude: 0.552935
NAIRU (UC-2 trend) and unemployment gap in Slovakia Amplitude: 0.1882
NAIRU (UC-2 trend) and unemployment gap in Hungary Amplitude: 0.32301
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)
Estimation of a constant NAIRU requires the introduction of a constant in (1): • For a time-varying NAIRU we use (1) as the measurement equation for a state space representation estimated with Kalman filter. Reduced-form methods • “Triangle model of inflation” (Gordon) where
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:
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).
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
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