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NAIRU Estimation in Romania ( including a comparison with other transition countries)

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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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  1. 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

  2. Contents • The paper’s incentives • Features of unemployment rate in Romania • Estimation methods • Comparison of results • Concluding remarks

  3. 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

  4. 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

  5. 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).

  6. Unemployment Rate in Romania (1994:1 – 2004:1)

  7. 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

  8. Hodrick-Prescott ( =1600)

  9. - 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:

  10. - 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:

  11. 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

  12. Period: 25.9808 ( 6.49521 'years') • Amplitude: 0.0142053 • Rho: 0.94072 • Variance: 0.000111226 Estimated parameters for the cycle:

  13. 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

  14. Univariate UC for Czech R.and Lithuania • Fitted model: • Intervention variables: Irr 2002. 1 & Irr 2003. 4 for Czech R.

  15. NAIRU (UC-1 trend) Czech R.

  16. UC-1 slope for Czech R.

  17. Unemployment gap Czech R.

  18. 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.

  19. 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

  20. 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

  21. Potential Output (trend UC-2) and Output Gap

  22. Unemployment Rates in Transition Economies

  23. 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’)

  24. 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

  25. Short-run commovements between unemployment rate in Czech R. and Hungary

  26. 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

  27. Seasonal comovements between unemployment rate in Poland and Romania

  28. Seasonal components in unemployment rate: Romania

  29. Seasonal components in unemployment rate: Poland

  30. Seasonal comovements between unemployment rate in Hungary and Slovakia

  31. Seasonal components in unemployment rate: Hungary

  32. Seasonal components in unemployment rate: Slovakia

  33. NAIRU (UC-2 trend) and unemployment gap in Romania Amplitude: 0.5306

  34. NAIRU (UC-2 trend) and unemployment gap in Czech R. Amplitude: 0.94145

  35. NAIRU (UC-2 trend) and unemployment gap in Lithuania Amplitude: 0.74114

  36. NAIRU (UC-2 trend) and unemployment gap in Poland Amplitude: 0.552935

  37. NAIRU (UC-2 trend) and unemployment gap in Slovakia Amplitude: 0.1882

  38. NAIRU (UC-2 trend) and unemployment gap in Hungary Amplitude: 0.32301

  39. 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)

  40. Zivot, Andrews test results

  41. Breakpoints endogenously determined by ZA test

  42. 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

  43. Constant NAIRU (u* = 6.98%)

  44. 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:

  45. Elmeskov Method

  46. 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).

  47. Time-varying NAIRU

  48. Comparison of results

  49. 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

  50. 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