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NAIRU Estimation in Romania (including a comparison with other transition countries) Student: Otilia Iulia Ciotau Supervisor: Professor Moisa Altar THE.

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Presentation on theme: "NAIRU Estimation in Romania (including a comparison with other transition countries) Student: Otilia Iulia Ciotau Supervisor: Professor Moisa Altar THE."— Presentation transcript:

1 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

2 Doctoral School of Finance and Banking – June, 2004 Contents  The paper’s incentives  Features of unemployment rate in Romania  Estimation methods  Comparison of results  Concluding remarks

3 Doctoral School of Finance and Banking – June, 2004 Natural Rate and NAIRU Is 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 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

5 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).

6 Doctoral School of Finance and Banking – June, 2004 Unemployment Rate in Romania (1994:1 – 2004:1)

7 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

8 Doctoral School of Finance and Banking – June, 2004 Hodrick-Prescott ( =1600)

9 Doctoral School of Finance and Banking – June, 2004 Univariate UC for Romania  Fitted model: - is generated by the stochastic process: k t and k t * are uncorrelated w.n. with the same variance. - and its reduced form is a restricted ARMA(2,1):

10 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 : - same variance to each harmonic. is a pulse intervention variable:

11 Doctoral School of Finance and Banking – June, 2004 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 Doctoral School of Finance and Banking – June, 2004 Estimated parameters for the cycle: Period: 25.9808 ( 6.49521 'years') Amplitude: 0.0142053 Rho: 0.94072 Variance: 0.000111226

13 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:

14 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.

15 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-1 trend) Czech R.

16 Doctoral School of Finance and Banking – June, 2004 UC-1 slope for Czech R.

17 Doctoral School of Finance and Banking – June, 2004 Unemployment gap Czech R.

18 Doctoral School of Finance and Banking – June, 2004 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 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.

20 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

21 Doctoral School of Finance and Banking – June, 2004 Potential Output (trend UC-2) and Output Gap

22 Doctoral School of Finance and Banking – June, 2004 Unemployment Rates in Transition Economies

23 Doctoral School of Finance and Banking – June, 2004 Multivariate framework  SUTSE model for six countries: 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’)

24 Doctoral School of Finance and Banking – June, 2004 Correlation between cyclical components  Czech R.  Hungary0.983  Lithuania-0.244 -0.146  Polonia0.041 0.137 0.958  Slovakia0.176 0.104 0.459 0.523  Romania0.548 0.441 -0.004 0.155 0.848

25 Doctoral School of Finance and Banking – June, 2004 Short-run commovements between unemployment rate in Czech R. and Hungary

26 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

27 Doctoral School of Finance and Banking – June, 2004 Seasonal comovements between unemployment rate in Poland and Romania

28 Doctoral School of Finance and Banking – June, 2004 Seasonal components in unemployment rate: Romania

29 Doctoral School of Finance and Banking – June, 2004 Seasonal components in unemployment rate: Poland

30 Doctoral School of Finance and Banking – June, 2004 Seasonal comovements between unemployment rate in Hungary and Slovakia

31 Doctoral School of Finance and Banking – June, 2004 Seasonal components in unemployment rate: Hungary

32 Doctoral School of Finance and Banking – June, 2004 Seasonal components in unemployment rate: Slovakia

33 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-2 trend) and unemployment gap in Romania Amplitude: 0.5306

34 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-2 trend) and unemployment gap in Czech R. Amplitude: 0.94145

35 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-2 trend) and unemployment gap in Lithuania Amplitude: 0.74114

36 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-2 trend) and unemployment gap in Poland Amplitude: 0.552935

37 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-2 trend) and unemployment gap in Slovakia Amplitude: 0.1882

38 Doctoral School of Finance and Banking – June, 2004 NAIRU (UC-2 trend) and unemployment gap in Hungary Amplitude: 0.32301

39 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)

40 Doctoral School of Finance and Banking – June, 2004 Zivot, Andrews test results Country AIC Model A AIC Model B AIC Model C AIC Model D Best model Estimated for best model Tmin Unit root test outcome Czech R.1.142321.172351.098731.16907C0.5940293.8940 Not significant at 10% Hungary0.29012-0.029730.017300.43079B-0.60811-18231Significant at 5% Poland1.876651.326741.464561.79896B-0.067584.7456 Not significant at 10% Slovakia1.394491.857561.159081.33907D0.6073447.3205Significant at 1% Lithuania2.989712.624691.870742.87342C0.1886244.3976 Not significant at 10% Romania2.781142.762732.135813.21371C-0.320098.2090Significant at 1%

41 Doctoral School of Finance and Banking – June, 2004 Breakpoints endogenously determined by ZA test CountryBreakpointSignificance 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%

42 Doctoral School of Finance and Banking – June, 2004 Reduced-form methods  “Triangle model of inflation” (Gordon) where 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.

43 Doctoral School of Finance and Banking – June, 2004 Constant NAIRU (u* = 6.98%) Method: Least Squares Sample: 1994:1 2004:1 VariableCoefficientStd. Errort-StatisticProb. C9.9329655.1028611.9465480.0599 DINF(-2)-0.4045290.104494-3.8713200.0005 SOM(-1)-1.4237500.535690-2.6577870.0119 DSOM-2.3796471.320497-1.8020840.0804 CFE0.2207460.0382905.7651390.0000 OILM(-1)0.1660080.0637162.6054300.0135 REER-0.3305470.128512-2.5721060.0146 R-squared0.651039 Adjusted R-squared0.589458

44 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:

45 Doctoral School of Finance and Banking – June, 2004 Elmeskov Method

46 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).

47 Doctoral School of Finance and Banking – June, 2004 Time-varying NAIRU

48 Doctoral School of Finance and Banking – June, 2004 Comparison of results HPUnivar.UCBivar.UCMultivar.UCRecursiveElmeskovKalman1Kalman2 2002.019.3396 9.1483 9.70349.0274 9.3396 9.50587.09093.6776 2002.029.192 9.4607 9.80969.0080 9.1919 9.33717.08983.6412 2002.039.0318 9.1727 9.83978.9093 9.0318 9.14987.09623.6078 2002.048.862 8.9239 9.97138.8573 8.8619 8.90647.09963.5797 2003.018.6849 8.7293 9.85378.6359 8.6848 8.62447.1053.5547 2003.028.503 8.5727 9.81878.5837 8.5029 8.32057.12053.5241 2003.038.318 8.5915 9.86278.6024 8.3183 8.00617.13223.4987 2003.048.1327 8.5939 8.4646 8.1327 7.68927.14813.4794 2004.017.947 8.3691 8.4149 7.9469 7.37187.14813.4625

49 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

50 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

51 Doctoral School of Finance and Banking – June, 2004 Predictive-testing (Romania UC-1)

52 Doctoral School of Finance and Banking – June, 2004 Predictive-testing (Romania UC-1)

53 Doctoral School of Finance and Banking – June, 2004 Auxiliary observation residuals (Romania UC-1)

54 Doctoral School of Finance and Banking – June, 2004 CUSUM test UC-1

55 Doctoral School of Finance and Banking – June, 2004 Bivariate UC – better fit for unemployment than in univariate case

56 Doctoral School of Finance and Banking – June, 2004 Predictive testing: bivariate GDP

57 Doctoral School of Finance and Banking – June, 2004 Forecast for GDP and unemployment rate

58 Doctoral School of Finance and Banking – June, 2004 Predictive testing for multivariate UC-1

59 Doctoral School of Finance and Banking – June, 2004 Forecast multivariate UC

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