Presentation on theme: "Academy of Economic Studies Bucharest Doctoral School of Finance and Banking DISSERTATION PAPER OUTPUT AND UNEMPLOYMENT DYNAMICS IN ROMANIA Student: SINCA."— Presentation transcript:
Academy of Economic Studies Bucharest Doctoral School of Finance and Banking DISSERTATION PAPER OUTPUT AND UNEMPLOYMENT DYNAMICS IN ROMANIA Student: SINCA FLORIN EUGEN Supervisor: Professor MOISA ALTAR
2 Presentation content 1.Introduction 2.The importance of output and unemployment dynamics in Romania 3.Theoretical connection between output and unemployment 4.Unit root tests 5.Estimation of ARIMA (p,1,q) models for output 6.Estimation of ARIMA (p,1,q) models for unemployment rate 7.Bivariate analysis – Granger causality tests 8.A VAR analysis of the joint evolution of output and unemployment 9.Estimating Okun’s coefficient using dynamic OLS 10.Conclusions
3 Output and unemployment dynamics is a subject of intense macroeconomic importance. It has been analyzed both in univariate in bivariate models: Campbell and Mankiw (1987) Blanchard and Quah (1989) Evans (1989) Weber (1995) Leon-Ledesma and McAdam (2003) 1. Introduction
4 2. The importance of output and unemployment dynamics in Romania as a present candidate and future member of the European Union Romania must undertake labour market reforms unemployment rate (7.2 % in 2003) is currently lower than EU average, but long unemployment duration (24.1 months during the third quarter of 2003) is a major problem and creates the image of a stagnant pool for Romanian unemployment Romania has to pursue high and constant economic growth rates and in this context shocks may affect both unemployment and output it is important to maintain an economic growth of 5 % in 2004 and 2005 without deteriorating labour market equilibriums
5 3. Theoretical connection between output and unemployment Following Blanchard and Fisher (1989), the relation between output and labour is given by the production function: (1) (2) equation (1) gives the straight relation between output and labour and not between output and unemployment labour force is represented by the total number of employees in the economy and in some cases the connection between the number of employees and the unemployment rate is weak if we consider the existence of a negative relation between the total number of employees and the unemployment rate, then there is a negative relation between output and unemployment rate
This negative relation between cyclical fluctuations in output and the level or the change of unemployment rate is known as Okun’s Law Okun’s Law says that an increase of 3 % in output over its normal growth rate over a year leads to an increase of 2 % in employment and a decrease of 1 % in the unemployment rate
7 4. Unit root tests The variables of this econometric analysis are: industrial production index, denoted as output (IPI, monthly observations 1993:01 – 2003:12, seasonally adjusted) unemployment rate (UNR, monthly observations 1993:01 – 2003:12, seasonally adjusted)
8 Macroeconomic variablepa0a0 γγ + 1Conclusions Industrial production index (IPI), full sample 36.550 (1.696) -0.053 (-1.694) 0.947I (1) Unemployment rate (UNR), full sample 5 0.419 (1.985) -0.045 (-2.067) 0.955I (1) Unemployment rate (UNR), 1993:01 – 2001:12 120.552 (3.308) -0.058 (-3.368) 0.942I (0) Macroeconomic variableKPSS statisticConclusions Industrial production index (IPI), full sample 0.237I (0) Unemployment rate (UNR), full sample 0.113I (0) Unemployment rate (UNR), 1993:01 – 2001:12 0.150I (0) RESULTS OF THE ADF TEST RESULTS OF THE KPSS TEST (3)
9 The long-run impulse response represents the response of IPI t+i to an innovation at time t, for large i and is given by: (4) (6) (5) (7) (8) 5. Estimation of ARIMA (p,1,q) models for output
10 The estimated ARMA (2,2) model for the first difference of output, having standard errors in parentheses, is: (9) Inverted AR roots are –0.32 ± 0.81i Inverted MA roots are –0.17 ± 0.82i Akaike information criterion is 5.809 Schwarz criterion is 5.898 Adjusted R-squared is 0.163 Ljung-Box Q-statistics are: Q(8)=1.938 (0.747) ; Q(16)=18.310 (0.107) ; Q(34)= 40.836 (0.090) Breusch-Godfrey Serial Correlation LM Test for 10 lags is 11.640 (0.309) The long-run impulse response is 0.854 implying that an innovation of 1 percent in output leads to a revision of the forecast in the long-run by an amount less than 1 percent.
11 6. Estimation of ARIMA (p,1,q) models for unemployment rate x t is a dummy variable that takes value 1 in 2002:01 and 0 in all other periods The estimated model for the first difference of unemployment rate, having standard errors in parentheses, is AR (1): (10) (11) Inverted AR root is 0.55 Akaike information criterion is –0.059 and Schwarz criterion is –0.015 Adjusted R-squared is 0.620 Ljung-Box Q -statistics are: Q(8)=2.786 (0.904) ; Q(16)=15.801 (0.395) ; Q(34)= 30.553 (0.590) Breusch-Godfrey Serial Correlation LM Test for 10 lags is 5.226 (0.875)
12 The sharp increase of the unemployment rate at the beginning of 2002 had only a temporary effect JanFebMarAprMayJuneJulyAugSeptOctNovDec 2.6851.4760.8120.4460.2450.1350.0740.0400.0220.0120.0060.003 Δ unemployment increase in January 2002 and the subsequent effects according to the estimated AR (1) model
13 Null hypotheses Lags number 123812 F-statProbF-statProbF-statProbF-statProbF-statProb ΔUNR does not Granger cause ΔIPI 0.1240.7251.8130.1671.1060.3490.6780.7090.4790.922 ΔIPI does not Granger cause ΔUNR 0.8890.3470.6620.5171.2040.3111.5250.1571.1150.356 7. Bivariate analysis – Granger causality tests Granger causality tests between output growth and unemployment growth, full sample 1993:01 – 2003:12 No causality between output and unemployment for the full sample No causality between unemployment and output for the full sample
14 Null hypotheses Lags number 123812 F-statProbF-statProbF-statProbF-statProbF-statProb ΔUNR does not Granger cause ΔIPI 1.9820.1622.9600.0561.9120.1321.1170.3601.4920.147 ΔIPI does not Granger cause ΔUNR 0.2100.6470.2000.8180.4060.7481.1640.3301.4660.158 Granger causality tests between output growth and unemployment growth, shorter sample 1993:01 – 2001:12 There is a weak causality between unemployment and output with two lags.
15 8. A VAR analysis of the joint evolution of output and unemployment Impulse response functions
17 9. Estimating Okun’s coefficient using dynamic OLS Output gap: Cyclical unemployment rate: An autoregressive-distributed lag model is estimated for the cyclical unemployment rate: The impact of a change in output gap on cyclical unemployment rate in the long-run is given by: (12) (13) (14) (15)
18 Potential output and natural unemployment rate are determined by Hodrick Prescott filter, using the smoothing parameters: 14,400 ; 10,000 and 40,000. Okun’s coefficient is estimated for the full sample 1993:01 – 2003:12 and also for the shorter sample 1993:01 – 2001:12. There are no important differences between these estimates and the value of Okun’s coefficient lies between –0.1239 and –0.0943.
19 10. Conclusions considering the results of the unit root tests and the estimated AR (1) model, the hysteresis hypothesis is rejected for the unemployment rate an output innovation of 1 percent leads to a revision of output forecast in the long-run of 0.854 percents there is a weak Granger causality between unemployment growth and output growth with a lag of two months the results of the estimated VAR illustrate a negative relation between output and unemployment attributed to Okun’s law according to VAR results, output presents a higher degree of persistence than unemployment; after ten months 1 % of the output initial innovation is still present in output, but only 0.62 % of the unemployment initial innovation is still present in unemployment variance decomposition for the estimated VAR shows that output explains a larger part of unemployment variance, while unemployment explains only a small part of output variation the estimated Okun’s coefficient is –0.120
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