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

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


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 about underestimation of unemployment rate

(masked unemployment, methodology); Labor force working in informal economy; Active measures for unemployment mitigation (Law

no76/2002).


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

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

Unemployment rate

Seasonally adjustedunemployment rate


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


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)


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


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


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


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


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%


for unemployment rate

forecast:


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


NAIRU (UC-1 trend) Czech R.


UC-1 slope for Czech R.


Unemployment gap Czech R.


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.


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.


NAIRU (trend UC-2) and unemployment gap (cycle UC-2)


for NAIRU:

(2003:1) 9.333-10.375%

(2003:2) 9.298-10.34%

(2003:3) 9.342 -10.384%

UC-1 NAIRU


Potential Output (trend UC-2) and Output Gap


Unemployment Rates in Transition Economies

0%

5%

10%

15%

20%

25%

Czech R. HungaryLithuania PolandRomania Slovakia


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’)


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 0.888 0.826 0.673 Romania 0.049 0.806 0.655 0.942 0.791


Seasonal comovements between unemployment rate in Poland and Romania


Seasonal components in unemployment rate: Romania


Seasonal components in unemployment rate: Poland


Seasonal comovements between unemployment rate in Hungary and Slovakia


Seasonal components in unemployment rate: Hungary


Seasonal components in unemployment rate: 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)


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%


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%


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.


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


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


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


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*


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)


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


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


Predictive-testing (Romania UC-1)


Auxiliary observation residuals (Romania UC-1)


CUSUM test UC-1


Bivariate UC – better fit for unemployment

than in univariate case


Predictive testing: bivariate GDP


Forecast for GDP and unemployment rate


Predictive testing for multivariate UC-1


Forecast multivariate UC

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

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