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
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
2
1. Introduction
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)
3
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
4
3. Theoretical connection between output and
unemployment
Following Blanchard and Fisher (1989), the relation between output and
labour is given by the production function:
Y  sF L
F' 0
(1)
F " 0
(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
5
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
4. Unit root tests
160
13
150
12
140
11
130
10
120
9
110
8
7
100
6
90
93
94
95
96
97
98
99
00
01
02
03
93
output
94
95
96
97
98
99
00
01
02
03
unemployment rate
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)
7
p
yt  a0  yt 1    i yt i   t
(3)
i 1
RESULTS OF THE ADF TEST
p
a0
γ
γ+1
Conclusions
Industrial production index (IPI),
full sample
3
6.550
(1.696)
-0.053
(-1.694)
0.947
I (1)
Unemployment rate (UNR),
full sample
5
0.419
(1.985)
-0.045
(-2.067)
0.955
I (1)
Unemployment rate (UNR),
1993:01 – 2001:12
12
0.552
(3.308)
-0.058
(-3.368)
0.942
I (0)
Macroeconomic variable
RESULTS OF THE KPSS TEST
Macroeconomic variable
KPSS statistic
Conclusions
Industrial production index (IPI), full
sample
0.237
I (0)
Unemployment rate (UNR),
full sample
0.113
I (0)
Unemployment rate (UNR),
1993:01 – 2001:12
0.150
I (0)
8
5. Estimation of ARIMA (p,1,q) models for output
 L IPI t   L  t
(4)
IPI t   L  L t  AL t
(5)
IPI  1  L  L  L t  1  L AL t  BL t
(6)
1
1
1
1
i
Bi   A j
(7)
j 0
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:
(8)
A1
9
The estimated ARMA (2,2) model for the first difference of output, having
standard errors in parentheses, is:
IPI t  0.636IPI t 1  0.760IPI t  2   t  0.341 t 1  0.705 t  2
0.127
0.099
0.136
(9)
0.107
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.
10
6. Estimation of ARIMA (p,1,q) models for unemployment
rate
 L UNRt  c0 xt   L  t
(10)
xt 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):
UNRt  2.685xt  0.550UNRt 1
0.204
0.074
(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)
11
The sharp increase of the unemployment rate at the beginning of 2002
had only a temporary effect
Δ unemployment increase in January 2002 and the subsequent effects according to the
estimated AR (1) model
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
2.685
1.476
0.812
0.446
0.245
0.135
0.074
0.040
0.022
0.012
0.006
0.003
12
7. Bivariate analysis – Granger causality tests
Granger causality tests between output growth and unemployment growth, full sample
1993:01 – 2003:12
Null
hypotheses
Lags number
1
2
3
8
12
F-stat
Prob
F-stat
Prob
F-stat
Prob
F-stat
Prob
F-stat
Prob
ΔUNR
does not
Granger
cause ΔIPI
0.124
0.725
1.813
0.167
1.106
0.349
0.678
0.709
0.479
0.922
ΔIPI does
not
Granger
cause
ΔUNR
0.889
0.347
0.662
0.517
1.204
0.311
1.525
0.157
1.115
0.356
No causality between output and unemployment for the full sample
No causality between unemployment and output for the full sample
13
Granger causality tests between output growth and unemployment growth, shorter sample
1993:01 – 2001:12
Null
hypotheses
Lags number
1
2
3
8
12
F-stat
Prob
F-stat
Prob
F-stat
Prob
F-stat
Prob
F-stat
Prob
ΔUNR
does not
Granger
cause ΔIPI
1.982
0.162
2.960
0.056
1.912
0.132
1.117
0.360
1.492
0.147
ΔIPI does
not
Granger
cause
ΔUNR
0.210
0.647
0.200
0.818
0.406
0.748
1.164
0.330
1.466
0.158
There is a weak causality between unemployment and output with two lags.
14
8. A VAR analysis of the joint evolution of output and
unemployment
Impulse response functions
15
Variance decomposition for output growth
Period
Output growth
Unemployment growth
1
100.000
0.000
2
99.995
0.005
4
97.648
2.352
6
97.611
2.389
8
97.597
2.403
10
97.595
2.405
12
97.594
2.406
14
97.593
2.407
Variance decomposition for unemployment growth
Period
Output growth
Unemployment growth
1
0.106
99.894
2
2.537
97.463
4
4.150
95.850
6
4.227
95.773
8
4.228
95.772
10
4.230
95.770
12
4.230
95.770
14
4.231
95.769
16
9. Estimating Okun’s coefficient using dynamic OLS
Output gap:
y tc  y t  y tn
(12)
Cyclical unemployment rate:
U tc  U t  U tn
(13)
An autoregressive-distributed lag model is estimated for the cyclical
unemployment rate:
U
c
t

k
 U
i
i 1
c
t i

k

i 1
i
y tci   t
(14)
The impact of a change in output gap on cyclical unemployment rate in the
long-run is given by:
k
a
LR


i 1
1
i
(15)
k

i 1
i
17
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.
18
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
19
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