Academy of Economic Studies

Doctoral School of Finance and Banking

Romania’s potential growth rates and output gap

MSc.: Catalin Condrache

Supervisor: Prof. Moisa Altar

Bucharest, July 2008

Contents

Preliminary aspects

Methods for estimating potential GDP

Model Definition

Modeling Romania’s situation

Estimating and testing methodology

Conclusions and future research

Abstract

The present working paper sets a goal to assess the impact of production progress, stock of capital, employment in the economy and human capital, within GDP formation. The approach is slightly different from that used so far in estimating potential GDP in the models for Romania’s production function, because in my opinion the current approach reveals the evolution of the potential GDP more realistically. In order to improve the production function model for Romania, I augmented the model by human capital approximation.

Preliminary aspects

Definitions

Potential GDP – represents the level of real GDP which the economy can produce without

generating inflationary pressures.

Output Gap – represents the difference, expressed in percentage points, between actual real

GDP and potential GDP. Importance The concept of potential GDP plays a key role in

understanding the economic long term growth theory. According to this theory the long term growth rate in GDP is explained by fundamentals factors, such as: the structure of the economy, demographic and educational factors, technology, etc.

Methods for estimating potential GDP

Univariate Methods Hodrick – Prescott Filter Band Pass Filter Models with unobserved components - Kalman Filter

Multivariate Methods Production Function, Cobb – Douglas Multivariate unobserved components models Structural Vector Autoregression model

Difficulties in estimating potential GDP

Short sample of usable data for Romania

Structural changes happened during the analyzed period

Official GDP data is published with a lag, being subsequently subject to revision

Unreliable statistical data for capital stock

Model Definition

The types of production function used in the literature are particular forms of the constant elasticity of substitution –CES

In all models, the Cobb – Douglas production function is used

The following equation seems to be a better approximation:

In order to surprise the dynamic of GDP we take the log

)1( tttt LKAY

1ttttt LHKAY

ttttt LHKAY log)1(loglogloglog

Modeling Romania’s situation

Data series: quarterly 1998 Q1 -2007 Q4

Real GDP (expressed in 2000 ct price)

Gross Formation of Fixed Capital (2000 ct price) - GFFC

Real accumulated capital

Employment in the economy

Human Capital

Modeling Romania’s situation

Challenge –estimation of capital stock Harberger (1978)- assumes a capital growth rate equal to

the average growth rate of real GDP.

K1 = K0 x (1 – φ) + I1

g = 4.70% (average growth rate of real GDP – for the period considered)

Φ = 5.0% (depreciation of fixed capital)

;)(

g

IK tt

Modeling Romania’s situation

Evolution of labor force – structural brake

The series was adjusted by assuming zero growth between 2001 Q4 – 2002 Q1, and the data prior to 2001 Q4 was recursively corrected using the quarterly difference taken from the data based on the previous methodology

Unadj Labor Force

-

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

1998

Q1

1998

Q4

1999

Q3

2000

Q2

2001

Q1

2001

Q4

2002

Q3

2003

Q2

2004

Q1

2004

Q4

2005

Q3

2006

Q2

2007

Q1

2007

Q4

Adj Labor Force

7,800,000

8,000,000

8,200,000

8,400,000

8,600,000

8,800,000

9,000,000

9,200,000

9,400,000

9,600,000

9,800,000

Modeling Romania’s situation

The following data sets likely to figure as human capital: Share of capital education expenditure in GDP

Share of employed with secondary and university education in the employment group over 15 years of age (Eurostat data; in line with levels 3 – 6 of ISCDE 1997)

Share of employed with university education in the employment group over 15 years of age

Share of employed with secondary and university education in employment group over 25 years of age

Share of employed with university education in the employment group over 25 years of age

Share of male with secondary and university education in the employment group over 15 years of age

Estimating and testing methodology

Census – X12 algorithm has been used to seasonally adjust all

time series

The employment in the economy data series was adjusted for structural break

In order to surprise the dynamic of GDP, I take a log of the Cobb – Douglas function

Modeling and testing methodology

The firs estimation showed a Durbin–Watson =0.536 (autocorrelation)

Remedy for serial correlation: Cochrane-Orcutt

ρ = 0.723194.

ttttt HLKY 4321

ttt 1

Variable Coefficient Std. Error t-Statistic Prob.  RHO(-1) 0.732194 0.121095 6.04643 0

Modeling and testing methodology

New error estimate

New equation

After all the adjustments were implemented, I obtained

New DW = 1.975663

1 ttt

ttttttttt HHLLKKYY )()()()1( 14131211

ttttt HLKY *4

*3

*2

*1

*

LogGDP = 1.673 + 0.29*LogK + 0.11*LogL + 0.62*LogH

Modeling and testing methodology

The coefficients are statistically different from zero at a 5% significance level

The percentage of the total variation in the dependent variable explained by the independent variables, R2, is at a good level of 84%

By adding the human capital to the C–D production function the R-squared has improved, increasing the accuracy of the forecasting

Constant returns to scale assumption, was tested using Wald test

T-Statistic Value   df     ProbabilityF-statistic 0.007611 (1, 36)   0.931Chi-square 0.007611 1   0.9305

Value   Std. Err.0.016221 0.185927

Wald Test:Equation: COBB_DOUGLAS

Null Hypothesis Summary:Normalized Restriction (= 0)-1 + C(2) + C(3) + C(4)

Modeling and testing methodology

Normality test

The residuals seems to be almost normally distributed Kurtosis is almost 3 Skewness is very close to zero Jarque-Berra – is at a small value

0

1

2

3

4

5

6

7

-0.04 -0.02 0.00 0.02

Series: ResidualsSample 1998Q2 2007Q4Observations 39

Mean -3.68e-16Median 0.000182Maximum 0.021764Minimum -0.037896Std. Dev. 0.014463Skewness -0.448283Kurtosis 2.620932

Jarque-Bera 1.539725Probability 0.463077

Calculating potential growth rates and output gap

Resulted production function

LogGDP = 1.673 + 0.29*LogK + 0.11*LogL + 0.62*LogH

The Total Factor Productivity (TFP) has the major impact

According to the GDP regression function, in order to create sustainable economic growth for the medium term, the solution is to rise human capital and stock of capital mainly

Country like Romania would attract capital and loose qualified labor force

Calculating potential growth rates and output gap

Growth rates of real GDP and potential GDP

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

Real GDP -1.16% 0.88% 5.72% 5.10% 5.15% 8.45% 4.22% 7.86% 6.05%

Potential GDP 2.00% 3.20% 4.50% 5.10% 5.30% 5.70% 5.90% 6.00% 6.20%

1999 2000 2001 2002 2003 2004 2005 2006 2007

Calculating potential growth rates and output gap

Output –gap

-5.00%

-4.50%

-4.00%

-3.50%

-3.00%

-2.50%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

Output-gap -3.45% -3.62% -4.45% -3.34% -3.34% -3.48% -0.97% -2.54% -0.83% -0.97%

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Conclusions and future research The results show an increasing annual potential GDP growth rate, from

an average of 3.70% in the period 1998 – 2002, to values of around 6% in recent period

Romania experienced in the past 10 years, a potential GDP growth rates above the those registered by new EU Central and Eastern European member states in their periods of high growth

The factors with the biggest impact in growth rates of potential GDP are total factor productivity, human capital and stock of capital

Estimating the production function, was the first step in understanding and analyzing real convergence

The convergence process concept has its origin in the exogenous model of growth of Robert Solow. According to it, the existence of some economies that have similar characteristics in terms of preferences and technologies, of some declining marginal efficiency as well as of a perfect flexibility from the production factors generates a reduction of the incomes differences between the countries (regions)

Thank you for your consideration!

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Sources of data – National Institute of Statistics, AMECO, EUROSTAT