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Cointegration Analysis of the Aggregate Production Function through Autoregressive Distributive Lags Models (ARDL)

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  • Plamen Petkov

Abstract

This paper tests the validity of two-factors and augmented aggregate production function, applying bounds testing (ARDL) approach to cointegration, which is more appropriate for estimation in small sample studies, using different combinations of variables that measure the production result, capital and labor. In the augmented production function, along with “conventional imputs” of labour and capital, are included “unconventional imputs” like real gross foreign investment direct flows and impact of trade openness variable (sum of export and import values to production ratio). Based on quarterly data for Bulgaria, covering the period from first quarter of 1996 to third quarter of 2008, long-run relationships are investigated using the bounds testing cointegaration procedure, developed by Pesaran, and short-run dynamic parameters are obtained by estimating an error correction model (VECM). The results show that production, labor and capital are cointegrated in 4 combinations without trend (three of them describing the augmented production function) and in 6 combinations with linear trend (two of them describing the augmented production function). All the valid combinations indicated that ?lasticity of capital is between 0.001% and 0.286%, while elasticity of labor is between 0.316% and 0.533%.

Suggested Citation

  • Plamen Petkov, 2009. "Cointegration Analysis of the Aggregate Production Function through Autoregressive Distributive Lags Models (ARDL)," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 3-35.
  • Handle: RePEc:bas:econst:y:2009:i:4:p:3-35
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    References listed on IDEAS

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    1. Elliott, Graham, 1999. "Efficient Tests for a Unit Root When the Initial Observation Is Drawn from Its Unconditional Distribution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(3), pages 767-783, August.
    2. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
    3. Dickey, David A & Pantula, Sastry G, 1987. "Determining the Ordering of Differencing in Autoregressive Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 455-461, October.
    4. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    5. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    6. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    7. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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