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Long Run and Short Effects in Static Panel Models

Author

Listed:
  • Peter Egger

    (University of Innsbruck)

  • Michael Pfaffermayr

    (University of Innsbruck)

Abstract

For short and fat panels the Mundlak model can be viewed as an approximation of a general dynamic autoregressive distributed lag model. We give an exact interpretation of short run and long effects and provide simulations to assess the quality of the approximation of the long run and short run effects by the parameters of the Mundlak Model.

Suggested Citation

  • Peter Egger & Michael Pfaffermayr, 2002. "Long Run and Short Effects in Static Panel Models," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B6-2, International Conferences on Panel Data.
    • Handle: RePEc:cpd:pd2002:b6-2
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    File URL: http://econpapers.repec.org/cpd/2002/44_Pfaffermayer.pdf
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    References listed on IDEAS

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    1. Cornwell, Christopher & Schmidt, Peter & Wyhowski, Donald, 1992. "Simultaneous equations and panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 151-181.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June.
    4. Baltagi, Badi H & Griffin, James M, 1984. "Short and Long Run Effects in Pooled Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(3), pages 631-645, October.
    5. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    6. Amemiya, Takeshi, 1971. "The Estimation of the Variances in a Variance-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 12(1), pages 1-13, February.
    7. Pirotte, Alain, 1999. "Convergence of the static estimation toward the long run effects of dynamic panel data models," Economics Letters, Elsevier, vol. 63(2), pages 151-158, May.
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    Cited by:

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    2. Ludsteck, Johannes, 2008. "Wage cyclicality and the wage curve under the microscope," IAB-Discussion Paper 200811, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Stern, David I., 2009. "Between Estimates of the Environmental Kuznets Curve," Research Reports 94883, Australian National University, Environmental Economics Research Hub.
    4. Olatunji Abdul Shobande, 2021. "Decomposing the Persistent and Transitory Effect of Information and Communication Technology on Environmental Impacts Assessment in Africa: Evidence from Mundlak Specification," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    5. INSEL, Aysu & TEKCE, Mahmut, 2010. "Econometric Analysis of the Bilateral Trade Flows in the Gulf Cooperation Council Countries," MPRA Paper 22184, University Library of Munich, Germany.
    6. Peter Egger & Hannes Winner, 2003. "Does Contract Risk Impede Foreign Direct Investment?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(II), pages 155-172, June.
    7. Desbordes, Rodolphe & Koop, Gary & Vicard, Vincent, 2018. "One size does not fit all… panel data: Bayesian model averaging and data poolability," Economic Modelling, Elsevier, vol. 75(C), pages 364-376.
    8. Völz, Manja & Wedow, Michael, 2009. "Does banks size distort market prices? Evidence for too-big-to-fail in the CDS market," Discussion Paper Series 2: Banking and Financial Studies 2009,06, Deutsche Bundesbank.

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    More about this item

    Keywords

    Random Effects Models; Mundlak Model; Panel Econometrics;
    All these keywords.

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