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Long Run and Short Effects in Static Panel Models

Author

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  • 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. 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.
    3. 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.
    4. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    5. 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.
    6. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    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:

    1. David I.Stern, 2009. "Between estimates of the environmental Kuznets curve," Environmental Economics Research Hub Research Reports 0934, Environmental Economics Research Hub, Crawford School of Public Policy, The Australian National University.
    2. Johannes Schwarze & Rainer Winkelmann, 2005. "What can happiness research tell us about altruism? Evidence from the German Socio-Economic Panel," SOI - Working Papers 0503, Socioeconomic Institute - University of Zurich, revised Sep 2005.
    3. INSEL, Aysu & TEKCE, Mahmut, 2010. "Econometric analysis of the bilateral trade flows in the Gulf Cooperation Council countries," MPRA Paper 22130, University Library of Munich, Germany.
    4. 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.
    5. 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.
    6. 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].

    More about this item

    Keywords

    Random Effects Models; Mundlak Model; Panel Econometrics;

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