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Monte Carlo analysis for dynamic panel data models

Author

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  • Giovanni S.F. Bruno

    (Universita Commerciale Luigi Bocconi, Milano)

Abstract

The Monte Carlo strategy by McLeod and Hipel (Water Resources Research, 1978), originally thought for time series data, has been adapted to dynamic panel data models by Kiviet (1995). This procedure is more efficient than the traditional approaches in that it generates start-up values according to the data generation process, so it avoids wasting random numbers in the generation of initial conditions and also small sample non-stationarity problems. This presentation discusses my Stata implementation of Kiviet's (Journal of Econometrics, 1995) procedure, as used in Bruno (2005) and (2004) to evaluate the finite sample properties of theoretical approximations for the LSDV bias (Bruno (Economics Letters 2005; UKSUG 2004)) and of the bias-corrected LSDV estimator (Bruno (2004); Italian SUG 2004) in the presence of unbalanced designs.

Suggested Citation

  • Giovanni S.F. Bruno, 2005. "Monte Carlo analysis for dynamic panel data models," United Kingdom Stata Users' Group Meetings 2005 06, Stata Users Group.
    • Handle: RePEc:boc:usug05:06
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    File URL: http://repec.org/usug2005/Bruno.pdf
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    File URL: http://repec.org/usug2005/xtarsim.ado
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    File URL: http://repec.org/usug2005/xtarsim.hlp
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    File URL: http://repec.org/usug2005/dyn_bias.do
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    File URL: http://repec.org/usug2005/static2way_bias.do
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    Cited by:

    1. Klinger, Sabine & Wolf, Katja, 2008. "What explains changes in full-time and part-time employment in Western Germany? : a new method on an old question," IAB-Discussion Paper 200807, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Robert Baumann & Bryan Engelhardt & Victor A. Matheson, 2012. "Labor Market Effects of the World Cup: A Sectoral Analysis," Chapters, in: Wolfgang Maennig & Andrew Zimbalist (ed.), International Handbook on the Economics of Mega Sporting Events, chapter 22, Edward Elgar Publishing.
    3. Martin Klein & Tobias Weirowski, 2011. "Trade and Unemployment in Germany: An Empirical Exploration and Some Theory," Global Financial Markets Working Paper Series 2011-24, Friedrich-Schiller-University Jena.
    4. Robert Baumann & Victor A. Matheson, 2013. "Estimating economic impact using ex post econometric analysis: cautionary tales," Chapters, in: Plácido Rodríguez & Stefan Késenne & Jaume García (ed.), The Econometrics of Sport, chapter 10, pages 169-188, Edward Elgar Publishing.
    5. Vincenzo Verardi & Ben Jann, 2021. "A robust regression estimator for pairwise-difference transformed data: xtrobreg," London Stata Conference 2021 17, Stata Users Group.
    6. Sabina Silajdzic & Eldin Mehic, 2017. "Trade Openness and Economic Growth: Empirical Evidence from Transition Economies," MIC 2017: Managing the Global Economy; Proceedings of the Joint International Conference, Monastier di Treviso, Italy, 24–27 May 2017,, University of Primorska Press.
    7. Peltonen, Tuomas A. & Sousa, Ricardo M. & Vansteenkiste, Isabel S., 2012. "Wealth effects in emerging market economies," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 155-166.
    8. Tehmina S. Khan, 2006. "Productivity Growth, Technological Convergence, RandD, Trade, and Labor Markets: Evidence From the French Manufacturing Sector," IMF Working Papers 2006/230, International Monetary Fund.
    9. Robert Baumann & Bryan Engelhardt & Victor Matheson, 2011. "Labor Market Effects of the World Cup: A Sectoral Analysis," Working Papers 1104, College of the Holy Cross, Department of Economics.

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