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Heuristic Optimization Methods for Dynamic Panel Data Model Selection. Application on the Russian Innovative Performance

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  • Ivan Savin
  • Peter Winker

Abstract

Innovations, be they radical new products or technology improvements are widely recognized as a key factor of economic growth. To identify the factors triggering innovative activities is a main concern for economic theory and empirical analysis. As the number of hypotheses is large, the process of model selection becomes a crucial part of the empirical implementation. The problem is complicated by the fact that unobserved heterogeneity and possible endogeneity of regressors have to be taken into account. A new efficient solution to this problem is suggested, applying optimization heuristics, which exploits the inherent discrete nature of the problem. The model selection is based on information criteria and the Sargan test of overidentifying restrictions. The method is applied to Russian regional data within the framework of a log-linear dynamic panel data model. To illustrate the performance of the method, we also report the results of Monte-Carlo simulations.

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Bibliographic Info

Paper provided by COMISEF in its series Working Papers with number 027.

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Length: 33 pages
Date of creation: 04 Feb 2010
Date of revision:
Handle: RePEc:com:wpaper:027

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Web page: http://www.comisef.eu

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Keywords: Innovation; dynamic panel data; GMM; model selection; threshold accepting; genetic algorithms.;

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References

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Citations

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Cited by:
  1. D. Blueschke & V. Blueschke-Nikolaeva & Ivan Savin, 2012. "New Insights Into Optimal Control of Nonlinear Dynamic Econometric Models: Application of a Heuristic Approach," Jena Economic Research Papers 2012-008, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  2. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Society for Computational Economics, vol. 39(4), pages 337-363, April.
  3. Sachs, Andreas & Schleer, Frauke, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," ZEW Discussion Papers 13-040, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  4. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
  5. Ivan Savin, 2013. "A Comparative Study of the Lasso-type and Heuristic Model Selection Methods," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 233(4), pages 526-549, July.
  6. Hagemann, Harald & Kufenko, Vadim, 2014. "The political Kuznets curve for Russia: Income inequality, rent seeking regional elites and empirical determinants of protests during 2011/2012," Violette Reihe Arbeitspapiere 39/2013, Promotionsschwerpunkt "Globalisierung und Beschaeftigung".

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