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

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Author Info

  • 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

Related research

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. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.
  2. Andreas Sachs & Frauke Schleer, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," WWWforEurope Working Papers series 7, WWWforEurope.
  3. 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".
  4. 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.
  5. 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.
  6. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.

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