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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

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  • Peter Winker

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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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Suggested Citation

  • Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
  • Handle: RePEc:kap:compec:v:39:y:2012:i:4:p:337-363
    DOI: 10.1007/s10614-010-9243-x
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    Citations

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    Cited by:

    1. 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: Schriftenreihe des Promotionsschwerpunkts "Globalisierung und Beschäftigung" 39/2013, University of Hohenheim, Carl von Ossietzky University Oldenburg, Evangelisches Studienwerk.
    2. Blueschke, D. & Blueschke-Nikolaeva, V. & Savin, I., 2013. "New insights into optimal control of nonlinear dynamic econometric models: Application of a heuristic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 821-837.
    3. Savin Ivan, 2013. "A Comparative Study of the Lasso-type and Heuristic Model Selection Methods," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(4), pages 526-549, August.
    4. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    5. Herrmann, Johannes & Savin, Ivan, 2015. "Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112959, Verein für Socialpolitik / German Economic Association.
    6. repec:elg:eechap:14395_24 is not listed on IDEAS
    7. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    8. repec:ris:apltrx:0320 is not listed on IDEAS
    9. Jens K. Perret, 2016. "A Spatial Knowledge Production Function Approach for the Regions of the Russian Federation," EIIW Discussion paper disbei217, Universitätsbibliothek Wuppertal, University Library.
    10. 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.
    11. Jens K. Perret, 2016. "An Alternative Approach towards the Knowledge Production Function on a Regional Level - Applications for the USA and Russia," Schumpeter Discussion Papers SDP16003, Universitätsbibliothek Wuppertal, University Library.

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