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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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  • 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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    Cited by:

    1. Oleg S. Mariev & Karina M. Nagieva & Viktoria L. Simonova, 2020. "Managing innovation activity factors in Russian regions through econometric modeling," Upravlenets, Ural State University of Economics, vol. 11(1), pages 57-69, March.
    2. 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.
    3. 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.
    4. 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.
    5. Sachs, Andreas & Schleer, Frauke, 2019. "Labor Market Performance in OECD Countries: The Role of Institutional Interdependencies," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 33(3), pages 431-454.
    6. Ivan Savin & Peter Winker, 2012. "Lasso-type and Heuristic Strategies in Model Selection and Forecasting," Jena Economics Research Papers 2012-055, Friedrich-Schiller-University Jena.
    7. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    8. Herrmann, Johannes & Savin, Ivan, 2015. "Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112959, Verein für Socialpolitik / German Economic Association.
    9. repec:elg:eechap:14395_24 is not listed on IDEAS
    10. 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.
    11. Teplykh, Grigorii & Galimardanov, Amal, 2017. "Modeling of innovative investment in Russian regions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 46, pages 104-125.
    12. Andreas Sachs & Frauke Schleer, 2013. "Labour Market Performance in OECD Countries: A Comprehensive Empirical Modelling Approach of Institutional Interdependencies. WWWforEurope Working Paper No. 7," WIFO Studies, WIFO, number 46851.
    13. 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.
    14. 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 - Leibniz Centre for European Economic Research.
    15. Jens K. Perret, 2019. "Re-Evaluating the Knowledge Production Function for the Regions of the Russian Federation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(2), pages 670-694, June.
    16. 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.
    17. Delgado Castillo, Ángela & van den Bergh, Jeroen C.J.M. & Savin, Ivan & Sarto i Monteys, Víctor, 2020. "Cost-benefit analysis of conservation policy: The red palm weevil in Catalonia, Spain," Ecological Economics, Elsevier, vol. 167(C).

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