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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.

Suggested Citation

  • Ivan Savin & Peter Winker, 2010. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection. Application on the Russian Innovative Performance," Working Papers 027, COMISEF.
  • Handle: RePEc:com:wpaper:027
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    1. Bucci, Alberto & Parello, Carmelo Pierpaolo, 2009. "Horizontal innovation-based growth and product market competition," Economic Modelling, Elsevier, vol. 26(1), pages 213-221, January.
    2. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    3. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
    4. Riccardo Crescenzi & Andrés Rodriguez-Pose & Michael Storper, 2007. "The territorial dynamics of innovation: a Europe-United States comparative analysis," Journal of Economic Geography, Oxford University Press, vol. 7(6), pages 673-709, November.
    5. MacGarvie, Megan, 2005. "The determinants of international knowledge diffusion as measured by patent citations," Economics Letters, Elsevier, vol. 87(1), pages 121-126, April.
    6. Mark Funk, 2006. "Business cycles and research investment," Applied Economics, Taylor & Francis Journals, vol. 38(15), pages 1775-1782.
    7. Gatu, Cristian & Kontoghiorghes, Erricos J. & Gilli, Manfred & Winker, Peter, 2008. "An efficient branch-and-bound strategy for subset vector autoregressive model selection," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1949-1963, June.
    8. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
    9. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    10. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    11. Michael Porter, 2003. "The Economic Performance of Regions," Regional Studies, Taylor & Francis Journals, vol. 37(6-7), pages 549-578.
    12. Daron Acemoglu & Philippe Aghion & Fabrizio Zilibotti, 2003. "Vertical Integration and Distance to Frontier," Journal of the European Economic Association, MIT Press, vol. 1(2-3), pages 630-638, 04/05.
    13. Eugenio J. Miravete & José C. Pernías, 2006. "INNOVATION COMPLEMENTARITY AND SCALE OF PRODUCTION -super-," Journal of Industrial Economics, Wiley Blackwell, vol. 54(1), pages 1-29, March.
    14. Kazunori Shinohara & Hiroshi Okuda, 2010. "Dynamic Innovation Diffusion Modelling," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 51-62, January.
    15. 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.
    16. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
    17. Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 34(2), pages 173-193, September.
    18. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    19. Teodosio Perez-Amaral & Giampiero M. Gallo & Halbert White, 2003. "A Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 821-838, December.
    20. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    21. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
    22. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
    23. Ivan Savin & Peter Winker, 2009. "Forecasting Russian Foreign Trade Comparative Advantages in the Context of a Potential WTO Accession," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 1(2), pages 111-138, November.
    24. E. Bacchiocchi & F. Montobbio, 2009. "Knowledge diffusion from university and public research. A comparison between US, Japan and Europe using patent citations," The Journal of Technology Transfer, Springer, vol. 34(2), pages 169-181, April.
    25. Giulio Cainelli & Rinaldo Evangelista & Maria Savona, 2006. "Innovation and economic performance in services: a firm-level analysis," Cambridge Journal of Economics, Oxford University Press, vol. 30(3), pages 435-458, May.
    26. Winker, Peter, 1996. "Causes and effects of financing constraints at the firm level: Some microeconometric evidence," Discussion Papers, Series II 292, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    27. Jefferson, Gary & Hu, Albert G. Z. & Guan, Xiaojing & Yu, Xiaoyun, 2003. "Ownership, performance, and innovation in China's large- and medium-size industrial enterprise sector," China Economic Review, Elsevier, vol. 14(1), pages 89-113.
    28. 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.
    29. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.
    30. Beñat Bilbao-Osorio & Andrés Rodríguez-Pose, 2004. "From R&D to Innovation and Economic Growth in the EU," Growth and Change, Wiley Blackwell, vol. 35(4), pages 434-455.
    31. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    32. Richard Blundell & Rachel Griffith & John van Reenen, 1999. "Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms," Review of Economic Studies, Oxford University Press, vol. 66(3), pages 529-554.
    33. Petra Opitz & Thomas Sauer, 1999. "Strategic Technology Alliances: A Way to Innovative Enterprises in Russia?," Post-Communist Economies, Taylor & Francis Journals, vol. 11(4), pages 487-501.
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    Citations

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    Cited by:

    1. 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.
    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. 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.
    6. 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.
    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," 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. 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.
    11. repec:ris:apltrx:0320 is not listed on IDEAS

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    Keywords

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

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