IDEAS home Printed from https://ideas.repec.org/p/com/wpaper/027.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://comisef.eu/files/wps027.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. Irina Tytell & Ksenia Yudaeva, 2005. "The Role of FDI in Eastern Europe and New Independent States: New Channels for the Spillover Effect," Working Papers w0060, Center for Economic and Financial Research (CEFIR).
    4. 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.
    5. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    6. Michael Porter, 2003. "The Economic Performance of Regions," Regional Studies, Taylor & Francis Journals, vol. 37(6-7), pages 549-578.
    7. 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.
    8. Eugenio J. Miravete & José C. Pernías, 2006. "Innovation Complementarity And Scale Of Production," Journal of Industrial Economics, Wiley Blackwell, vol. 54(1), pages 1-29, March.
    9. Kazunori Shinohara & Hiroshi Okuda, 2010. "Dynamic Innovation Diffusion Modelling," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 51-62, January.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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, September.
    21. 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.
    22. 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.
    23. 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.
    24. MacGarvie, Megan, 2005. "The determinants of international knowledge diffusion as measured by patent citations," Economics Letters, Elsevier, vol. 87(1), pages 121-126, April.
    25. Mark Funk, 2006. "Business cycles and research investment," Applied Economics, Taylor & Francis Journals, vol. 38(15), pages 1775-1782.
    26. 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.
    27. 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.
    28. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.
    29. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
    30. 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, Central European Journal of Economic Modelling and Econometrics, vol. 1(2), pages 111-138, November.
    31. 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.
    32. 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".
    33. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.
    34. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    35. Richard Blundell & Rachel Griffith & John van Reenen, 1999. "Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(3), pages 529-554.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. repec:elg:eechap:14395_24 is not listed on IDEAS
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    11. 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.
    12. 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.
    13. 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, Juni.
    14. 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.
    15. 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.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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, Juni.
    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 - Leibniz Centre for European Economic Research.
    4. Andrés Rodríguez-Pose & Marco Di Cataldo, 2015. "Quality of government and innovative performance in the regions of Europe," Journal of Economic Geography, Oxford University Press, vol. 15(4), pages 673-706.
    5. Andreas Sachs & Frauke Schleer, 2019. "Labor Market Performance in OECD Countries: The Role of Institutional Interdependencies," International Economic Journal, Taylor & Francis Journals, vol. 33(3), pages 431-454, July.
    6. 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.
    7. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    8. Andrés Rodríguez-Pose & Tobias D. Ketterer, 2012. "Do Local Amenities Affect The Appeal Of Regions In Europe For Migrants?," Journal of Regional Science, Wiley Blackwell, vol. 52(4), pages 535-561, October.
    9. Seker, Murat, 2011. "Effects of licensing reform on firm innovation : evidence from India," Policy Research Working Paper Series 5876, The World Bank.
    10. Donald W.K. Andrews & Biao Lu, 1999. "Consistent Model and Moment Selection Criteria for GMM Estimation with Applications to Dynamic Panel Data Models," Cowles Foundation Discussion Papers 1233, Cowles Foundation for Research in Economics, Yale University.
    11. Chu, Angus C. & Cozzi, Guido & Galli, Silvia, 2014. "Stage-dependent intellectual property rights," Journal of Development Economics, Elsevier, vol. 106(C), pages 239-249.
    12. Arellano, Manuel, 2016. "Modelling optimal instrumental variables for dynamic panel data models," Research in Economics, Elsevier, vol. 70(2), pages 238-261.
    13. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    14. Kazuhiko Hayakawa, 2008. "On the Effect of Nonstationary Initial Conditions in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d07-245, Institute of Economic Research, Hitotsubashi University.
    15. Badi H. Baltagi & Chihwa Kao, 2000. "Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey," Center for Policy Research Working Papers 16, Center for Policy Research, Maxwell School, Syracuse University.
    16. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    17. Florian Leon, 2017. "Implications of loan portfolio concentration in Cambodia," Economics Bulletin, AccessEcon, vol. 37(1), pages 282-296.
    18. Massimo Colombo & Annalisa Croce & Samuele Murtinu, 2014. "Ownership structure, horizontal agency costs and the performance of high-tech entrepreneurial firms," Small Business Economics, Springer, vol. 42(2), pages 265-282, February.
    19. Samuel Fosu, 2013. "Banking Competition in Africa: Sub-regional Comparative Studies," Discussion Papers in Economics 13/12, Division of Economics, School of Business, University of Leicester, revised Jun 2013.
    20. Florent Silve & Alexander Plekhanov, 2018. "Institutions, innovation and growth : Evidence from industry data," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 26(3), pages 335-362, July.

    More about this item

    Keywords

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:com:wpaper:027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anil Khuman (email available below). General contact details of provider: http://www.comisef.eu .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.