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

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Computational Econometrics ; No. A3-V2

Classification and Topics

• Classification: Wirtschaft ; Computational Techniques; Simulation Modeling ; Innovation and Invention: Processes and Incentives ; Model Evaluation, Validation, and Selection
• Subject: Innovation ; dynamic panel data ; GMM ; model selection ; threshold accepting ; genetic algorithms

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Savin, Ivan ; Winker, Peter
• Event: Veröffentlichung
• (who): Verein für Socialpolitik
• (where): Frankfurt a. M.
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/37270
• Last update: 10.03.2025, 11:42 AM CET