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

摘要

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 empiri-cal 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 unobserved heterogeneity and possible endogeneity of regressors. A new efficient solution to this problem is suggested, applying optimization heuristics, which exploits the inherent discrete nature of the model selection problem. 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.