Optimization heuristics in econometrics: applications of threshold accepting
Optimization heuristics in econometrics: applications of threshold accepting
Optimized Multivariate Lag Structure Selection
Computational Economics - Special issue on computational studies at Cambridge
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
Subset selection for vector autoregressive processes using Lasso
Computational Statistics & Data Analysis
Dynamic Innovation Diffusion Modelling
Computational Economics
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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 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.