An approximate dynamic programming approach to solving dynamic oligopoly models

In this article, we introduce a new method to approximate Markov perfect equilibrium in large‐scale Ericson and Pakes (1995)‐style dynamic oligopoly models that are not amenable to exact solution due to the curse of dimensionality. The method is based on an algorithm that iterates an approximate best response operator using an approximate dynamic programming approach. The method, based on mathematical programming, approximates the value function with a linear combination of basis functions. We provide results that lend theoretical support to our approach. We introduce a rich yet tractable set of basis functions, and test our method on important classes of models. Our results suggest that the approach we propose significantly expands the set of dynamic oligopoly models that can be analyzed computationally.     

Investment decisions in troubled times: A Bayesian approach applied to Brazilian firms

This study analyses the investment decision of 497 Brazilian firms during a period of unstable macroeconomic conditions. The role of financial constraints is considered in a Bayesian econometric model. We estimate three different models, and the results indicate the presence of financial restrictions, especially for capital-intensive firms. The recursive predictive density criterion indicates that the most preferred model is the one in which firm-specific effects are correlated with cash-flow. Financial restrictions are more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.