Structural Estimation of Real Options Models
42 Pages Posted: 8 Jun 2006 Last revised: 11 Mar 2014
Date Written: March 1, 2008
We propose a numerical approach for structural estimation of a class of Discrete (Markov) Decision Processes emerging in real options applications. The approach is specifically designed to account for two typical features of aggregate data sets in real options: the endogeneity of firms' decisions; the unobserved heterogeneity of firms. The approach extends the Nested Fixed Point algorithm by Rust (1987,1988) because both the nested optimization algorithm and the integration over the distribution of the unobserved heterogeneity are accommodated using a simulation method based on a polynomial approximation of the value function and on recursive least squares estimation of the coefficients. The Monte Carlo study shows that omitting unobserved heterogeneity produces a significant estimation bias because the model can be highly non-linear with respect to the parameters.
Keywords: Real options, Markov Decision Processes, Discrete Decision Processes, Monte Carlo methods
JEL Classification: C15, C63, G13, G31
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