Structural Estimation of Real Options Models

42 Pages Posted: 8 Jun 2006 Last revised: 11 Mar 2014

Andrea Gamba

University of Warwick - Finance Group

Matteo Tesser

Polytechnic University of Catalonia (UPC)

Date Written: March 1, 2008

Abstract

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

Suggested Citation

Gamba, Andrea and Tesser, Matteo, Structural Estimation of Real Options Models (March 1, 2008). Journal of Economic Dynamics and Control, Forthcoming. Available at SSRN: https://ssrn.com/abstract=906990

Andrea Gamba (Contact Author)

University of Warwick - Finance Group ( email )

Scarman Road
Coventry, CV4 7AL
Great Britain
+44 (0)24 765 24 542 (Phone)
+44 (0)24 765 23 779 (Fax)

Matteo Tesser

Polytechnic University of Catalonia (UPC) ( email )

C. Jordi Girona, 31
Barcelona, 08034
Spain

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