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Structural estimation of real options models

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  • Gamba, Andrea
  • Tesser, Matteo

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. Optimal replacement of GMC bus engines: an empirical model of Harold Zurcher. Econometrica 55(5), 999-1033; 1988. Maximum likelihood estimation of discrete control processes. SIAM Journal of Control and Optimization 26(5), 1006-1024] 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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 33 (2009)
Issue (Month): 4 (April)
Pages: 798-816

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Handle: RePEc:eee:dyncon:v:33:y:2009:i:4:p:798-816

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: Real options Markov decision processes Discrete decision processes Monte Carlo methods;

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References

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Cited by:
  1. Andrea GAMBA & Nicola FUSARI, 2008. "Valuing modularity as a real option," Swiss Finance Institute Research Paper Series 08-20, Swiss Finance Institute.
  2. Muehlenbachs, Lucija, 2012. "Testing for Avoidance of Environmental Obligations," Discussion Papers dp-12-12, Resources For the Future.

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