Markov Chain Monte Carlo Methods in Corporate Finance

44 Pages Posted: 26 Nov 2011

See all articles by Arthur G. Korteweg

Arthur G. Korteweg

University of Southern California - Marshall School of Business

Date Written: November 25, 2011

Abstract

This chapter introduces Markov Chain Monte Carlo (MCMC) methods for empirical corporate finance. These methods are very useful for researchers interested in capital structure, investment policy, financial intermediation, corporate governance, structural models of the firm, and other areas of corporate finance. In particular, MCMC can be used to estimate models that are difficult to tackle with standard tools such as OLS, Instrumental Variables regressions and Maximum Likelihood. Starting from simple examples, this chapter exploits the modularity of MCMC to build sophisticated discrete choice, self-selection, panel data and structural models that can be applied to a variety of topics. Emphasis is placed on cases for which estimation by MCMC has distinct benefits compared to the standard methods in the field. I conclude with a list of suggested applications. Matlab code for the examples in this chapter is available on the author’s personal homepage.

Keywords: Markov Chain Monte Carlo, Corporate Finance

Suggested Citation

Korteweg, Arthur G., Markov Chain Monte Carlo Methods in Corporate Finance (November 25, 2011). Available at SSRN: https://ssrn.com/abstract=1964923 or http://dx.doi.org/10.2139/ssrn.1964923

Arthur G. Korteweg (Contact Author)

University of Southern California - Marshall School of Business ( email )

3670 Trousdale Parkway
Los Angeles, CA 90089
United States

HOME PAGE: http://www.marshall.usc.edu/personnel/arthur-korteweg

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