Markov chain Monte Carlo methods in corporate finance
Markov chain Monte Carlo methods in corporate finance
This chapter introduces Markov chain Monte Carlo (MCMC) methods and provides a hands-on guide to writing algorithms. It also illustrates some of the many applications of MCMC in corporate finance. The chapter is organized as follows. Section 26.2 introduces MCMC estimation through a simple regression example. Section 26.3 introduces the concept of data augmentation through a missing data problem. Section 26.4 discusses the limited dependent variable and sample selection models, currently the most widely used application of MCMC in corporate finance. Section 26.5 addresses panel data models, introduces the powerful tool of hierarchical modelling, and presents the application to capital structure regressions with attrition. Section 26.6 describes the estimation of structural models by MCMC, and in particular the concepts of Metropolis–Hastings sampling and Forward Filtering and Backward Sampling. Section 26.7 suggests further applications in corporate finance for which MCMC is preferable to classical methods, while Section 26.8 concludes.
Keywords: Markov chain Monte Carlo, corporate finance, estimation, Metropolis–Hastings sampling, forward filtering, backward sampling
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