Quantitative Research Methods in Corporate Finance Exemplified by Stata, Python, and R

In regressions where the dependent variable takes limited values such as 0 and 1, or takes some category values, using the OLS estimation method will likely provide biased and inconsistent results. Because the dependent variable is either discontinuous or its range is bounded, one of the assumptions of the CLRM is violated (that the standard error is normally distributed conditional on the independent variables). This chapter focuses on limited dependent-variable models, for example, covering firm decision-making, capital structure decisions, investor decision-making, and so on. The chapter presents and discusses the linear probability model, maximum-likelihood estimator, probit model, logit model, ordered probit and logit models, multinomial logit model, conditional logit model, tobit model, Heckman selection model, and count data models. It covers the assumptions behind and applications of these models. As usual, the chapter provides an application of selected limited dependent-variable models, lab work, and a mini case study.