Microeconometrics Methods and Applications

Part 1 emphasized that microeconometric models are frequently nonlinear models estimated using large and heterogeneous data sets drawn from surveys that are complex and subject to a variety of sampling biases. A realistic depiction of the economic phenomena in such settings often requires the use of models for which estimation and subsequent statistical inference are difficult. Advances in computing hardware and software now make it feasible to tackle such tasks. Part 3 presents modern, computerintensive, simulation-based methods of estimation and inference that mitigate some of these difficulties. The background required to cover this material varies somewhat with the chapter, but the essential base is least squares and maximum likelihood estimation.

Chapter 11 presents bootstrap methods for statistical inference. These methods have the attraction of providing a simple way to obtain standard errors when the formulae from asymptotic theory are complex, as is the case, for example, for some two-step estimators. Furthermore, if implemented appropriately, a bootstrap can lead to a more refined asymptotic theory that may then lead to better statistical inference in small samples.

Chapter 12 presents simulation-based estimation methods. These methods permit estimation in situations where standard computational methods may not permit calculation of an estimator, because of the presence of an integral over a probability distribution that leads to no closed-form solution.

Chapter 13 surveys Bayesian methods that provide an approach to estimation and inference that is quite different from the classical approach used in other chapters of this book.