MICROECONOMETRICS: Methods and Applications

Cambridge University Press, New York

May 2005

PART 3 (chapters 11-13)

Part 1 emphasized that: (1) Microeconometric models are often nonlinear; (2) they are frequently estimated using large and heterogeneous data sets; and (3) the data often come 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 that are difficult to estimate and analyze. Advances in computing hardware and software now make it feasible to tackle such tasks. Part 3 presents modern, computer-intensive, simulation-based methods of 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 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 for which there is 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. Despite this different approach, the Bayesian toolkit can also be adopted to permit classical estimation and inference for problems that are otherwise intractable.