Economists have devoted increasing attention to the magnitude and consequences of measurement error in their data. Most discussions of measurement error are based on the “classical” assumption that errors in measuring a particular variable are uncorrelated with the true value of that...

We discuss the use of calibration techniques in economic models. Calibration contrasts with estimation in relying on deterministic calculation of model parameter values consistent with data, rather than econometric estimation. The reasons why calibrators use these methods, as well as the main...

This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods. These methods, which are concerned with the simulation of high dimensional probability distributions, have gained enormous prominence and revolutionized Bayesian statistics. The chapter, provides...

Until recently, inference in many interesting models was precluded by the requirement of high dimensional integration. But dramatic increases in computer speed, and the recent development of new algorithms that permit accurate Monte Carlo evaluation of high dimensional integrals, have greatly...

Since the early 1980s, the econometric analysis of duration variables has become widespread. This chapter provides an overview of duration analysis, with an emphasis on the specification and identification of duration models, and with special attention to models for multiple durations. Most of...

This paper describes a range of methods which have been proposed to study interactions in economic and social contexts. By interactions, we refer to interdependences between individual decisions which are not mediated by markets. These types of models have been employed to understand phenomena...

This chapter focuses on two of the developments in panel data econometrics since the Handbook chapter by Chamberlain (1984). The first objective of this chapter is to provide a review of linear panel data models with predetermined variables. We discuss the implications of assuming that...

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of...