Chapter 11 Estimation for dirty data and flawed models

This chapter focuses on resistant estimation procedures and methods for evaluating the impact of particular data elements on regression estimates. Model builders using macroeconomic time series are often plagued by occasional unusual events, leading them to decrease the weights to be attached to these data in the spirit of resistant estimation. Even when there are good data and theory that correspond reasonably well to the process being modeled, there are episodic model failures. The chapter discusses some model failures that can arise in practice. It describes recent developments in methods for the detection of influential data in regression and discusses several issues about inference in the resistant case and the main theoretical foundations of robust and bounded-influence (BIF) estimation. The chapter presents an example of BIF applied to the HarrisonRubinfeld large cross-section hedonic price index. The chapter also presents some recent results on instrumental-variables bounded-influence estimation, and discusses resistant estimation for time-series models.