tailieunhanh - Handbook of Econometrics Vols1-5 _ Chapter 11

Chupter I I ESTIMATION FOR DIRTY DATA AND FLAWED MODELS The comparison of different hypotheses, . of competing models, is the basis of model specification. It may be performed along two main lines. The first one consists in associating with each model a loss function and in retaining the specification | Chapter 11 ESTIMATION FOR DIRTY DATA AND FLAWED MODELS WILLIAM S. KRASKER Harvard University EDWIN KUH and ROY E. WELSCH Massachusetts Institute of Technology Contents 1. Introduction 652 2. Sources of model failure 658 3. Regression diagnostics 660 4. Bounded-influence regression 664 5. Aspects of robust inference 673 6. Historical background 676 7. Bounded-influence estimates for a hedonic price index 678 . The model 681 . Partial plots 681 8. Bounded-influence estimation with endogenous explanatory variables 691 9. Resistant time-series estimation 693 10. Directions for further research 695 References 696 This research was supported in part by the National Science Foundation . Department of Energy and . Handbook of Econometrics Volume I Edited by Z. Griliches and . Intriligator North-Holland Publishing Company 1983 652 IV. S. Krasker et al. 1. Introduction We are concerned with the econometric implications of the sensitivity to data of coefficient estimates policy analyses and forecasts in the context of a regression model. In contrast to the emphasis in standard treatments of the linear model paradigm described subsequently we are interested in data how they are generated and particular data configurations in the context of a specified regression model. The focus of this chapter is on resistant estimation procedures and methods for evaluating the impact of particular data elements on regression estimates. While terminology is not yet firmly fixed in this rapidly evolving area resistant estimation here is presumed to include classical robust estimation for location Andrews et al. 1972 or regression Huber 1977 and bounded-influence regression Krasker and Welsch 1982a . Classical robust estimation reduces the effect of outliers in error space. Bounded-influence regression in addition limits the permissible impact of outliers in explanatory-variable space. The time-honored point of departure in econometrics is the ordinary least squares OLS .