Microeconometrics: Methods and Applications A. Colin Cameron and Pravin K. Trivedi
MICROECONOMETRICS: Methods and Applications

Cambridge University Press, New York
May 2005

PART 6  (chapters 24-27)

Frequently in empirical work data present not one but multiple complications that the analysis must simultaneously deal with.  Examples of such complications include departures from simple random sampling, clustering of observations, measurement errors, and missing data.  When they occur, individually or jointly, and in the context of any of the models developed in Parts 4 and 5, identification of parameters of interest will be compromised.  Three chapters in Part 6 – Chapters 24, 26, and 27 – analyze the consequences of  such complications and then present methods that attempt to overcome the consequences. The methods are illustrated using examples taken from the earlier parts of the book. This features gives points of connection between Part 6 and the rest of the book. 

Chapter 24, which deals with features of data from complex surveys,  complements various topics covered Chapters 3, 5, and 16. Chapter 26 which deals with measurement errors complements topics in Chapter 4, 14, and 20. Chapter 27 is a stand-alone chapter on missing data and multiple imputation, but its use of the EM algorithm and Gibbs sampler also gives it points of contact with Chapters 10 and 13, respectively.

Chapter 25 deals with the important topic of treatment evaluation. Treatment is a broad term that refers to the impact of one variable, e.g. schooling, on some outcome variable, e.g. income. Treatment variables may be exogenously assigned, or may be endogenously chosen. The topic of treatment evaluation concerns the identifiability of the impact of treatment on outcome, as measured by either the marginal effects or certain functions of marginal effect.  A variety of methods are used including instrumental variables regression and propensity score matching. The problem of treatment evaluation can arise in the context of any model considered in parts 4 and 5. This chapter may also be read on its own, but it does presume familiarity with many other topics covered in the book, including instrumental variables and selection models, which is why it is placed in the last part.

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