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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