Terence C. Mills and Kerry Patterson xixstart is the Poisson model, this, as Greene shows, is insufficient as a general frame-
work; the extension is provided and illustrated with panel data from the German
health care system. A second application illustrates a mixed logit and error com-
ponents framework for modeling modes of transport choice (air, train, bus, car).
Overall, this chapter provides an indication, through the variety of its applications,
as to why discrete choice models have become such a significant part of applied
econometrics.
The theme of panel data methods and applications is continued in Chapter 12
by Andrew Jones. The application of econometrics to health economics has been
an important area of development over the last decade or so. However, this has not
just been a case of applying existing techniques: rather, econometrics has been able
to advance the subject itself, asking questions that had not previously been asked
- and providing answers. This chapter will be of interest not only to health eco-
nomics specialists, but also to those seeking to understand how treatment effects in
particular are estimated and to those investigating the extent of the development
and application of panel data methods (it is complemented by Colin Cameron
in Chapter 14). At the center of health economics is the question “What are the
impacts of specific health policies?” Given that we do not observe experimental
data, what can we learn from non-experimental data? Consider the problem of
evaluating a particular treatment; for an individual, the treatment effect is the dif-
ference in outcome between the treated and the control, but since an individual is
either treated or not at a particular time, the treatment effect cannot be observed.
“Treatment” is here a general term that covers not only single medical treatments
but also broad policies, and herein lies its generality, since a treatment could equally
be a policy to reduce unemployment or to increase the proportion of teenagers
receiving higher education. In a masterful understanding of a complex and expand-
ing literature, Jones takes the reader through the theoretical and practical solutions
to the problems associated with estimating and evaluating treatment effects, cov-
ering,inter alia, identification strategies, dynamic models, estimation methods,
different kinds of data, and multiple equation models; throughout the chapter
the methods and discussion are motivated by practical examples illustrating the
breadth of applications.
A key development in econometrics over the last thirty years or so has been the
attention given to the properties of the data, as these enlighten the question of
whether the underlying probability structure is stationary or not. In a terminologi-
cal shorthand, we refer to data that is either stationary or non-stationary. Initially,
this was a question addressed to individual series (see Nelson and Plosser, 1982);
subsequently, the focus expanded, through the work of Engle and Granger (1987)
and Johansen (1988), to a multivariate approach to non-stationarity. The next
step in the development was to consider a panel of multivariate series. In Chapter
13, Anindya Banerjee and Martin Wagner bring us up to date by considering panel
methods to test for unit roots and cointegration. The reader will find in this chapter
a theoretical overview and critical assessment of a vast and growing body of meth-
ods, combined with practical recommendations based on the insights obtained
from a wide base of substantive applications. In part, as is evident in other areas