"Economic Modeling and Inference" takes econometrics to a new
level by demonstrating how to combine modern economic theory with
the latest statistical inference methods to get the most out of
economic data. This graduate-level textbook draws applications from
both microeconomics and macroeconomics, paying special attention to
financial and labor economics, with an emphasis throughout on what
observations can tell us about stochastic dynamic models of
rational optimizing behavior and equilibrium. Bent Jesper
Christensen and Nicholas Kiefer show how parameters often thought
estimable in applications are not identified even in simple dynamic
programming models, and they investigate the roles of extensions,
including measurement error, imperfect control, and random utility
shocks for inference. When all implications of optimization and
equilibrium are imposed in the empirical procedures, the resulting
estimation problems are often nonstandard, with the estimators
exhibiting nonregular asymptotic behavior such as short-ranked
covariance, superconsistency, and non-Gaussianity. Christensen and
Kiefer explore these properties in detail, covering areas including
job search models of the labor market, asset pricing, option
pricing, marketing, and retirement planning. Ideal for researchers
and practitioners as well as students, "Economic Modeling and
Inference" uses real-world data to illustrate how to derive the
best results using a combination of theory and cutting-edge
econometric techniques.Covers identification and estimation of
dynamic programming models Treats sources of error--measurement
error, random utility, and imperfect control Features financial
applications including asset pricing, option pricing, and optimal
hedging Describes labor applications including job search,
equilibrium search, and retirement Illustrates the wide
applicability of the approach using micro, macro, and marketing
examples
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