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Econ 615, Advanced Microeconometrics
Georgetown University
Fall 2013
John Rust,
Georgetown University
Optional (but Recommended) Texts: Dynamic Economics By Jerome Adda and Russell Cooper (2003) MIT Press.
Introductory lecture comparing structural and reduced form econometric methods
for inferring the demand for credit.
Readings:
Lectures 2-3: Discrete Decision Processes
Discrete Decision Processes are Problems where the choice variable
is restricted to a finite set of alternatives. I describe parametric
econometric methods for inferring the unknown parameters of these
processes, particularly the method of maximum likelihood
and survey some of the numerous applications of these
models in many different parts of economics. I discuss the
identification problem and show that these problems
are non-parametrically unindentified, and discuss the
implications of this result for empirical work in this area.
I also discuss recent work on Bayesian estimation and simulation
and other ``quasi monte carlo'' methods for solving and estimating
these models.
Readings:
Lectures 4-5: Continuous Decision Processes
Continuous Decision Processes are Problems where the choice variable
can take on a continuum of possible values. I describe parametric
econometric methods for inferring the unknown parameters of these
processes based on the "Euler Equation" and via parametric maximum
likelihood and simulated method of moments approaches. I discuss in
particular a problem arising in modeling optimal commodity price
speculation and the problem of endogenous sampling of prices
and the econometric problems this creates when one tries to estimate
the model via maximum likelihood methods. However I show that the
problem is quite tractable when one adopts a simulated minimum
distance esimator. The general lesson is that via simulation
methods, a huge range of endogeneity, measurement error, attrition,
selectivity bias and other types of econometric problems can be
handled in a very natural way, provided one is willing to do some
parametric modeling. I discuss the identification problem, which can
also be dicey when one combines behavioral modeling assumptions with
assumptions about processes leading to endogenous attrition,
participation, reporting and so forth. However for certain types
of problems, particularly for case of risk neutral profit maximizers,
results on non-parametric identification of unknows may be available.
Readings:
Lecture 6-7: Computation and Estimation of Static and Dynamic Games
I extend the single agent decision framework to multi-agent dynamic
games. This is much harder and is at the current frontier of research
in this area. We will discuss several recent papers that make
substantial headway on these topics.
Readings:
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