期刊名称：Discussion Paper Series / Department of Economics, New York University
出版年度：2010
卷号：2010
期号：1
出版社：New York University
摘要：Many modern estimation methods in econometrics approximate an objective function,
through simulation or discretization for instance. The resulting \approximate" estimator
is often biased; and it always incurs an eciency loss. We here propose three methods to
improve the properties of such approximate estimators at a low computational cost. The
rst two methods correct the objective function so as to remove the leading term of the
bias due to the approximation. One variant provides an analytical bias adjustment, but
it only works for estimators based on stochastic approximators, such as simulation-based
estimators. Our second bias correction is based on ideas from the resampling literature;
it eliminates the leading bias term for non-stochastic as well as stochastic approximators.
Finally, we propose an iterative procedure where we use Newton-Raphson (NR) iterations
based on a much ner degree of approximation. The NR step removes some or all of
the additional bias and variance of the initial approximate estimator. A Monte Carlo
simulation on the mixed logit model shows that noticeable improvements can be obtained
rather cheaply.
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