We develop a minimum distance estimator for dynamic games of incomplete information. We take a two-step approach, following Hotz and Miller (1993), based on the pseudo-model that does not solve the dynamic equilibrium so as to circumvent the potential indeterminacy issues associated with...

We propose a general two-step estimation method for the structural parameters of popular semiparametric Markovian discrete choice models that include a class of Markovian Games and allow for continuous observable state space. The estimation procedure is simple as it directly generalizes the...

Bajari, Benkard and Levin (2007) propose an estimation methodology for a broad class of dynamic optimization problems. To carry out their procedure, one needs to select a set of alternative policy functions and compare the implied expected payoffs with that from the data. We show that this can...

We develop a two-step estimator for a class of Markov decision processes with continuous control that is intuitive and simple to implement. Making use of the monotonicity assumption we estimate the expected continuation value functions nonparametrically in the first stage. In the second stage...

We develop a minimum distance estimator for dynamic games of incomplete information. We take a two-step approach, following Hotz and Miller (1993), based on the pseudo-model that does not solve the dynamic equilibrium in order to circumvent the potential indeterminacy issues associated with...

The estimation of dynamic games is known to be a numerically challenging task. In this paper we propose an alternative class of asymptotic least squares estimators to Pesendorfer and Schmidt-Dengler's (2008), which includes several well known estimators in the literature as special cases. Our...

We study the uniform convergence rates of a nonparametric estimator for a probability density function and its derivatives when the density has a known pole. Such situation arises in some structural microeconometric models, e.g. in auction, labor, and consumer search. Existing uniform...