We propose a new estimation method for models defined by conditional moment restrictions,that minimizes a distance criterion based on kernel smoothing. Whether the bandwidth parameter is fixed or decreases to zero with the sample size, our approach defines a whole class of estimators. We develop...

This paper provides an analytic method of filtering for partially observed diffusions, which can be also used for parameter estimation with the quasi-maximum likelihood method. The filtering is shown to have consistency in a weak sense. In addition, using the stochastic volatility models, a...

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set. We show that...

We present the clrbound, clr2bound, clr3bound and clrtest commands for estimation and inference developed by Chernozhukov et al. (2013). The commands clrbound, clr2bound and clr3bound provide bound estimates that can be used directly for estimation or to construct asymptotically valid confidence...

This article proposes an omnibus test for monotonicity of nonparametric conditional distributions and its moments. Unlike previous proposals, our method does not require smooth estimation of the derivatives of nonparametric curves and it can be implemented even when the probability densities do...

This article proposes a nonparametric test of monotonicity for conditional distributions and its moments. Unlike previous proposals, our method does not require smooth estimation of the derivatives of nonparametric curves. Distinguishing features of our approach are that critical values are...