We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking...

This paper is concerned with the inference of nonparametric mean function in a time series context. The commonly used kernel smoothing estimate is asymptotically normal and the traditional inference procedure then consistently estimates the asymptotic variance function and relies upon normal...

Endogeneity and misspecification of models are two main concerns in structural estimation, which usually involves the optimal choices of economic agents with unobservable characteristics. In estimating production functions, input variables are endogenous because input decisions depend on...

Nonparametric function estimation via local polynomial regression has been widely studied in the literature, especially in the past two decades. In practice, we confront two challenges. Firstly, the local least squares estimator may not be the best choice when errors are heavily tailed....