This paper investigates asymptotic properties of the maximim likelihood estimator and the quasi-maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those… (More)

We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero… (More)

In functional linear regression, the slope “parameter” is a function. Therefore, in a nonparametric context, it is determined by an infinite number of unknowns. Its estimation involves solving an… (More)

We develop a new test of a parametric model of a conditional mean function against a nonparametric alternative. The test adapts to the unknown smoothness of the alternative model and is uniformly… (More)

We suggest two nonparametric approaches, based on kernel methods and orthogonal series to estimating regression functions in the presence of instrumental variables. For the first time in this class… (More)

In this paper we develop semiparametric estimators of and in the model (Y ) = min[ X + U;C], where Y is a nonnegative dependent variable, X is a vector of explanatory variables, U is an unobserved… (More)

We study the asymptotic properties of bridge estimators in sparse, highdimensional, linear regression models when the number of covariates may increase to infinity with the sample size. We are… (More)

Ha .. rdle and Stoker (1989), Powell, et al. (1989), and Stoker (1991) have developed average derivative estimators of the parameter in the singleindex model E(Y X=x) = G(x ), where G is an unknown… (More)

This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables. The paper presents a test of the hypothesis that g… (More)