Professor Joel L. Horowitz

Models with high-dimensional covariates arise frequently in economics and other fields. Often, only a few covariates have important effects on the dependent variable.

Book chapter; edited by Jeffrey S. Racine, Liangjun Su, and Aman Ullah.

A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter.

This paper presents a theoretically justified empirical method for choosing the regularization parameter in series estimation.

In this paper we suggest a new approach, which exploits to our advantage one of the difficulties that, in the past, has prevented an attractive solution to this problem - the fact that the standard bootstrap bias estimator suffers from relatively high-frequency stochastic error.

In this paper we suggest a new approach, which exploits to our advantage one of the difficulties that, in the past, has prevented an attractive solution to the problem—the fact that the standard bootstrap bias estimator suffers from relatively high-frequency stochastic error.

This paper presents such a method for use in series estimation, where the regularisation parameter is the number of terms in a series approximation to g.

We consider estimation of a linear or nonparametric additive model in which a few coefficients or additive components are "large" and may be objects of substantive interest, whereas others are "small" but not necessarily zero.

A Cemmap working paper.

This paper is concerned with developing uniform confidence bands for functions estimated nonparametrically with instrumental variables.

This paper develops a new method for estimating a demand function and the welfare consequences of price changes.

This paper develops a new method for estimating a demand function and the welfare consequences of price changes.

This paper develops a new method for estimating a demand function and the welfare consequences of price changes.