Methods

A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics.

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data.

This paper examines the trends in geographic localization of knowledge spillovers via patent citations, extracting multiple cohorts of new sample US patents from the period of 1976-2015.

From its inception, demand estimation has faced the problem of “many prices.”

The classical problem of the monopolist faced with an unknown demand curve is considered in a simple stochastic setting.

We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness.

We specify an equilibrium model of car ownership with private information where individuals sell and purchase new and second-hand cars over their life-cycle.

We present results on the identification of social networks from observational panel data that contains no information on social ties between agents.

The taxable income elasticity is a key parameter for predicting the effect of tax reform or designing an income tax.

We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters.

We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.

We establish nonparametric identification in a class of so-called index models using a novel approach that relies on general topological results.

The multinomial logit model with random coefficients is widely used in applied research. This paper is concerned with estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters.

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector.

This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data.

This paper proposes a simple nonparametric test of the hypothesis of no measurement error in explanatory variables and of the hypothesis that measurement error, if there is any, does not distort a given object of interest.

We propose a demand model where consumers simultaneously choose a few different goods from a large menu of available goods, and choose how much to consume of each good.

The idea of summarizing the information contained in a large number of variables by a small number of “factors” or “principal components” has been broadly adopted in economics and statistics.

In this paper, we describe how to test for the presence of measurement error in explanatory variables.

We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable.