In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector. Examples that can be cast into this frame include threshold regression models with estimated thresholds, and structural break models with estimated...

We establish nonparametric identification in a class of so-called index models using a novel approach that relies on general topological results. Our proof strategy imposes very weak smoothness conditions on the functions to be identified and does not require any large support conditions on the...

The taxable income elasticity is a key parameter for predicting the e?ect of tax reform or designing an income tax. Bunching at kinks and notches in a single budget set have been used to estimate the taxable income elasticity. We show that when the heterogeneity distribution is unrestricted the...

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. Our simple kernel-based double debiased machine learning (DML) estimators for the average...

We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous...

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. In contrast to prior proposals for quantile regression estimation of...

The classical problem of the monopolist faced with an unknown demand curve is considered in a simple stochastic setting. Sequential pricing strategies designed to maximize discounted profits are shown to converge sufficiently rapidly that they leave the monopolist ignorant about all but the most...

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. Despite accelerating globalization and widespread perception of the "death of distance," our matched-sample...

From its inception, demand estimation has faced the problem of "many prices." While some aggregation across goods is always necessary, the problem of many prices remains even after aggregation. Although objects of interest may mostly depend on a few prices, many prices should be included to...

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi-log likelihood function in an appropriate way and then samples...