Research methods

High-dimensional linear models with endogenous variables play an increasingly important role in recent econometric literature.

This paper considers nonparametric additive models that have a deterministic time trend and both stationary and integrated variables as components.

We consider Bayesian estimation of state space models when the measurement density is not available but estimating equations for the parameters of the measurement density are available from moment conditions.

This paper provides new identification results for the bid–ask spread and the nonparametric distribution of the latent fundamental price increments (εt) from the observed transaction prices alone.

We propose two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags.

This paper proposes a specification test of the mixed logit models, by generalizing Hausman and McFadden’s (1984) test. We generalize the test even further by considering a model developed by Berry, Levinsohn and Pakes (1995).

This paper develops and implements a nonparametric test of Random Utility Models. The motivating application is to test the null hypothesis that a sample of cross-sectional demand distributions was generated by a population of rational consumers.

This paper proposes a powerful alternative to the t-test in linear regressions when a regressor is mismeasured.

We derive testable implications of model in which first best allocations are not achieved because of a moral hazard problem with hidden saving.

This paper examines the trends in geographic localization of knowledge spillovers via patent citations, considering US patents from the period of 1976-2015.

The understanding of co-movements, dependence, and influence between variables of interest is key in many applications. Broadly speaking such understanding can lead to better predictions and decision making in many settings.

In this article, we propose a general method for testing inequality restrictions on nonparametric functions. Our framework includes many nonparametric testing problems in a unified framework, with a number of possible applications in auction models, game theoretic models, wage inequality, and revealed preferences.

We propose a new estimator for the dynamic panel model, which solves the failure of strict exogeneity by calculating the bias in the first-order conditions as a function of the autoregressive parameter and solving the resulting equation.

Overall, high-skilled individuals form an important group of potential migrants because of both their relatively high rates of mobility and their potential contribution to the host economy. Studying migrant selection in this context is particularly useful because these migrants face low formal barriers to migration and are unlikely to be credit constrained. The observed selection patterns underline the relevance of the Roy/Borjas model in this setting.

We consider a variable selection problem for the prediction of binary outcomes. We study the best subset selection procedure by which the explanatory variables are chosen by maximizing Manski (1975, 1985)'s maximum score type objective function subject to a constraint on the maximal number of selected variables.

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities.

We show that the generalized method of moments (GMM) estimation problem in instrumental variable quantile regression (IVQR) models can be equivalently formulated as a mixed integer quadratic programming problem.

We propose a bootstrap-based calibrated projection procedure to build confidence intervals for single components and for smooth functions of a partially identified parameter vector in moment (in)equality models. The method controls asymptotic coverage uniformly over a large class of data generating processes.

This paper introduces two classes of semiparametric triangular systems with nonadditively separable unobserved heterogeneity. They are based on distribution and quantile regression modeling of the reduced-form conditional distributions of the endogenous variables.

This paper presents a simple non-asymptotic method for carrying out inference in IV models.