Methods

This paper proposes a simple and efficient estimation procedure for the model with non-ignorable missing data studied by Morikawa and Kim (2016).

In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive regression model, in which the predictive variables are stochastically nonstationary.

This paper studies a model with both a parametric global trend and a nonparametric local trend.

Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics.

New nonparametric methods that identify and estimate counterfactuals for individuals, when each is characterized by a vector of unobserved characteristics, are developed and applied to estimate systems of individual consumer demand and welfare measures.

Extremal quantile regression, i.e. quantile regression applied to the tails of the conditional distribution, counts with an increasing number of economic and financial applications such as value-at-risk, production frontiers, determinants of low infant birth weights, and auction models.

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments.

The R package Counterfactual implements the estimation and inference methods of Chernozhukov et al. (2013) for counterfactual analysis.

This paper introduces new inference methods for counterfactual and synthetic control methods for evaluating policy effects.

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.

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 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.

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).

Using the Reinhart–Rogoff dataset, we find a debt threshold not around 90 per cent but around 30 per cent, above which the median real gross domestic product (GDP) growth falls abruptly.

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

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.

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.