Methods

This paper reviews recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics.

In this note, we offer an approach to estimating structural parameters in the presence of many instruments and controls based on methods for estimating sparse high-dimensional models.

This paper considers identification of treatment effects on conditional transition probabilities.

This paper considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n.

We develop uniformly valid confidence regions for regression coefficients in a high-dimensional sparse median regression model with homoscedastic errors.

This paper considers identification and estimation of ceteris paribus effects of continuous regressors in nonseparable panel models with time homogeneity.

The authors propose a notion of conditional vector quantile function and a vector quantile regression.

This work proposes new inference methods for the estimation of a regression coefficient of interest in quantile regression models.

The authors characterize the time-series properties of group-level consumption, income, and interest rates using microdata.

The authors evaluate the impact of a localized policing experiment that depenalized the possession of small quantities of cannabis in the London borough of Lambeth.

We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.

This article aims to provide some insight into the question by drawing lessons from the literature. The definition of "economic theory" includes a broad range of examples, such as accounting identities, disaggregation and spatial restrictions when forecasting aggregate variables, cointegration and forecasting with Dynamic Stochastic General Equilibrium (DSGE) models.

In this paper, the authors consider estimation of general modern moment-condition problems in econometrics in a data-rich environment where there may be many more control variables available than there are observations.

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Compu- tation which build likelihoods based on limited information.

This article investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLEs) of the GARCH model augmented by including an additional explanatory variable—the so-called GARCH-X model.

This paper evaluates a novel form of fiscal stimulus: a temporary cut in the rate of Value Added Tax (VAT).

In this paper we set out a theoretical framework for the systematic consideration of “randomisation bias”, and provide what is to our knowledge the first empirical evidence on this form of bias in an actual social experiment, the UK Employment Retention and Advancement (ERA) study.

The authors propose a new statistical test of the stochastic dominance efficiency of a given portfolio over a class of portfolios.

This paper develops a new approach to the estimation of consumer demand models with unobserved heterogeneity subject to revealed preference inequality restrictions.