Working papers

This work studies the large sample properties of the posterior-based inference in the curved exponential family under increasing dimension.

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

In this paper we evaluate the premise from the recent literature on Monte Carlo studies that an empirically motivated simulation exercise is informative about the actual ranking of various estimators when applied to a particular problem.

We estimate the effect of breastfeeding on children's development for a sample of uncomplicated births from low educated mothers.

This paper analyses whether higher lifetime income households save a larger share of their income.

In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes data-driven model selection and model averaging procedures that address this issue for the propensity score weighting estimation of the average treatment effects for treated (ATT).

The authors develop analytical and jackknife bias corrections for nonlinear panel data models with both individual and time effects.

This paper provides an overview of how innovations in "data mining" can be adapted and modified to provide high-quality inference about model parameters.

This working paper describes how the IFS’s model of the UK’s long-run public finances (and those of its constituent nations) is constructed.

We consider testing for weak instruments in a model with multiple endogenous variables. Unlike Stock and Yogo (2005), who considered a weak instruments problem where the rank of the matrix of reduced form parameters is near zero, here we consider a weak instruments problem of a near rank reduction of one in the matrix of reduced form parameters.

We consider estimation of policy relevant treatment effects in a data-rich environment where there may be many more control variables available than there are observations.

We study the problem of nonparametric regression when the regressor is endogenous, which is an important nonparametric instrumental variables (NPIV) regression in econometrics and a difficult ill-posed inverse problem with unknown operator in statistics.

This paper provides tools for partial identification inference and sensitivity analysis in a general class of semiparametric models.

In this paper, we estimate a collective model of household consumption and test the restrictions of collective rationality using z-conditional demands in the context of a large Conditional Cash Transfer programme in rural Mexico.

This paper develops a specification test for the instrument validity conditions in the heterogeneous treatment effect model with a binary treatment and a discrete instrument.