Working papers

This paper presents a method of calculating sharp bounds on the average treatment effect using linear programming under identifying assumptions commonly used in the literature.

In this paper we propose two alternative estimators. The first estimator relies on a generalization of common trends assumptions to fuzzy designs, while the second extends the changes-in-changes estimator of Athey & Imbens (2006). When the distribution of treatment changes in the control group, treatment effects are partially identified. Finally, we prove that our estimators are asymptotically normal and use them to revisit applied papers using fuzzy designs.

This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics.

This paper explains how to estimate and obtain an asymptotic uniform confidence band for a conditional mean function under possibly nonlinear shape restrictions, such as the Slutsky inequality.

The objective of this paper is to understand and test empirically the relationship between group size and informal risk sharing. Models of informal risk sharing with limited commitment and grim-trigger punishments upon deviation imply that larger groups provide better informal insurance.

We extend the standard intergenerational mobility literature by modelling individual outcomes as a function of the whole history of parental income, using data from Norway. We find that, conditional on permanent income, education is maximized when income is balanced between the early childhood and middle childhood years. In addition, there is an advantage to having income occur in late adolescence rather than in early childhood. These result are consistent with a model of parental investments in children with multiple periods of childhood, income shocks, imperfect insurance, dynamic complementarity and uncertainty about the production function and the ability of the child.

Call an economic model incomplete if it does not generate a probabilistic prediction even given knowledge of all parameter values. The authors propose a method of inference about unknown parameters for such models that is robust to heterogeneity and dependence of unknown form

The authors discuss the roles that assumptions and data play in partial identification analysis, with the goal of providing information to applied researchers that can help them employ these methods in practice.

This paper proposes a theoretical model to understand what social structures arise in heterogeneous populations.

This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous and/or discrete variables.

In this paper, the authors consider semiparametric model averaging of the nonlinear dynamic time series system where the number of exogenous regressors is ultra large and the number of autoregressors is moderately large.

The authors consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption based asset pricing Euler equations. This is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness).

This paper uses the frequentist statistical decision theory of Wald (1950) to study design of trials under ambiguity. The authors show that ε-optimal rules exist when trials have large enough sample size.

In this paper, the authors compile available methods for sample size calculations for continuous and binary outcomes with and without covariates, for both clustered and non-clustered RCTs.

The authors find that very broadly the LFS (Labour Force Survey) and administrative data show a similar distribution of graduates’ earnings. However, the administrative data has considerably less gender disparity, higher high quantiles and more time series persistence. The authors also report on how the distribution of graduate and non-graduate earnings fell during each year of the Great Recession.

Over the Great Recession UK households reduced real food expenditure. The authors show that they were able to maintain the number of calories that they purchased, and the nutritional quality of these calories, by adjusting their shopping behaviour.

This paper investigates how our impression of redistribution undertaken by the tax and benefit system changes when viewed from a lifetime perspective. To do so, the authors simulate lifecycle data designed to be representative of the experiences of the baby-boom cohort, born 1945–54.

This paper examines a general class of inferential problems in semiparametric and nonparametric models defined by conditional moment restrictions.

In this paper, the authors provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments.

Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with no large parameters and very many small non-zero parameters. The authors consider here a generalisation of these two basic models, termed here a “sparse + dense” model, in which the signal is given by the sum of a sparse signal and a dense signal.