Working papers

The Wald development of statistical decision theory addresses decision making with sample data. Wald's concept of a statistical decision function (SDF) embraces all mappings of the form [data => decision]. An SDF need not perform statistical inference; that is, it need not use data to draw conclusions about the true state of nature. Inference-based SDFs have the sequential form [data => inference => decision]. This paper offers remarks on the use of statistical inference in statistical decisions.

The “annuity puzzle” refers to the fact that annuities are rarely purchased despite the longevity insurance they provide. Most explanations for this puzzle assume that indi-viduals have accurate expectations about their future survival. We provide evidence that individuals misperceive their mortality risk, and study the demand for annuities in a setting where annuities are priced by insurers on the basis of objectively-measured survival probabilities but in which individuals make purchasing decisions based on their own subjective survival probabilities. Subjective expectations have the capacity to explain significant rates of non-annuitization, yielding a quantitatively important explanation for the annuity puzzle.

The political resource curse is the idea that natural resources can lead to the deterioration of public policies through corruption and rent-seeking by those closest to political power. One prominent consequence is the emergence of conflict. This paper takes this theory to the data for the case of Mozambique, where a substantial discovery of natural gas recently took place.

In a model with endogenous regressors, heteroskedastic and autocorrelated (HAC) errors and weak instruments, tests that depend on the data only through the Anderson-Rubin (AR) and Lagrange Multiplier (LM) statistics ignore important information on the regression coefficients. This is in contrast to the homoskedastic case, where these statistics, together with the rank statistic, are one-to-one with the maximal invariant. The information loss with heteroskedastic and/or autocorrelated errors can be so extreme that the LM and conditional quasi-likelihood ratio (CQLR) tests have power close to size when it is trivial to distinguish the null from the alternative hypothesis. The severe loss of power can occur if the Hermitian part of the reduced-form covariance matrix has eigenvalues of opposite signs.

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control.

This paper studies models in which hypothesis tests have trivial power, that is, power smaller than size.

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions.

James places Tinbergen’s work in context and discusses Tinbergen’s approach to using supply and demand to interpret the pricing of skills – a fundamental conceptual achievement. He shows how his work is related to the modern literature on hedonics and how it was and still is used to integrate the roles of technical change and supply side policies into a common equilibrium framework. Tinbergen’s research is then discussed on educational planning and his work on optimal inequality. In the final section, James assesses his contributions and discusses why they are relevant today. He speculates about why his work was neglected by many of his contemporaries.

This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression sub-problems, which are convex and can be solved efficiently. This allows for reformulating the original estimation problem as the problem of finding the fixed point of a low dimensional map.

Many empirical questions can be cast as inference on a parameter selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a winner’s curse, where conventional estimates are biased and conventional confidence intervals are unreliable.

This paper develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy objective is to maximize an equality-minded rank-dependent social welfare function, which puts higher weight on individuals with lower-ranked outcomes.

In this review, we document end-of-life medical spending: its level, composition, funding, and contribution to aggregate medical spending. We discuss how end-of-life expenses affect household behavior and economic evidence on the efficacy of medical spending at the end of life. Finally, we document recent trends in health and chronic disease at older ages and discuss what they might imply for end-of-life spending and medical spending in the aggregate.

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables.

We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved.

e develop a consistent estimator using external information on the true distribution of prices. Examining the demand for gasoline in the U.S., accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations. Imposing the Slutsky shape constraint greatly reduces the sensitivity to Berkson errors.

We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of the covariates. The model applies to continuous, discrete and mixed outcomes. We study the identification of the model, and develop a computationally attractive two-step method to estimate the model parameters, where the first step is a probit regression for the selection equation and the second step consists of multiple distribution regressions with selection corrections for the outcome equation.

We document employment rates of older men and women in the UK over the last forty years. In both cases growth in employment since the mid 1990s has been stronger than for younger age groups. On average, older men are still less likely to be in work than they were in the mid 1970s although this is not true for those with low education. We highlight issues with using years of schooling as a measure of educational achievement for analysing labour market trends at older ages, not least because a large proportion of men who left school at young ages without any formal qualifications, have subsequently acquired some.

In this paper we describe the history of state pension policy in the UK since the introduction of the State Pension in 1948. We calculate simple summary measures of the generosity of the system over time and of the degree to which the system has created implicit taxes on, or subsidies to, work at older ages.

This paper estimates how much additional work capacity there might be among men and women aged between 55 and 74 in the United Kingdom, given their health, and how this has evolved over the last decade.

Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. We consider heterogenous coefficients models where the outcome is a linear combination of known functions of treatment and heterogenous coefficients.