Working papers

Cross-validation is the most common data-driven procedure for choosing smoothing parameters in nonparametric regression. For the case of kernel estimators with iid or strong mixing data, it is well-known that the bandwidth chosen by crossvalidation is optimal with respect to the average squared error and other performance measures. In this paper, we show that the cross-validated bandwidth continues to be optimal with respect to the average squared error even when the datagenerating process is a -recurrent Markov chain. This general class of processes covers stationary as well as nonstationary Markov chains. Hence, the proposed procedure adapts to the degree of recurrence, thereby freeing the researcher from the need to assume stationary (or nonstationary) before inference begins. We study finite sample performance in a Monte Carlo study. We conclude by demonstrating the practical usefulness of cross-validation in a highly-persistent environment, namely that of nonlinear predictive systems for market returns.

Consider two heterogenous populations of agents who, when matched, jointly produce an output, Y. For example, teachers and classrooms of students together produce achievement, parents raise children, whose life outcomes vary in adulthood, assembly plant managers and workers produce a certain number of cars per month, and lieutenants and their platoons vary in unit effectiveness. Let W ∈ 𝕨= {ω1, . . . ,ωJ} and X ∈ 𝕩 = {x1, . . . ,xK} denote agent types in the two populations. Consider the following matching mechanism: take a random draw from the W = wj subgroup of the first population and match her with an independent random draw from the X = xk subgroup of the second population. Let β ;(wj, xk), the average match function (AMF), denote the expected output associated with this match. We show that (i) the AMF is identified when matching is conditionally exogenous, (ii) conditionally exogenous matching is compatible with a pairwise stable aggregate matching equilibrium under specific informational assumptions, and (iii) we calculate the AMF’s semiparametric efficiency bound.

We assigned two cohorts of kindergarten students, totaling more than 24,000 children, to teachers within schools with a rule that is as-good-as-random. We collected data on children at the beginning of the school year, and applied 12 tests of math, language and executive function (EF) at the end of the year. All teachers were filmed teaching for a full day, and the videos were coded using a wellknown classroom observation tool, the Classroom Assessment Scoring System (or CLASS). We find substantial classroom effects: A one-standard deviation increase in classroom quality results in 0.11, 0.11, and 0.07 standard deviation higher test scores in language, math, and EF, respectively. Teacher behaviors, as measured by the CLASS, are associated with higher test scores. Parents recognize better teachers, but do not change their behaviors appreciably to take account of differences in teacher quality.

This paper studies how targeted cash transfers to women affect their empowerment. We use a novel identification strategy to measure women's willingness to pay to receive cash transfers instead of their partner receiving it. We apply this among women living in poor households in urban Macedonia.

We estimate a dynamic model of employment, human capital accumulation - including education, and savings for women in the UK, exploiting tax and benefit reforms, and use it to analyze the effects of welfare policy.

This paper introduces a bivariate version of the generalized accelerated failure time model. It allows for simultaneity in the econometric sense that the two realized outcomes depend structurally on each other. Another feature of the proposed model is that it will generate equal durations with positive probability. The motivating example is retirement decisions by married couples. In that example it seems reasonable to allow for the possibility that each partner's optimal retirement time depends on the retirement time of the spouse.

We analyze the effect of patent thickets on entry into technology areas by firms in the UK.

In this article I provide a (selective) review of the recent econometric literature on networks.

This paper surveys some of the recent literature on inference in partially identified models.

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

In this paper, we propose a doubly robust method to present the heterogeneity of the average treatment effect with respect to observed covariates of interest.

This paper proposes a bootstrap-based procedure to build confidence intervals for single components of a partially identified parameter vector, and for smooth functions of such components, in moment (in)equality models.

We provide general compactness results for many commonly used parameter spaces in nonparametric estimation.

The partial (ceteris paribus) effects of interest in nonlinear and interactive linear models are heterogeneous as they can vary dramatically with the underlying observed or unobserved covariates. Despite the apparent importance of heterogeneity, a common practice in modern empirical work is to largely ignore it by reporting average partial effects (or, at best, average effects for some groups, see e.g. Angrist and Pischke (2008)).

We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method-of-moments criterion. Given these parameter estimates, the percentile levels of the outcome are re-adjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978-2000. We also extend the method to account for the presence of equilibrium effects when performing counterfactual exercises.

What explains the variation in how income changes as people age? Using household panel data, we investigate the contribution of different time-varying factors in explaining variation in income changes over prime working-age life (between 35-44 and 50-59). We find that demographic changes, such as acquiring or losing a partner and the entry or exit of children to and from the household, account for a larger share of the variation in household income changes than shifts in employment status or occupation.

Using the English Housing Survey, the authors estimate a supply side selection model of the allocation of properties to the owner-occupied and rental sectors.

Multivalued treatment models have only been studied so far under restrictive assumptions: ordered choice, or more recently unordered monotonicity. We show how marginal treatment effects can be identified in a more general class of models.

This paper contributes to the understanding of key drivers of stunted growth, a factor widely recognized as major impediment to human capital development.

The authors find that, under minimal primitive assumptions, it is possible to consistently estimate an upper bound on the magnitude of the bias, which is sufficient to deliver a valid confidence interval whose length decreases at the optimal rate and which does not contradict Stone’s results.