Research methods

This article measures the positive effect of a social intervention on individual willingness to cooperate and relates cooperation with traditional measures of social capital.

In this paper, the authors study a random-coefficients model for a binary outcome.

This paper estimates a structural model of private provision of public goods to provide some new empirical evidence on individuals' strategic contributing behaviors.

Fixed effects estimators of nonlinear panel data models can be severely biased because of the incidental parameter problem. In this paper, the authors develop analytical and jackknife bias corrections for nonlinear models with both individual and time effects.

The authors study identification and estimation in first-price auctions with risk-averse bidders and selective entry, building on a flexible entry and bidding framework they call the Affiliated Signal with Risk Aversion (AS- RA) model.

The authors propose several multivariate ratio statistics.

The authors propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data.

The authors present the clrbound, clr2bound, clr3bound, and clrtest commands for estimation and inference on intersection bounds as developed by Chernozhukov, Lee, and Rosen (2013, Econometrica 81: 667–737).

This paper presents new approaches to testing for exogeneity in non-parametric models with discrete regressors and instruments.

One of the main objectives of empirical analysis of experiments and quasi-experiments is to inform policy decisions that determine the allocation of treatments to individuals with different observable covariates. The authors propose the Empirical Welfare Maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies. The EWM approach is attractive in terms of both statistical performance and practical implementation in realistic settings of policy design.

The authors propose a two-step estimation method of stochastic volatility models. The estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise.

We investigate a nonparametric panel model with heterogeneous regression functions. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the observed data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real-data example.

The authors propose an alternative Ratio Statistic for measuring predictability of stock prices, based on actual returns rather than logarithmic returns and better suited to capturing price predictability.

Dynamic portfolio choice has been a central and essential objective for institutional investors in active asset management. In this paper, the authors study the dynamic portfolio choice depending on multiple conditioning variables, where the number of the conditioning variables can be either fixed or diverging to infinity at certain polynomial rate in comparison with the sample size.

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 a generalization 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.

The authors develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals

We propose new concepts of statistical depth, multivariate quantiles, ranks and signs, based on canonical transportation maps between a distribution of interest on IRd and a reference distribution on the d-dimensional unit ball.

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.