This paper formulates a model of undirected dyadic link formation which allows for assortative matching on observed agent characteristics (homophily) as well as unrestricted agent level heterogeneity in link surplus (degree heterogeneity).
This paper examines the long-term impacts on health and healthy behaviors of two of the oldest and most widely cited U.S. early childhood interventions evaluated by the method of randomization with long-term follow-up.
A new bandwidth selection rule that uses different bandwidths for the local linear regression estimators on the left and the right of the cut-off point is proposed for the sharp regression discontinuity estimator of the mean program impact at the cut-off point.
This paper considers the finite sample distribution of the 2SLS estimator and derives bounds on its exact bias in the presence of weak and/or many instruments.
The ill-posedness of the inverse problem of recovering a regression function in a nonparametric instrumental variable model leads to estimators that may suffer from a very slow, logarithmic rate of convergence. In this paper, the authors show that restricting the problem to models with monotone regression functions and monotone instruments significantly weakens the ill-posedness of the problem.
In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the sequence of bootstrap P values obtained by iterating the bootstrap.
The linear regression model is widely used in empirical work in Economics. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. Here, the authors give inference methods that allow for many covariates and heteroskedasticity.
Many empirical studies estimate the structural effect of some variable on an outcome of interest while allowing for many covariates. In this paper, the authors present inference methods that account for many covariates. The methods are based on asymptotics where the number of covariates grows as fast as the sample size.
This paper provides a constructive argument for identification of nonparametric panel data models with measurement error in a continuous explanatory variable.
Models with high-dimensional covariates arise frequently in economics and other fields. This paper reviews methods for discriminating between important and unimportant covariates with particular attention given to methods that discriminate correctly with probability approaching 1 as the sample size increases.
The triangular model is a very popular way to capture endogeneity. In this paper, the authors study the triangular model with random coefficients and exogenous regressors in both equations.
This paper makes several contributions to the literature on the important yet difficult problem of estimating functions nonparametrically using instrumental variables.
An incomplete model of English auctions with symmetric independent private values, similar to the one studied in Haile and Tamer (2003), is shown to fall in the class of Generalized Instrumental Variable Models introduced in Chesher and Rosen (2014). A characterization of the sharp identified set for the distribution of valuations is thereby obtained and shown to refine the bounds available until now.
The authors examine economic determinants of name choice amongst immigrants to the United States at the beginning of the 20th century, by studying the relationship between changes in the proportion of immigrants with an American first name and changes in the concentration of immigrants as well as changes in local labor market conditions, across different census years.
In this paper the authors show that theory-consistent demand analysis remains feasible in the presence of partially observed prices, and hence partially observed implied budget sets, even if we are agnostic about the nature of the missing prices.
