Methods

We propose a framework for estimation and inference about the parameters of an economic model and predictions based on it, when the model may be misspecified.

It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents.

Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such ‘empirical Monte Carlo studies’ (EMCS) have been proposed.

This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions.

This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics.

We consider many structural objects of interest, such as average or quantile treatment effects. We illustrate our results with an empirical application to Engel curve estimation.

Shape constraints play an increasingly prominent role in nonparametric function estimation. While considerable recent attention has been focused on log concavity as a regularizing device in nonparametric density estimation, weaker forms of concavity constraints encompassing larger classes of densities have received less attention but offer some additional flexibility.

In this paper, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new Stata command [R] dgmtest for a nonparametric test proposed in Wilhelm (2018b).

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD-validity). We derive a new set of testable implications, characterized by a set of inequality restrictions on the joint distribution of observed outcomes and treatment status at the cut-off. We show that this new characterization exploits all the information in the data useful for detecting violations of FRD-validity.

This article introduces and investigates the properties of a new bootstrap method for time-series data, the kernel block bootstrap.

Given additional distributional information in the form of moment restrictions, kernel density and distribution function estimators with implied generalised empirical likelihood probabilities as weights achieve a reduction in variance due to the systematic use of this extra information.

This paper shows how to increase the power of Hausman’s (1978) specification test as well as the difference test in a large class of models.

This paper considers inference on fixed effects in a linear regression model estimated from network data.

This chapter sets out the extension of the scope of the classical IV model to cases in which unobserved variables are set-valued functions of observed variables.

We examine the labor market consequences of an exogenous increase in the supply of skilled labor in several cities in Norway, resulting from the construction of new colleges in the 1970s.

In our laboratory experiment, subjects, in sequence, have to predict the value of a good. We elicit the second subject’s belief twice: first (“first belief”), after he observes his predecessor’s action; second (“posterior belief”), after he observes his private signal. Our main result is that the second subjects weigh the private signal as a Bayesian agent would do when the signal confirms their first belief; they overweight the signal when it contradicts their first belief.

This paper provides estimation and inference methods for a structural function, such as Conditional Average Treatment Effect (CATE), based on modern machine learning (ML) tools.

We develop a theory for estimation of a high-dimensional sparse parameter 𝛳 defined as a minimizer of a population loss function LD(𝛳,g0) which, in addition to 𝛳, depends on a, potentially infinite dimensional, nuisance parameter g0.

Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications.

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an empirical approach for first-price sealed bid auctions with affiliated values, unobserved heterogeneity, and endogenous bidder entry.