This paper presents calculations of semiparametric efficiency bounds for quantile treatment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a...

Forecasting inflation remains an intriguing research topic among academics and practitioners alike. Recent studies by Stock and Watson (1999 JME, 2003 JEL) have documented the limited usefulness of Phillip curve type models using unemployment or other macroeconomic and financial variables for...

Economic policy decisions are often informed by empirical economic analysis. While the decision-maker is usually only interested in good estimates of outcomes, the analyst is interested in estimating the model. Accurate inference on the structural features of a model, such as cointegration, can...

This paper examines the intensity of financial crises during the 1990s with a view to informing crisis prevention and mitigation policies. We compare the performance of a full Bayesian and an information-theoretic approach in addressing the econometric problems posed by the lack of a unifying...

This paper provides conditions for identification and estimation of the conditional or unconditional average effect of a binary treatment or policy on a scalar outcome in models where treatment may be misclassified. Misclassification probabilities and the true probability of treatment are also...

When selection bias can purely be attributed to observables, several estimators have been discussed in the literature to estimate the average effect of a binary treatment or policy on a scalar outcome. Identification typically exploits the unconfoundedness of the treatment, which is verified if...

One of the lessons of the treatment effects literature is the lack of consensus about the ability of statistical and econometric methods to replicate experimental estimates. In this paper, we provide new evidence using an unusual unemployment insurance experiment that allows the identification...

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression...