Recent research underscores the sensitivity of conclusions drawn from the application of econometric methods devised for quantitative outcome variables to data featuring ordinal outcomes. The issue is particularly acute in the analysis of happiness data, for which no natural cardinal scale...

This paper focuses on the bivariate probit model's identifying assumptions: joint normality of errors, instrument exogeneity, and relevance conditions. First, we develop novel sharp testable equalities that can detect all possible observable violations of the assumptions. Second, we propose an...

This paper is concerned with the semiparametric estimation of function means that are scaled by an unknown conditional density function. Parameters of this form arise naturally in the consideration of models where interest is focused on the expected value of an integral of a conditional...

We develop novel regression discontinuity inferences where the binary treatment and/or continuous assignment variable may contain measurement errors. For a measurement error of the binary treatment, the standard estimator is inconsistent for the causal parameter. To solve the problem, we develop...

This paper introduces the conditional likelihood estimator of relative risk (CLERR). The CLERR estimates the relative risk of an outcome analogously to the way the conditional logit estimates an odds ratio. Aside from the fact that relative risk is often the preferred measure of association, the...

This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum...

Current estimates of global poverty vary substantially across studies. In this paper we undertake a sensitivity analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using different data sources, parametric and nonparametric...

This paper develops and implements a GMM estimator for latent class models suitable for count data. The estimator uses conditional moment restrictions derived from standard count models. Both the efficient and consistent variants are considered. The implementation of optimal GMM based on...

When making decisions, agents tend to make use of decisions others have made in similar situations. Ignoring this behavior in empirical models can be interpreted as a problem of omitted variables. We suggest a possibility of integrating such outside influences into models of discrete choice by...

We consider the regression discontinuity (RD) design with the duration outcome which has discrete support. The parameters of policy interest are treatment effects on unconditional (duration effect) and conditional (hazard effect) exiting probabilities for each discrete level. We find that a...