"In missing data analysis, there is often a need to assess the sensitivity of key inferences to departures from untestable assumptions regarding the missing data process. Such sensitivity analysis often requires specifying a missing data model which commonly assumes parametric functional forms...

A prominent challenge when drawing causal inference using observational data is the ubiquitous presence of endogenous regressors. The classical econometric method to handle regressor endogeneity requires instrumental variables that must satisfy the stringent condition of exclusion restriction,...

Unlike random sampling, selective sampling draws units based on the outcome values, such as over-sampling rare events in choice outcomes and extreme activities on continuous and count outcomes. Despite high cost effectiveness for marketing research, such endogenously selected samples must be...

Causal inference is of central interests in many empirical applications yet often challenging because of the presence of endogenous regressors. The classical approach to the problem requires using instrumental variables that must satisfy the stringent condition of exclusion restriction. At the...

"Research on the effects of patent protection on innovation and technology transfer in the cross-country pharmaceutical industry adds to our understanding of the underlying forces driving a country's innovation level. Qian (2007) constructs a comprehensive database useful for evaluating the...

"This paper combines a natural policy experiment and randomized lab experiments to estimate the differential impacts of counterfeiting on the sales and purchase intent of branded products of various quality levels. I collect new product-line level panel data from Chinese shoe companies from...