Endogeneity is a primary concern when evaluating causal effects using observational panel data. While unit-specific intercepts control for unobserved time-invariant confounders, dependence between (i) regressors (e.g., marketing mix strategy of interests) and the current error term (regressor...

"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...

Given the ubiquitous presence of endogenous regressors and the challenges in finding good instruments to overcome the endogeneity problem, a forefront of recent research is the development and application of endogeneity correction methods without requiring instruments. In this article, we...

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...