This course will introduce students to an applied, intermediate level of quantitative and econometric analysis focused on practical applications that are relevant in fields such as economics, finance, public policy, business, and marketing. This course will focus on applied regression analysis and is intended to give students hands-on experience with real data and real analysis. The course will help you become both a sophisticated consumer of relatively advanced statistical techniques and a better practitioner in conducting your own empirical analyses. By learning econometric methods and applications, students will develop the capacity to build the kind of predictive models that enhance decision making when faced with uncertainty in real world contexts. These tools and skills will also enable students to perform analyses that, under some circumstances, allow us to make valid causal inferences about the effect of a program or intervention on an outcome of interest. The course begins with a recap of simple and multiple linear regression, and then moves to techniques for analyzing real-world quantitative data: incorporating variables in regression analysis that are categorical as well as quantitative, and considering the interactions between independent variables. We will consider model specification in practice—how to choose our independent variables, and how to model the correct functional form. Students will learn how to model nonlinear functional relationships using OLS through transformations of the data. We consider important assumptions that must be fulfilled in order that we obtain credible estimates of our predictors of interest, how to diagnose departures from these assumptions, and practical correction strategies. We follow this with select topics of special interest including modeling binary dependent variables, and the analysis of pooled-cross sectional and panel data. Lectures will include examples in STATA format, a widely used statistical package in the social sciences and business programs. All course exercises, however, will be designed and presented in both STATA and R. Each lesson will include an instructional component and an exercise to give you an opportunity to apply the methods and techniques using actual data. Basic instruction (i.e., sample syntax) will be provided in both STATA and R for all exercises.
What am I going to get from this course?
- What is the impact of non-traditional factors in predicting credit worthiness?
- What is the effect of a country's resource abundance in promoting economic growth?
- What are the key financial determinants of loan application approval, all else being equal?
- What is the impact of air pollution levels on median neighborhood housing prices?
- What is the estimated gender-wage gap, all else being equal (and how does the wage gap vary by level of education)?