Statistical Modeling for Policy Analysis
Statistics is easy. Good statistics – the kind you’d be comfortable basing public policy on – takes a little more work. This class focuses on the intuition underlying statistical technique, the art of learning from others’ mistakes, and the practical challenges of estimating, trouble-shooting, comparing, and interpreting models using real data. Students will estimate statistical models and report on their results throughout the course; they will also compile data, estimate and fine-tune a model, and draw policy implications for a final paper and presentation on a policy issue of their choice.
Specific topics will include the following:
The role of uncertainty and modeling in policy choice;
The classic ordinary least squares (OLS) model;
Specification issues, including omitted variable bias, heteroskedasticity and autocorrelation, variable selection, and functional form;
Causality, endogeneity, and instrumental variables;
Research design, including experiments and quasi-experiments; and
Combining and incorporating results from previous studies.
We will also consider some of these (somewhat more advanced and specialized) subjects:
Limited dependent variables, including Poisson, binary and multiple response, Tobit, and survival analysis;
Time-series (ARIMA) and forecasting models;
Multivariate time-series, the error-correction model, and cointegration;
Panel data and hierarchical linear models;
Matching, regression discontinuity, and interrupted time-series designs; and
Robust alternatives to OLS.
Textbooks
Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach (Cengage, 2014).
Peter Kennedy, A Guide to Econometrics (MIT Press, 2008) (recommended).
Software
Stata, available from www.stata.com. Buy Stata/IC 14. A perpetual license will cost $198; six-month and annual licenses are also available.