Spring 2016 - 60544 - PA397C - Advanced Empirical Methods for Policy Analysis

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.


Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach (Cengage, 2014).

Peter Kennedy, A Guide to Econometrics (MIT Press, 2008) (recommended).


Stata, available from www.stata.com. Buy Stata/IC 14. A perpetual license will cost $198; six-month and annual licenses are also available.

SRH 3.B7