Applied Econometrics I
Course Description
I offer a modern introduction to empirical strategies in applied micro research in fields such as public policy, development economics, labor economics, education, marketing, and corporate finance. Geared towards first-year Ph.D. students familiar with introductory statistics or econometrics, this course focuses on causal reasoning and design-driven identification in the social sciences, offering practical insights and techniques for real-world applications. Throughout our journey, I will emphasize intuition over extensive formal derivation of theoretical aspects of probability and statistics, focusing instead on the essential foundations for assessing causal relationships. We will explore a range of methods to address causal questions effectively. Our exploration begins with approximating the conditional expectation function using a linear predictor, employing Ordinary Least Squares (OLS). We will delve into the implications of omitted variables and examine the value of research designs that replicate the outcomes of randomized experiments, such as Instrumental Variables (IV) and other quasi-experimental methods. Additionally, we will delve into sampling theory, understanding the differences between design-based and sampling-based uncertainty. Time permitting, we will also explore extensions to high-dimensional, big-data contexts. All concepts will be brought to life with practical applications on real data using statistical software.
Syllabus: Fall 2023
Schedule: Testudo
Webpage: Canvas (UMD students only)
I offer a modern introduction to empirical strategies in applied micro research in fields such as public policy, development economics, labor economics, education, marketing, and corporate finance. Geared towards first-year Ph.D. students familiar with introductory statistics or econometrics, this course focuses on causal reasoning and design-driven identification in the social sciences, offering practical insights and techniques for real-world applications. Throughout our journey, I will emphasize intuition over extensive formal derivation of theoretical aspects of probability and statistics, focusing instead on the essential foundations for assessing causal relationships. We will explore a range of methods to address causal questions effectively. Our exploration begins with approximating the conditional expectation function using a linear predictor, employing Ordinary Least Squares (OLS). We will delve into the implications of omitted variables and examine the value of research designs that replicate the outcomes of randomized experiments, such as Instrumental Variables (IV) and other quasi-experimental methods. Additionally, we will delve into sampling theory, understanding the differences between design-based and sampling-based uncertainty. Time permitting, we will also explore extensions to high-dimensional, big-data contexts. All concepts will be brought to life with practical applications on real data using statistical software.
Syllabus: Fall 2023
Schedule: Testudo
Webpage: Canvas (UMD students only)
Course Contents
Part I. Economic Reasoning, Statistical Learning and Modern Micro-Econometrics
Slides for lectures (revised in August 2023).
Most of the terminology and concepts discussed in this part can be found in the following references:
Most of the terminology and concepts discussed in this part can be found in the following references:
- Efron, Bradley, and Trevor Hastie (2016). Computer Age Statistical Inference: Algorithms, Evidence and Data Science, Cambridge University Press. [Chapter 1]
- Manski, Charles F. (2000). Analog Estimation Methods in Econometrics, Chapman & Hall. [Chapter 1]
- Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf (2017). Elements of Causal Inference: Foundations and Learning Algorithms, MIT Press. [Chapter 1]
- Andrews, Isaiah, and Maximilian Kasy (2019). Identification of and Correction for Publication Bias, American Economic Review, 109(8), pp. 2766-94.
- Angrist, Joshua D., Pierre Azoulay, Glenn Ellison, Ryan Hill, and Lu Susan Feng (2017). Economic Research Evolves: Fields and Styles, American Economic Review, 107(5), pp. 293-97.
- Biasi, Barbara, and Song Ma (2022). The Education-Innovation Gap. National Bureau of Economic Research Working Paper 29853.
- Brodeur, Abel, Mathias Le, Marc Sangnier, and Yanos Zylberberg (2016). Star Wars: The Empirics Strike Back, American Economic Journal: Applied Economics, 8(1), pp. 1-32.
- Chetty, Raj, Nathaniel Hendren, Maggie R. Jones, and Sonya Porter (2020). Race and Economic Opportunity in the United States: An Intergenerational Perspective. Quarterly Journal of Economics, 135(2), pp. 711-783.
- Currie, Janet, Henrik Kleven, and Esmée Zwiers (2020). Technology and Big Data Are Changing Economics: Mining Text to Track Methods, AEA Papers and Proceedings, 110, pp. 42-48.
- Kleven, Henrik J. (2018). Language Trends in Public Economics. Unpublished manuscript, Princeton University.
- Lewbel, Arthur (2019). The Identification Zoo: Meanings of Identification in Econometrics. Journal of Economic Literature, 57(4), pp. 835-903.
- Manski, Charles F. (1993). Identification Problems in the Social Sciences. Sociological Methodology, 23, pp. 1-56.
- Meyer, Bruce D., and James X. Sullivan (2012). Identifying the Disadvantaged: Official Poverty, Consumption Poverty, and the New Supplemental Poverty Measure. Journal of Economic Perspectives, 26(3), pp. 111-36.
- Tamer, Elie (2019). The ET Interview: Professor Charles Manski. Econometric Theory, 35(2), pp. 233-294.
- Goldstein, Markus (2015). The infinite loop failure of replication in economics. World Bank Blogs.
Part II. Policy Relevant Probability and Causal Estimands
Slides for lectures (revised in August 2023).
Most of the terminology and concepts discussed in this part can be found in the following references:
Most of the terminology and concepts discussed in this part can be found in the following references:
- Angrist Joshua D., and Pischke Jorn-Steffen (2009). Mostly Harmless Econometrics. An Empiricist’s Companion, Princeton University Press. [Chapters 1 and 2]
- Rohatgi, Vijay K., and A.K. Md. Ehsanes Saleh (2015). An Introduction to Probability and Statistics (3rd Edition), Wiley. [Chapter 1]
- Wooldridge, Jeffrey M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd Edition), MIT Press. [Chapters 1 and 2]
- Abadie, Alberto, Susan Athey, Guido W. Imbens, and Jeffrey M. Wooldridge (2020). Sampling-Based versus Design-Based Uncertainty in Regression Analysis. Econometrica, Volume 88(1), pp. 265-296.
- Abdulkadiroglu, Atila, Parag A. Pathak, Jonathan Schellenberg, and Christopher R. Walters (2020). Do Parents Value School Effectiveness?. American Economic Review, 110(5), pp 1502-39.
- Battistin, Erich, Richard Blundell, and Arthur Lewbel (2009). Why Is Consumption More Log Normal than Income? Gibrat's Law Revisited. Journal of Political Economy, 117(6), pp. 1140-154.
- Battistin, Erich, and Lorenzo Neri (2024). School Performance, Score Inflation and Neighborhood Development. Forthcoming in the Journal of Labor Economics.
- Chetty, Raj, John N. Friedman, Nathaniel Hilger, Emmanuel Saez, Diane Whitmore Schanzenbach, and Danny Yagan (2011). How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star. The Quarterly Journal of Economics, 126(4), pp. 1593-1660.
- Efron, Bradley (2011). Tweedie's Formula and Selection Bias. Journal of the American Statistical Association, 106, no. 496, pp. 1602-614.
- Heckman, James J., Jeffrey Smith, and Nancy Clements (1997). Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts. The Review of Economic Studies, 64(4), pp. 487-535.
- Krueger, Alan B. (1999). Experimental Estimates of Education Production Functions. The Quarterly Journal of Economics, 114(2), pp. 497-532.
- Stantcheva, Stefanie (2022). How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing the Invisible. NBER Working Paper 30527.
- Kondylis, Florence, and John Loeser (2020). Back-of-the-envelope power calcs. World Bank Blogs.
- SI 2022 Methods Lectures - Empirical Bayes Methods, Theory and Application.
Part III. Linear Regression with Exogenous "Treatment"
Slides for lectures (revised in September 2023).
Most of the terminology and concepts discussed in this part can be found in the following references:
Most of the terminology and concepts discussed in this part can be found in the following references:
- Efron, Bradley (2010). Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press. [Chapter 6].
- Efron, Bradley, and Trevor Hastie (2016). Computer Age Statistical Inference: Algorithms, Evidence and Data Science, Cambridge University Press. [Chapters 10 and 11]
- Rohatgi, Vijay K., and A.K. Md. Ehsanes Saleh (2015). An Introduction to Probability and Statistics (3rd Edition), Wiley. [Chapter 7]
- Wooldridge, Jeffrey M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd Edition), MIT Press. [Chapters 3 and 4]
- Abadie, Alberto (2020). Statistical Nonsignificance in Empirical Economics. American Economic Review: Insights, 2(2), pp. 193-208.
- Abadie, Alberto, Susan Athey, Guido W. Imbens, and Jeffrey M. Wooldridge (2022). When Should You Adjust Standard Errors for Clustering?, forthcoming in the Quarterly Journal of Economics.
- Angrist, Joshua D., Erich Battistin, and Daniela Vuri (2017). In a Small Moment: Class Size and Moral Hazard in the Italian Mezzogiorno. American Economic Journal: Applied Economics, 9(4), pp. 216-49.
- Goldsmith-Pinkham, Paul, Hull, Peter, and Kolesár, Michal (2022). Contamination Bias in Linear Regressions. NBER Working Paper No. 30108.
- Lewbel, Arthur, Yingying Dong, and Thomas Tao Yang (2012). Comparing features of convenient estimators for binary choice models with endogenous regressors. The Canadian Journal of Economics / Revue Canadienne d'Economique,
45(3), pp. 809-829.
- Friedman, Jed (2012). Whether to probit or to probe it: in defense of the Linear Probability Model. World Bank Blogs.
- Imbens, G.W. (2021). Clustering Adjustments to Standard Errors. Chamberlain Seminar.
- McKenzie, David (2017). Finally, a way to do easy randomization inference in Stata! World Bank Blogs.
- McKenzie, David (2017). When should you cluster standard errors? New wisdom from the econometrics oracle. World Bank Blogs.
- McKenzie, David (2020). An overview of multiple hypothesis testing commands in Stata. World Bank Blogs.
Part IV. Endogenous "Treatment" and Instrumental Variation
Slides for lectures (revised in November 2023).
Most of the terminology and concepts discussed in this part can be found in the following references:
Most of the terminology and concepts discussed in this part can be found in the following references:
- Angrist, Joshua D., and Alan B. Krueger (1991). Does Compulsory School Attendance Affect Schooling and Earnings? The Quarterly Journal of Economics, 106(4), pp. 979-1014.
- Angrist, Joshua D., Victor Lavy, Jetson Leder-Luis, and Adi Shany (2019). Maimonides' Rule Redux. American Economic Review: Insights, 1 (3): 309-24.
- Imbens, Guido W. 2020. Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics. Journal of Economic Literature, 58 (4): 1129-79.
- Mogstad, Magne, and Alexander Torgovitsky (2018). Identification and Extrapolation of Causal Effects with Instrumental Variables. Annual Review of Economics, 10(1), pp. 577-613.
- Mogstad, Magne, Alexander Torgovitsky, and Christopher R. Walters (2021). The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables. American Economic Review, 111(11), pp. 3663-98.
- Cunningham, S. (2021). Directed Acyclic Graphs. Causal Inference: The Mixtape.
PART V. There is no such thing as theory free learning
Slides for lectures are under revision.
Most of the terminology and concepts discussed in this part can be found in the following references:
Most of the terminology and concepts discussed in this part can be found in the following references:
- Alberto Abadie, and Maximilian Kasy (2019). Choosing among Regularized Estimators in Empirical Economics: The Risk of Machine Learning. The Review of Economics and Statistics, 101(5), pp. 743–762.
- Angrist, Joshua D., and Brigham Frandsen (2019). Machine Labor. NBER Working Paper No. 26584.
- Athey, Susan, and Guido W. Imbens (2019). Machine Learning Methods That Economists Should Know About. Annual Review of Economics, 11(1), pp. 685-725.
- Hastie, Trevor, Robert Tibshirani, and Martin Wainwright (2015). “Statistical Learning with Sparsity: The Lasso and Generalizations”, Chapman and Hall/CRC. [Chapters 1, 2 and 4].
- Mullainathan, Sendhil, and Jann Spiess (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), pp. 87-106.