Schedule

This schedule is subject to change due needs of the students in the course. Students will be informed of changes to the schedule.

Week 1

Mar 26 (Monday)

Topics: Syllabus; Introduction

Readings

None

Mar 28 (Wednesday)

Topics: Conditional expectation functions (CEF); Bivariate OLS; Estimator properties

Readings

Real Stats, “Preface”
Real Stats, Ch. 1: “Quest for Causality”
Real Stats, Ch. 2: “Stats in the Wild: Good Data Practices”
Real Stats, Ch. 2: “Bivariate OLS: The Foundation of Statistical Analysis”
Real Stats, Appendix: “Math and Probability Background”

Mar 30 (Friday)

Readings

None

Week 2

Apr 2 (Monday)

Topics: hypothesis testing; confidence intervals; p-values; power

Readings

Real Stats, Ch. 4: “Hypothesis Testing and Interval estimation: Answering Research Questions”
Wasserstein, Ronald L., and Nicole A. Lazar (2016) “The ASA’s Statement on p-Values: Context, Process, and Purpose”, American Statistician DOI:10.1080/00031305.2016.1154108
Magnusson, Kristoffer. “Understanding Statistical Power and Significance Testing an interactive visualization“

Apr 4 (Wednesday)

Topics:

Readings

Aschwanden, Christie (2015) “Science Isn’t Broken”, fivethirtyeight.com.
Tversky, Amos, and Daniel Kahneman (1971) “Belief in the Law of Small Numbers” Psychological Bulletin DOI:10.1037/h0031322
Hoekstra, Rink, Richard D. Morey, Jeffrey N. Rouder, and Eric-Jan Wagenmakers (2014) “Robust misinterpretation of confidence intervals” Psychonomic Bulletin Review DOI:10.3758/s13423-013-0572-3
Gelman, Andrew, and Hal Stern. (2006) “The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant”, American Statistician DOI:10.1198/000313006X152649
McCaskey, Kelly, and Carlisle Rainey (2015) “Substantive Importance and the Veil of Statistical Significance”, Statistics, Politics, and Policy DOI:10.1515/spp-2015-0001
Rainey, Carlisle (2014) “Arguing for a Negligible Effect”, American Journal of Political Science DOI:10.1111/ajps.12102

Optional

Gelman, Andrew. (2009) McCloskey et al. on significance testing in economics

Apr 6 (Friday)

Topics:

Readings

None

Week 3

Due

Assignment 1 Apr 13, 1:30 PM

Apr 9 (Monday)

Topics:

Readings

Real Stats, Ch. 5: “Multivariate OLS: Where the Action Is”

Apr 11 (Wednesday)

Topics:

Readings

Harvard University, Math (P)refresher for Political Scientists, “Linear Algebra”.
Real Stats, Ch. 6: “Dummy Variables: Smarter Than Your Think”
Real Stats, Ch. 12. “Dummy Dependent Variables”
Golder, Matt. “Interactions”.
Pischke (2012) “Probit better than LPM?”, mostlyharmlesseconometrics.com.
Moore and Siegal (2013) “A Mathematics Course for Political and Social Research”

Apr 13 (Friday)

Topics:

Readings

None

Week 4

Topics: Prediction; Model Fit; Cross Validation

Apr 16 (Monday)

Topics:

Readings

Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer (2015) “Prediction Policy Problems” DOI:10.1257/aer.p20151023
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2013) An Introduction to Statistical Learning with Applications in R, Ch 5. “Resampling Methods”
Hesterberg, Tim (2015) “What Teachers Should Know about the Resampling in the Undergraduate Statistics Curriculum“

Optional

Shalizi, Cosma. “F-Tests, R2, and Other Distractions“
Mullainathan, Sendhil, and Jann Spiess (2017) “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives DOI:10.1257/jep.31.2.87
Shmueli, Galit (2010) “To Explain or to Predict?” Statistical Science DOI:10.1214/10-STS330
Resnick, Brian (2017) “What a nerdy debate about p-values shows about science — and how to fix it”, vox.com.
Geoff Cumming (2014) “The New Statistics: Why and How”, Psychological Science DOI:10.1177/0956797613504966

Apr 18 (Wednesday)

Topics:

Readings

None

Apr 20 (Friday)

Topics:

Readings

None

Week 5

Topics: causal inference; potential outcomes; directed acyclic graphs; experiments

Due

Project Proposal Apr 25, 4:30 PM

Apr 23 (Monday)

Topics:

Readings

Mastering ‘Metrics, Ch. 1: “Randomized Trials”
EGAP “10 Things to Know About Causal Inference“
EGAP “10 Strategies for Figuring out if X Caused Y“

Optional

EGAP “10 Types of Treatment Effect You Should Know About“
EGAP “10 Things to Know About the Local Average Treatment Effect“

Apr 25 (Wednesday)

Topics:

Readings

Gelman and Hill (2007) “Causal inference using more advanced models”, Chapter 10 in Data Analysis Using Regression and Multilvel/Hierarchical Models.
Keele (2015) “The Statistics of Causal Inference: A View from Political Methodology” Political Analysis.
Rohrer (2018) “Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data” Advances in Methods and Practices in Psychological Science

Apr 27 (Friday)

Topics:

Readings

None

Week 6

Topics: selection on observables; conditional independence assumption

Due

Assignment 2 Apr 30, 4:30 PM

Apr 30 (Monday)

Topics:

Readings

Mastering ‘Metrics, Ch. 2: “Regression”
Mastering ‘Metrics, Ch. 6.1: “Schooling, Experience, and Earnings”
Gelman and Hill (2007) “Causal inference using more advanced models”, Chapter 10 in Data Analysis Using Regression and Multilvel/Hierarchical Models.

May 2 (Wednesday)

Readings

Nunn and Wantchekon (2011) “The Slave Trade and the Origins of Mistrust in Africa” American Economic Review.
Matching as a Regression Estimator
Aronow and Samii (2015) “Does Regression Produce Representative Estimates of Causal Effects?” American Journal of Political Science
Keele, Elwert, Stevenson (2015) “The Perils of the All Cause Model“
Iacus, King, and Porro (2012) “Causal Inference Without Balance Checking: Coarsened Exact Matching.” Political Analysis
Gary King and Langche Zeng. 2006. “The Dangers of Extreme Counterfactuals” Political Analysis
Ho, Imai, King, and Stuart. 2007. “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference” Political Analysis
Imai, Keele, Tingley, and Yamamoto. (2011). “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies” American Political Science Review.

May 4 (Friday)

Topics:

Readings

None

Week 7

Topics: panel data; difference-in-difference

Due

Assignment 3 May 7, 4:30 PM

May 7 (Monday)

Topics:

Readings

Mastering ‘Metrics, Ch. 3: “Difference-in-Differences”
Real Stats, Ch. 8. “Using Fixed Effects Models to Fight Endogeneity”

Optional

Cameron, A. Colin, and Douglas L. Miller (2015) “A Practitioner’s Guide to Cluster-Robust Inference” Journal of Human Resources DOI:10.3368/jhr.50.2.317
Clark, Michael “Clustered Data“
Real Stats, Ch. 15. “Advanced Panel Data”

May 9 (Wednesday)

Topics:

Readings

None

May 11 (Friday)

Topics:

Readings

None

Week 8

Topics: regression discontinuity

May 14 (Monday)

Topics:

Readings

Mastering ‘Metrics, Ch. 3: “Instrumental Variables”
Mastering ‘Metrics, Ch. 6.4: “Rustling Sheepskin in the Lone Star State”
Real Stats, Ch. 11. Regression Discontinuity: Looking for Jumps in Data

Optional

“Lee, David S., and Thomas Lemieux (2010) “Regression Discontinuity Designs in Economics”, Journal of Economic Literature DOI:10.1257/jel.48.2.281“
“Jacob, Robin, and Pei Zhu (2012) “A Practical Guide to Regression Discontinuity” URL“

May 16 (Wednesday)

Topics:

Readings

None

May 18 (Friday)

Topics:

Readings

None

Week 9

Topics: instrumental variables

Due

Assignment 4 May 14, 4:30 PM

May 21 (Monday)

Topics:

Readings

Mastering ‘Metrics, Ch. 3 “Instrumental Variables”
Mastering ‘Metrics, Ch. 6.2 “Twins Double the Fun”
Mastering ‘Metrics, Ch. 6.3 “Econometricians Are Known by Their … Instruments”
Real Stats, Ch. 9 “Instrumental Variables: Using Exogenous Variation to Fight Endogeneity”

Optional

Green, Donald P., and Allison J. Sovey. “Instrumental Variables Estimation in Political Science: A Readers’ Guide” DOI:10.1111/j.1540-5907.2010.00477.x
Kenneth A. Bollen (2012) “Instrumental Variables in Sociology and the Social Sciences” DOI:10.1146/annurev-soc-081309-150141 Annual Review of Sociology

May 23 (Wednesday)

Topics:

Readings

None

May 25 (Friday)

Topics:

Readings

None

Week 10

Due

May 28, 4:30 PM
Jun 1, 1:30 PM

May 28 (Monday)

Topics:

Readings

None

May 30 (Wednesday)

Topics:

Readings

None

Jun 1 (Friday)

Topics:

Readings

None

Week 11

Due

Jun 7, 11:59 PM