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Teaching/Econometrics/Readings
Econometrics · Course 01

Readings

Theory, estimation, identification and applications — from projection geometry to causal research design.

Readings are listed by session. Core textbooks for the whole course are listed at the end.

Lecture 1: From Probability to the Linear Regression Model

Required: Hansen, Econometrics (2022), chapters 2–4; Hayashi, Econometrics, chapter 1. Further: Angrist and Pischke, Mostly Harmless Econometrics, chapter 3.1 (regression and the CEF); Davidson and MacKinnon, Econometric Theory and Methods, chapter 2 (geometry of least squares); Frisch and Waugh (1933), "Partial time regressions as compared with individual trends", Econometrica 1(4); Lovell (1963), "Seasonal adjustment of economic time series and multiple regression analysis", JASA 58(304).

Lecture 2: OLS Inference: Sampling Distributions, Hypothesis Tests, and Confidence Intervals

Required: Hansen, Econometrics, chapters 5–7 and 9; Hayashi, Econometrics, sections 1.4 and 2.1–2.6. Further: White (1980), "A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity", Econometrica 48(4); Engle (1984), "Wald, likelihood ratio, and Lagrange multiplier tests in econometrics", Handbook of Econometrics vol. 2; Wasserstein and Lazar (2016), "The ASA statement on p-values", The American Statistician 70(2).

Lecture 3: Functional Form, Partial Effects, and Omitted-Variable Bias

Required: Wooldridge, Introductory Econometrics, chapters 3.3, 6 and 7; Angrist and Pischke, Mostly Harmless Econometrics, chapter 3.2. Further: Card (1999), "The causal effect of education on earnings", Handbook of Labor Economics vol. 3A; Santos Silva and Tenreyro (2006), "The log of gravity", Review of Economics and Statistics 88(4); Oster (2019), "Unobservable selection and coefficient stability", Journal of Business & Economic Statistics 37(2); Ramsey (1969), "Tests for specification errors in classical linear least-squares regression analysis", JRSS-B 31(2).

Lecture 4: Heteroskedasticity, Dependence, and Robust Inference

Required: Cameron and Miller (2015), "A practitioner's guide to cluster-robust inference", Journal of Human Resources 50(2); Hansen, Econometrics, chapters 4.14–4.23 and 7. Further: White (1980), Econometrica 48(4); Long and Ervin (2000), "Using heteroscedasticity consistent standard errors in the linear regression model", The American Statistician 54(3); Moulton (1990), "An illustration of a pitfall in estimating the effects of aggregate variables on micro units", REStat 72(2); Bertrand, Duflo and Mullainathan (2004), "How much should we trust differences-in-differences estimates?", QJE 119(1); Newey and West (1987), Econometrica 55(3); Lazarus, Lewis, Stock and Watson (2018), "HAR inference: recommendations for practice", JBES 36(4); Abadie, Athey, Imbens and Wooldridge (2023), "When should you adjust standard errors for clustering?", QJE 138(1).

Lecture 5: Endogeneity, Instrumental Variables, and Two-Stage Least Squares

Required: Angrist and Pischke, Mostly Harmless Econometrics, chapter 4; Hansen, Econometrics, chapter 12. Further: Imbens and Angrist (1994), "Identification and estimation of local average treatment effects", Econometrica 62(2); Angrist (1990), "Lifetime earnings and the Vietnam era draft lottery", AER 80(3); Bound, Jaeger and Baker (1995), JASA 90(430); Staiger and Stock (1997), Econometrica 65(3); Stock and Yogo (2005), in Identification and Inference for Econometric Models; Montiel Olea and Pflueger (2013), JBES 31(3); Andrews, Stock and Sun (2019), "Weak instruments in IV regression: theory and practice", Annual Review of Economics 11; Lee, McCrary, Moreira and Porter (2022), "Valid t-ratio inference for IV", AER 112(10).

Lecture 6: Simultaneous Equations and Structural Identification

Required: Wooldridge, Econometric Analysis of Cross Section and Panel Data, chapter 9; Hayashi, Econometrics, chapter 4. Further: Haavelmo (1943), "The statistical implications of a system of simultaneous equations", Econometrica 11(1); Graddy (1995), "Testing for imperfect competition at the Fulton fish market", RAND Journal of Economics 26(1); Angrist, Graddy and Imbens (2000), Review of Economic Studies 67(3); Rigobon (2003), "Identification through heteroskedasticity", REStat 85(4); Matzkin (2013), "Nonparametric identification in structural economic models", Annual Review of Economics 5.

Lecture 7: Panel Data: Fixed Effects, Random Effects, and Correlated Effects

Required: Wooldridge, Econometric Analysis of Cross Section and Panel Data, chapter 10; Angrist and Pischke, Mostly Harmless Econometrics, chapter 5.1. Further: Mundlak (1978), "On the pooling of time series and cross section data", Econometrica 46(1); Hausman and Taylor (1981), Econometrica 49(6); Griliches and Hausman (1986), "Errors in variables in panel data", Journal of Econometrics 31(1); Arellano (1987), "Computing robust standard errors for within-groups estimators", Oxford Bulletin 49(4); Freeman (1984), "Longitudinal analyses of the effects of trade unions", Journal of Labor Economics 2(1).

Lecture 8: Dynamic Panels, Difference GMM, and System GMM

Required: Arellano, Panel Data Econometrics (2003), chapters 6–8; Roodman (2009), "How to do xtabond2: an introduction to difference and system GMM in Stata", Stata Journal 9(1). Further: Nickell (1981), "Biases in dynamic models with fixed effects", Econometrica 49(6); Hansen (1982), "Large sample properties of generalized method of moments estimators", Econometrica 50(4); Anderson and Hsiao (1981), JASA 76(375); Arellano and Bond (1991), Review of Economic Studies 58(2); Blundell and Bond (1998), Journal of Econometrics 87(1); Windmeijer (2005), Journal of Econometrics 126(1); Roodman (2009), "A note on the theme of too many instruments", Oxford Bulletin 71(1); Bond, Hoeffler and Temple (2001), "GMM estimation of empirical growth models", CEPR DP 3048.

Lecture 9: Time-Series Foundations: Stationarity, ARMA, and Forecasting

Required: Hamilton, Time Series Analysis, chapters 3–4; Stock and Watson, Introduction to Econometrics, chapter 15. Further: Box, Jenkins, Reinsel and Ljung, Time Series Analysis: Forecasting and Control; Diebold and Mariano (1995), "Comparing predictive accuracy", JBES 13(3); Clark and West (2007), Journal of Econometrics 138(1); Stock and Watson (2007), "Why has U.S. inflation become harder to forecast?", Journal of Money, Credit and Banking 39(s1).

Lecture 10: Unit Roots, Structural Breaks, and Persistence

Required: Hamilton, Time Series Analysis, chapters 15–17; Stock and Watson, Introduction to Econometrics, chapter 15.6–15.7. Further: Dickey and Fuller (1979), JASA 74(366); Nelson and Plosser (1982), "Trends and random walks in macroeconomic time series", Journal of Monetary Economics 10(2); Perron (1989), "The great crash, the oil price shock, and the unit root hypothesis", Econometrica 57(6); Elliott, Rothenberg and Stock (1996), Econometrica 64(4); Kwiatkowski, Phillips, Schmidt and Shin (1992), Journal of Econometrics 54(1–3); Andrews (1993), "Tests for parameter instability and structural change with unknown change point", Econometrica 61(4); Bai and Perron (2003), Journal of Applied Econometrics 18(1); Zivot and Andrews (1992), JBES 10(3).

Lecture 11: VAR and Structural VAR: Dynamic Interactions and Shock Identification

Required: Kilian and Lütkepohl, Structural Vector Autoregressive Analysis (2017), chapters 2, 8–9 and 12–13; Stock and Watson (2016), "Dynamic factor models, factor-augmented VARs, and SVARs in macroeconomics", Handbook of Macroeconomics vol. 2. Further: Sims (1980), "Macroeconomics and reality", Econometrica 48(1); Blanchard and Quah (1989), AER 79(4); Christiano, Eichenbaum and Evans (1999), Handbook of Macroeconomics vol. 1A; Uhlig (2005), Journal of Monetary Economics 52(2); Baumeister and Hamilton (2015), Econometrica 83(5); Mertens and Ravn (2013), AER 103(4); Gertler and Karadi (2015), AEJ: Macro 7(1); Jordà (2005), AER 95(1); Plagborg-Møller and Wolf (2021), "Local projections and VARs estimate the same impulse responses", Econometrica 89(2).

Lecture 12: Cointegration, VECM, and ARDL Models

Required: Hamilton, Time Series Analysis, chapters 19–20; Juselius, The Cointegrated VAR Model (2006), chapters 1–7. Further: Engle and Granger (1987), "Co-integration and error correction: representation, estimation, and testing", Econometrica 55(2); Johansen (1991), Econometrica 59(6); Stock and Watson (1993), "A simple estimator of cointegrating vectors in higher order integrated systems", Econometrica 61(4); Phillips and Hansen (1990), Review of Economic Studies 57(1); Pesaran, Shin and Smith (2001), "Bounds testing approaches to the analysis of level relationships", Journal of Applied Econometrics 16(3); Sims, Stock and Watson (1990), Econometrica 58(1); Toda and Yamamoto (1995), Journal of Econometrics 66(1–2).

Lecture 13: Causal Inference: Potential Outcomes, DiD, and Regression Discontinuity

Required: Angrist and Pischke, Mostly Harmless Econometrics, chapters 2, 3.2–3.3, 5 and 6; Cunningham, Causal Inference: The Mixtape (2021), chapters 4–6 and 9–10. Further: Imbens and Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences (2015); Card and Krueger (1994), AER 84(4); Goodman-Bacon (2021), Journal of Econometrics 225(2); Callaway and Sant'Anna (2021), Journal of Econometrics 225(2); de Chaisemartin and D'Haultfœuille (2020), AER 110(9); Roth, Sant'Anna, Bilinski and Poe (2023), "What's trending in difference-in-differences?", Journal of Econometrics 235(2); Abadie, Diamond and Hainmueller (2010), JASA 105(490); Calonico, Cattaneo and Titiunik (2014), Econometrica 82(6); Lee and Lemieux (2010), "Regression discontinuity designs in economics", Journal of Economic Literature 48(2); Chernozhukov et al. (2018), "Double/debiased machine learning", Econometrics Journal 21(1).

Lecture 14: Research Design, Monte Carlo, Replication, and Reporting

Required: Christensen, Freese and Miguel, Transparent and Reproducible Social Science Research (2019), chapters 4–7 and 11; the Social Science Data Editors' template README and the AEA Data and Code Availability Policy. Further: Cameron and Trivedi, Microeconometrics (2005), chapter 11 (bootstrap); Horowitz (2001), "The bootstrap", Handbook of Econometrics vol. 5; MacKinnon (2009), "Bootstrap hypothesis testing", in Handbook of Computational Econometrics; Romano and Wolf (2005), Econometrica 73(4); Benjamini and Hochberg (1995), JRSS-B 57(1); Anderson (2008), JASA 103(484); Olken (2015), "Promises and perils of pre-analysis plans", Journal of Economic Perspectives 29(3); Brodeur, Cook and Heyes (2020), "Methods matter: p-hacking and publication bias in causal analysis in economics", AER 110(11).

Core textbooks

  • Hayashi, Econometrics.
  • Wooldridge, Econometric Analysis of Cross Section and Panel Data.
  • Greene, Econometric Analysis.
  • Hamilton, Time Series Analysis.
  • Lütkepohl, New Introduction to Multiple Time Series Analysis.
  • Angrist and Pischke, Mostly Harmless Econometrics.

Recommended research papers

  • White (1980), heteroskedasticity-consistent covariance estimation.
  • Arellano and Bond (1991), dynamic panel estimation.
  • Blundell and Bond (1998), system GMM.
  • Engle and Granger (1987), cointegration and error correction.
  • Johansen (1991), likelihood-based cointegration analysis.
  • Pesaran, Shin, and Smith (2001), bounds testing.
  • Callaway and Sant’Anna (2021), difference-in-differences with multiple time periods.