Book: Wooldridge Jeffrey M., Econometric Analysis of Cross Section and Panel Data
Imbens Guido W. and Jeffrey M. Wooldridge (2009) Recent Developments in the Econometrics of Program Evaluation Journal of Economic Literature 2009, 47:1, 5–86
Abstract: The course will teach basic concepts in econometrics with a focus on the design-based approach. Such designs are sometimes called quasi-experimental and sometimes natural experiments. The course deals with cross sectional and panel data. The design stems from the type of data that the researcher has available, but the estimators will basically always be some form of regression estimator. For this reason, the first part of the course will deal with “what do regression estimators identify?” and in discussing valid inferences to the population under different sampling designs.
Lecture notes on the basis for asymptotic inference (Based on Ch. 3 Wooldrige, 2010 edition)
Angrist, Joshua D. Guido W. Imbens and Donald B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables: Journal of the American Statistical Association, Vol. 91, No. 434 (Jun., 1996), pp.444- 455
Imbens Guido W. (2010) Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) Journal of Economic Literature 48, 399–423
Constantine E. Frangakis Donald B. Rubin (2002) Principal Stratification in Causal Inference. BIOMETRIC 58 , 21-29.
Diamond, Alexis and Sekhon, Jasjeet S (2013) Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies, Review of Economics and Statistics, 95 (3) 932-945.
José R. Zubizarreta (2012). Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery, Journal of the American Statistical Association, 107:500, 1360-1371
Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1):25–46.
Cameron A. C and Douglas L. M (2015). A Practitioner's Guide to Cluster-Robust Inference, Journal of Human Resources 50, 317-372
Athey, S. and Imbens G.W. (2016). The Econometrics of Randomized Experiments, Handbook of Economic Field Experiments, Volume 1 CHAPTER 3
Illustration to be discussed in the course
Hägglund, Pathric Per Johansson and Lisa Laun (2020) “The Impact of CBT on Sick Leave and Health”. Evaluation Review 2020, Vol. 44(2-3) 185-217
Hartman, Laura, Patrik Hesselius and Per Johansson (2013) “Effects of eligibility screening in the sickness insurance: Evidence from a field experiment”. Labour Economics2013, 20, 48-56.
Johansson, Per, Arizo Karimi and Peter Nilsson (2018). “Worker Absenteeism: Peer Influences, Monitoring, and Job Flexibility” J. R. Statist. Soc. A (2018).
Engström, Per , Pathric Hägglund and Per Johansson (2017). “Early Interventions and Disability Insurance: Experience from a Field Experiment. ”The Economic Journal, 2017, 127, 363-392.
Jans, Jenny & Johansson, Per & Nilsson, J. Peter, 2018. "Economic status, air quality, and child health: Evidence from inversion episodes," Journal of Health Economics, 61, pages 220-232.
Hallberg, Daniel, Per Johansson and Malin Josephson (2016). Is an early retirement offer good for your health? Quasi-experimentalevidence from the army Journal of Health Economics, 44 pages 274-285.
Fredriksson, P., Öckert, B., and Oosterbeek, H. (2013). Long-Term Effects of Class Size. The Quarterly Journal of Economics, 128 (1), 249-285.
Angelov, N., Johansson, P., and Lee, M.-j. (2019). Practical causal analysis for the treatment timing effect on doubly censored duration: effect of fertility on work span. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(4):1561–1585.
Lee, D. S. (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects. The Review of Economic Studies, 76(3):1071–1102.
Becker, S. O. and Caliendo, M. (2007). Sensitivity analysis for average treatment effects. The Stata Journal, 7(1):71–83.
Per Johansson: email@example.com or firstname.lastname@example.org