Syllabus.

Transcription

1 Business Applied Econometrics - Spring 2018 Instructor: Christian Hansen Office: HPC 329 Phone: chansen1@chicagobooth.edu TA: Jianfei Cao jcao0@chicagobooth.edu Syllabus Overview In this course, we will discuss statistical methodology for use in econometric settings. The course will cover topics such as basic treatment effect models, review doing inference with dependent data, outline some common discrete choice models, and introduce nonparametric and high-dimensional estimation. It is assumed that students have a good understanding of linear regression and methods of performing classical inference using asymptotic approximations for linear and nonlinear models. Grades will be determined by homework that may be done in groups (25%), an in-class midterm (25%), and an in-class final (50%). Course Website: Text and Class Notes There is no required text for this course. Some recommended texts are Econometrics by Hayashi, Econometric Analysis of Cross Section and Panel Data by Wooldridge, Mostly Harmless Econometrics by Angrist and Pischke, and An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani. The last book is available as a free downloadable pdf at Office Hours and Review Sessions is the fastest and most reliable way to contact me. I do not have official office hours, but I am happy to meet with anyone who would like to make an appointment with me. Also, feel free to stop by my office (HPC 375) if you have a question.

2 Preliminary Outline 1. Review of Least Squares and the Linear Model Angrist and Pischke, Ch. 3 Hayashi, Ch. 2 Wooldridge, Ch. 4 James, Witten, Hastie, and Tibshirani, Ch. 3 Bilias, Y. (2000), Sequential Testing of Duration Data: The Case of the Pennsylvania Reemployment Bonus Experiment, Journal of Applied Econometrics, 15(6), Meyer, B. (1995), Lessons from the U.S. unemployment insurance experiments, Journal of Economic Literature, 33, Poterba, J. M., S. F. Venti, and D. A. Wise (1995), Do 401(k) contributions crowd out other personal saving?, Journal of Public Economics, 58, Review of Linear Instrumental Variables Angrist and Pischke, Ch. 4 Hayashi, Ch. 3 Wooldridge, Ch. 5 Acemoglu, D., S. Johnson, and J. A. Robinson (2001), The colonial origins of comparative development: An empirical investigation, American Economic Review, 91(5), Anderson, T. W., and H. Rubin (1949), Estimation of the parameters of a single equation in a complete system of stochastic equations, Annals of Mathematical Statistics, 20, Andrews, D. W. K. and X. Cheng (2012), Estimation and inference with weak, semi-strong, and strong identification, Econometrica, 80(5), Chernozhukov, V. and C. Hansen (2008), The reduced form: A simple approach to inference with weak instruments, Economics Letters, 100(1), Conley, T. G., C. Hansen, and P. E. Rossi (2012), Plausibly exogenous, Review of Economics and Statistics, 94(1), Hansen, C., J. Hausman, and W. Newey (2008), Estimation with many instrumental variables, Journal of Business and Economic Statistics, 26(4), Stock, J. H., and J. H. Wright (2000), GMM with weak identification," Econometrica, 68, Stock, J. H., J. H. Wright, and M. Yogo (2002), A survey of weak instruments and weak identification in Generalized Method of Moments," Journal of Business and Economic Statistics, 20(4), Wright, P. G. (1928), The tariff on animal and vegetable oils, New York: Macmillan. 3. Basic Panel Data Methods Angrist and Pischke, Ch. 5 Hayashi, Ch. 5 Wooldridge, Ch Anderson, T. W., and C. Hsiao (1981), Estimation of dynamic models with error components, Journal of the American Statistical Association, 76, Anderson, T. W., and C. Hsiao (1982), Formulation and estimation of dynamic models using panel data, Journal of Econometrics 18, Arellano, M., and S. Bond (1991), Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies, 58, Arellano, M., and Hahn, J., (2005), Understanding Bias in Nonlinear Panel Models: Some Recent Developments, Invited Lecture, Econometric Society World Congress, London.

3 Athey, S. and Imbens (2006), Identification and Inference in Nonlinear Difference-In-Difference Models, Econometrica, 74(2), Bhargava, A., Franzini, L., Narendranathan, W. (1982), Serial correlation and the fixed effects model, Review of Economic Studies 49, Hahn, J., Kuersteiner, G.M. (2002), Asymptotically unbiased inference for a dynamic panel model with fixed effects when both N and T are large, Econometrica 70(4), Jenish, N. and Prucha, I. R., 2009, Central Limit Theorems and Uniform Laws of Large Numbers for Arrays of Random Fields, Journal of Econometrics, 150, Kaestner, R. and Simon, K., (2002), Labor Market Consequences of State Health Insurance Reforms, Industrial and Labor Relations Review, 56(1), Neyman, J. and Scott, E. L. (1948), Consistent Estimates Based on Partially Consistent Observations, Econometrica, 16(1), Nickell, S. (1981), Biases in dynamic models with fixed effects, Econometrica, 49(6), Papke, L. E. (2005), The Effects of Spending on Test Pass Rates: Evidence from Michigan, Journal of Public Economics, 89, Richardson, G. and W. Troost (2009), Monetary intervention mitigated banking panics during the Great Depression: Quasi-experimental evidence from a Federal Reserve District border, , Journal of Political Economy, 117(6), Group-Based Inference Methods Wooldridge, Ch Arellano, M. (1987), Computing robust standard errors for within-group estimators, Oxford Bulletin of Economics and Statistics, 49, Bertrand, M., Duflo, E., and Mullainathan, S. (2004), How Much Should We Trust Differencesin-Differences Estimates? Quarterly Journal of Economics, 119, Bester, C. A., Conley, T., and Hansen C. (2011), Inference for Dependent Data Using Cluster Covariance Estimators, Journal of Econometrics, 165(2), Cameron, A. C., J. B. Gelbach, and D. L. Miller (2011), Robust Inference with Multiway Clustering," Journal of Business and Economic Statistics, 29, Canay, I. A., J. P. Romano, and A. M. Shaikh (2014), Randomization Tests under an Approximate Symmetry Assumption," Technical Report No , Stanford University. Fama, E. F. and J. D. MacBeth (1973), Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, 81, Hansen, C. (2007), Asymptotic Properties of a Robust Variance Matrix Estimator for Panel Data when T is Large, Journal of Econometrics, 141, Ibragimov, R. and U. K. Müller (2010), t-statistic Based Correlation and Heterogeneity Robust Inference," Journal of Business and Economic Statistics, 28, Ibragimov, R. and U. K. Müller (2016), Inference with few heterogeneous clusters, Review of Economics and Statistics, 98, forthcoming. Imbens, G. W. and M. Kolesar (2012), Robust Standard Errors in Small Samples: Some Practical Advice," working paper. Liang, K.-Y. and S. L. Zeger (1986), Longitudinal data analysis using generalized linear models, Biometrika, 73, Introduction to Nonparametrics James et al., Ch. 2, 3, 5, 7, 8.2 Li, Q. and J. S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press. Newey, W. (2007), Convergence rates and asymptotic normality for series estimators, Journal of Econometrics, 79,

4 6. Estimation of Treatment Effects Angrist and Pischke, Ch. 3.2, 3.3, 4.4, 4.5, 6 Wooldridge, Ch. 21 Abadie, A. (2003), Semiparametric instrumental variable estimation of treatment response models, Journal of Econometrics, 113, Abadie, A. and G. W. Imbens (2006), Large sample properties of matching estimators for average treatment effects, Econometrica, 74(1), Abadie, A. and G. W. Imbesn (2008), On the failure of the bootstrap for matching estimators, Econometrica, 74(6), Abadie, A. and G. W. Imbens (2011), Bias-corrected matching estimators for average treatment effects, Journal of Business and Economic Statistics, 29(1), Abrevaya, J., Y.-C. Hsu, and R. P. Lieli (2015), Estimating conditional average treatment effects, Journal of Business and Economic Statistics, 33(4), Angrist, J. D., K. Graddy, and G. W. Imbens (2000), The interpretation of instrumental variables estimators in simultaneous equations models with an application to the demand for fish, Review of Economic Studies, 67, Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996), Identification and causal effects using instrumental variables, Journal of the American Statistical Association, 91, Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2016), Program Evaluation and Causal Inference with High-Dimensional Data, Calonico, S., M. D. Cattaneo, and R. Titiunik (2014), Robust nonparametric confidence intervals for regression-discontinuity designs, Econometrica, 82(6), Cattaneo, M. D., B. Frandsen, and R. Titiunik (2015), Randomization inference in the regression discontinuity design: An application to party advantages in the U.S. Senate, Journal of Causal Inference, 3(1), Fisher, R. A. (1935), Design of Experiments, Oliver and Boyd. Hahn, J. (1998), On the role of the propensity score in efficient semiparametric estimation of average treatment effects, Econometrica, 66, Hirano, K., G. W. Imbens, and G. Ridder (2003), Efficient estimation of average treatment effects using the estimated propensity score, Econometrica, 71(4), Imbens, G. W. (2004), Nonparametric estimation of average treatment effects under exogeneity: A review, Review of Economics and Statistics, 86(1), Imbens, G. W. and J. D. Angrist (1994), Identification and estimation of local average treatment effects, Econometrica, 60, Imbens, G. W. and K. Kalyanaraman (2012), Optimal bandwidth choice for the regression discontinuity estimator, Review of Economic Studies, 79, Imbens, G. W. and T. Lemieux (2008), Regression discontinuity designs: A guide to practice, Journal of Econometrics, 142, Imbens, G. W. and D. B. Rubin (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Cambridge University Press. Li, Q., J. S. Racine, and J. M. Wooldridge (2008), Estimating average treatment effects with continuous and discrete covariates: The case of Swan-Ganz catheterization, American Economic Review, 98, Li, Q., J. S. Racine, and J. M. Wooldridge (2009), Efficient estimation of average treatment effects with mixed categorical and continuous data, Journal of Business and Economic Statistics, 27, Ludwig, J. and D. Miller (2005), Does Head Start improve children s life chances? Evidence from a regression discontinuity design, NBER Working Paper No Neyman, J. (1923, 1990), On the application of probability theory to agricultural experiments. Essay on principles. Section 9, translated in Statistical Science, (with discussion), 5(4), , 1990.

5 Robins, J. M. and A. Rotnitzky (1995), Semiparametric efficiency in multivariate regression models, Journal of the American Statistical Association, 90, Rubin, D. B. (1974), Estimating causal effects of treatments in randomized and non-randomized studies, Journal of Educational Psychology, 66, Introduction to Data Mining: Subset Selection and Penalized Estimation James et al., Ch. 6 Belloni, A. and V. Chernozhukov (2013), Least squares after model selection in highdimensional sparse models, Bernoulli, 19(2), Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2016), Program evaluation and causal inference with high-dimensional data, forthcoming Econometrica. Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012), Sparse models and methods for optimal instruments with an application to eminent domain, Econometrica, 81(2), Belloni, A., V. Chernozhukov, C. Hansen, and D. Kozbur (2016), Inference in high-dimensional panel models with an application to gun control, forthcoming Journal of Business and Economic Statistics. Chen, J., and Z. Chen (2008), Extended Bayesian Information Criterion for Model Selection with Large Model Spaces, Biometrika, 95, Chernozhukov, V., C. Hansen, and M. Spindler (2015), Valid post-selection and postregularization inference: An elementary, general approach, Annual Review of Economics, 7, Chernozhukov, V., C. Hansen, and M. Spindler (2015), Post-selection and post-regularization inference in linear models with very many controls and instruments, American Economic Review: Papers and Proceedings, 105(5), Fan, J. and J. Lv (2008), Sure independence screening for ultrahigh dimensional feature space, Journal of the Royal Statistical Society, Series B, 70(5), Jing, B.-Y., Q.-M. Shao, and Q. Wang (2003), Self-normalized Cramer-type large deviations for independent random variables, Annals of Probability, 31(4), D. Kozbur (2016), Testing-based forward model selection, arxiv: H. Wang (2009), Forward regression for ultra-high dimensional variable screening, Journal of the American Statistical Association, 104,