Econ 715: Econometric Theory I, Fall 2015

 

Course Time: Tuesdays and Thursdays, 9:30 – 10:45.   Social Science 4308

 

Instructor:  Bruce Hansen, 6438 Social Science.

Email: bruce.hansen@wisc.edu

Classlist: econ715-1-f15@lists.wisc.edu

Office Hours: Tuesdays 1:30-3:30, or by appointment.

 

The course prerequisite is Econ 710 or consent of instructor.

 

This course is intended for second-year PhD students in economics who are either taking the econometrics field or who want to deepen their understanding of econometric methods. While Econ 710 focused primarily on linear econometric methods, Econ 715 will primarily focus on nonlinear econometric methods.

 

There is no textbook. However, the first half of the course will be largely based on the following Handbook chapter:

 Whitney Newey and Daniel McFadden, (1994) “Large sample estimation and hypothesis testing,” in Handbook of Econometrics, IV, ch 36.

 

There will be assignments, roughly every 2nd or 3rd week, which will involve problem solving and computer work. There will be no exams. The computer work will require nonlinear optimization, and can be done in either Matlab or R.

 

This course has been taught the past few years by Xiaoxia Shi, who has an excellent set of lecture notes on her webpage .

 

For background material I recommend my first-year PhD textbook

 

Problem Set # 1 and data

Problem Set # 2

 

Topics and Readings

Note: Links are provided for journal articles. They will only work if you have university library access.

 

1.      Extremum Estimators: NLLS, MLE, QMLE, Quantile Regression, GMM, Two-Step Estimators, Minimum Distance

a.       Hal White, (1982) “Maximum likelihood estimation of misspecified models”, Econometrica, 50, 1-25.

b.      Chernozhukov, Angrist, Fernandez-Val (2006) “Quantile regression under misspecification and the U.S. wage structureEconometrica, 74, 539-563.

2.      Consistency

a.       Whitney Newey and Daniel McFadden, (1994) “Large sample estimation and hypothesis testing,” in Handbook of Econometrics, IV, ch 36.

b.      Donald W.K. Andrews (1992) “Generic uniform convergence” Econometric Theory, 8, 241-257

c.       Whitney Newey (1991) “Uniform convergence in probability and stochastic equicontinuityEconometrica, 59, 1161-1167.

d.      Chernozhukov, Hong and Tamer (2007) “Estimation and confidence regions for parameter sets in econometric modelsEconometrica 75, 1243-1284

3.      Asymptotic Normality – smooth case

a.       Whitney Newey and Daniel McFadden, (1994) “Large sample estimation and hypothesis testing,” in Handbook of Econometrics, IV, ch 36.

4.      Empirical Process Theory

a.       Donald W.K. Andrews (1994) “Empirical process methods in econometrics”, in Handbook of Econometrics, IV, ch 37.

b.      David Pollard (1990) Empirical Processes: Theory and Applications.

5.      Asymptotic Normality – nonsmooth case

a.       Whitney Newey and Daniel McFadden, (1994) “Large sample estimation and hypothesis testing,” in Handbook of Econometrics, IV, ch 36.

6.      Generalized Method of Moments

a.       Lars Hansen (1982) “Large sample properties of generalized method of moments estimatorsEconometrica, 50, 1029-1054.

b.      Alastair Hall and Atsushi Inoue (2003) “The large sample behavior of the generalized method of moments estimator in misspecified modelsJournal of Econometrics, 114, 361-394.

7.      Two-Step Estimators

a.       Adrian Pagan (1984) “Econometric issues in the analysis of regressions with generated regressorsInternational Economic Review, 25, 221-247.

b.      Adrian Pagan (1986) “Two stage and related estimators and their applicationReview of Economic Studies, 53, 517-538.

c.       Whitney Newey and Daniel McFadden, (1994) “Large sample estimation and hypothesis testing,” in Handbook of Econometrics, IV, ch 36.

d.      Jinyong Hahn and Geert Ridder (2013) “Asymptotic variance of semiparametric estimators with generated regressors” Econometrica, 81, 315-340.

8.      Method of Simulated Moments

a.       Christian Gourieroux and Alain Monfort, (1996) Simulation-Based Econometric Methods, Oxford University Press.

b.      Daniel McFadden (1989) “A method of simulated moments for estimation of discrete response models without numerical simulationEconometrica 57, 995-1026.

c.       Ariel Pakes and David Pollard (1989) “Simulation and the asymptotics of optimization estimatorsEconometrica, 57, 1027-1057.

d.      Vassilis Hajivassiliou and Paul Ruud, (1994) “Classical estimation methods for LDV models using simulation,” in Handbook of Econometrics, IV, ch 40.

9.      Indirect Inference

a.       Christian Gourieroux and Alain Monfort, (1996) Simulation-Based Econometric Methods, Oxford University Press.

b.      Christian Gourieroux, Alain Monfort, and Eric Renault, (1993) “Indirect inferenceJournal of Applied Econometrics, 8, 85-118.

c.       Ron Gallant and George Tauchen (1996) “Which moments to match?” Econometric Theory, 12, 657-681.

10.  Quasi-Bayes Methods

a.       Victor Chernozhukov and Han Hong (2003) “An MCMC approach to classical estimationJournal of Econometrics, 115, 293-356.

b.      Ulrich Muller (2013) “Risk of Bayesian inference in misspecified models, and the sandwich covariance matrixEconometrica, 81, 1805-1849.

11.  GMM

a.       Susanne Schennach (2007) “Point estimation with exponentially tilted empirical likelihood” Annals of Statistics, 35, 634-672.

b.      Whitney Newey and Frank Windmeijer (2009) “Generalized method of moments with many weak moment conditionsEconometrica 77, 687-719

c.       Yuichi Kitamura, Andres Santos, Azeem Shaikh (2012) “On the asymptotic optimality of empirical likelihood for testing moment restrictionsEconometrica 80, 413-423.

d.      Yuichi Kitamura, Taisuke Otsu, and Kirill Evdokimov (2013) “Robustness, infinitesimal neighborhoods, and moment restrictionsEconometrica, 81, 1185-1202.

e.       Susanne Schennach (2014) “Entropic latent variable integration via simulationEconometrica, 82, 345-386.

12.  Moment Inequalities and Partial Identification

a.       Donald Andrews and Gustavo Soares (2010) “Inference for parameters defined by moment inequalities using generalized moment selectionEconometrica, 78, 119-158.

b.      Joseph Romano and Azeem Shaikh (2010) “Inference for the identified set in partially identified econometric modelsEconometrica, 78, 169-212.

c.       Ariel Pakes (2010) “Alternative models for moment inequalitiesEconometrica, 78, 1783-1822.

d.      Hyungsik Roger Moon and Frank Schorheide (2012) “Bayesian and frequentist inference in partially identified modelsEconometrica, 80, 755-782.

e.       Donald Andrews and Xu Cheng (2012) “Identification and inference with weak, semi-strong, and strong identificationEconometrica, 80, 2153-2211.

f.       Donald Andrews and Panle Jia Barwick (2012) “Inference for parameters defined by moment inequalities: A recommended moment selection procedureEconometrica, 80, 2805-2826.

g.      Donald Andrews and Xiaoxia Shi (2013) “Inference based on conditional moment inequalitiesEconometrica, 81, 609-666.

h.      Victor Chernozhukov, Sokbae Lee and Adam Rosen (2013) “Intersection bounds: Estimation and inferenceEconometrica, 81, 667-738.

i.        Hiroaki Kaido and Andres Santos (2014) “Asymptotically efficient estimation of models defined by convex moment inequalitiesEconometrica, 82, 387-414.

j.        Joseph Romano, Azeem Shaikh, and Michael Wolf (2014) “A practical two-step method for testing moment inequalitiesEconometrica, 82, 1979-2002.

k.      Ariel Pakes, Jack Porter, Kate Ho and Joy Ishi (2015) “Moment inequalities and their applicationEconometrica, 83, 315-334.

13.  Nondifferentiable Inference

a.       Keisuke Hirano and Jack Porter (2012) “Impossibility results for nondifferentiable functionsEconometrica, 80, 1769-1790.

b.      Zheng Fang and Andres Santos, “Inference on directionally differentiable functions,” working paper.