EC 615a-001: Course Outline and Reading List

 

 

The following is an overview of the topics I plan to discuss, together with a reading list. It may happen that, as the course progresses, I will add more references to this list. In that case, I will either announce these references in class, or provide you with an updated syllabus. Readings that are marked with a star (*) will be discussed in class in more detail. For each topic I have indicated an estimated number of lectures. This is meant as a rough guideline, and we may spend more or less time on certain topics if necessary.

1. (2 lectures) Review of Probability and Statistics: set theory; probability measure; random variables; useful inequalities; modes of convergence for sequences of random variables; laws of large numbers; central limit theorems.
  • *Sen & Singer, chapters 2-3;
  • *Amemiya, chapter 3;
  • Davidson, chapters 9, 18, 20, 23;
2. (2 lectures) Extremum Estimators: consistency and asymptotic normality; hypothesis testing.
  • *Amemiya, chapter 4;
  • *Newey & McFadden: ‘Large Sample Estimation and Hypothesis Testing’ Handbook of Econometrics, vol. IV, chapter 36, sections 1-4;
  • Hayashi, chapter 7;
  • Mittelhammer et al., chapter 7.
3. (4 lectures) Generalized Method of Moments: definition and examples (OLS, IV, 2SLS); asymptotic and finite sample properties; weak identification and overidentification.
  • Mittelhammer et al., chapter 16;
  • Davidson & MacKinnon, chapter 9;
  • Hansen, L.P: ‘Large Sample Properties of Generalized Method of Moments Estimators’, Econometrica 50(1982), 1029-1054;
  • * Hansen, L.P., Heaton, J. & Yaron, A: ‘Finite-Sample Properties of Some Alternative GMM Estimators’, Journal of Business and Economic Statistics 14(1996), 262-280;
Two-Stage Least Squares; Weak and Many Instruments.
  • * Nelson, C.R. & Startz, R: ‘The Distribution of the Instrumental Variables Estimator and Its t-ratio When the Instrument is a Poor One’, The Journal of Business 63(1990), 125-140;
  • * Nelson, C.R. & Startz, R: ‘Some further Results on the Exact Small Sample Properties of the Instrumental Variables Estimator’, Econometrica 58(1990), 967-976;
  • Bound, J., Jaeger, D.A. & Baker, R.M: ‘Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak’, Journal of the American Statistical Association 90(1995), 443-450;
  • Buse, A: ‘The Bias of Instrumental Variable Estimators’, Econometrica 60(1992), 173-180;
  • * Staiger, D. & Stock, J.H: ‘Instrumental Variables Regression with Weak Instruments’, Econometrica 65(1997), 557-586;
  • Bekker, P.A: ‘Alternative Approximations to the Distributions of Instrumental Variable Estimators’, Econometrica 62(1994), 657-681;
  • Koenker, R. & Machado, J.A.F: ‘GMM Inference When the Number of Moment Conditions is Large’, Journal of Econometrics 93(1999), 327-344;
  • * Hall, A.R., Rudebusch, G.D. & Wilcox, D.W: ‘Judging Instrument Relevance in Instrumental Variables Estimation’, International Economic Review 37(1996), 283-298;
  • * Shea, J: ‘Instrument Relevance in Multivariate Linear Models: A Simple Measure’, The Review of Economics and Statistics 79(1997), 348-352;
  • * Hahn, J. & Hausman, J: ‘Notes on Bias in Estimators for Simultaneous Equation Models’, Economics Letters 75(2002), 237-241;
  • Donald, S.G. & Newey, W.K: ‘Choosing the Number of Instruments’, Econometrica 69(2001), 1161-1191;
GMM with Weak and/or Many Instruments.
  • * Stock, J.H. & Wright, J.H: ‘GMM with Weak Identification’, Econometrica 68(2000), 1055-1096;
  • Han, C. & Phillips, P.C.B: ‘GMM with Many Moment Conditions’, Econometrica 74(2006), 147-192;
Valid and Robust Inference.
  • * Wang, J. & Zivot, E: ‘Inference on Structural Parameters in Instrumental Variables Regression with Weak Instruments’, Econometrica 66(1998), 1389-1404;
  • * Zivot, E., Startz, R. & Nelson, C.R: ‘Valid Confidence Intervals and Inference in the Presence of Weak Instruments’, International Economic Review 39(1998), 1119-1144;
  • * Gleser, L.J. & Hwang, J.T: ‘The Nonexistence of 100(1-α)% Confidence Sets of Finite Expected Diameter in Errors-in-Variables and Related Models’, The Annals of Statistics 15(1987), 1351-1362;
  • Dufour, J-M: ‘Some Impossibility Theorems in Econometrics With Applications to Structural and Dynamic Models’, Econometrica 65(1997), 1365-1387;
  • Dufour, J-M. & Taamouti, M: ‘Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments’, Econometrica 73(2005), 1351-1366;
  • * Kleibergen, F: ‘Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression’, Econometrica 70(2002), 1781-1803;
  • * Kleibergen, F: ‘Testing Parameters in GMM Without Assuming that They Are Identified’, Econometrica 73(2005), 1103-1124;
  • * Moreira, M: ‘A Conditional Likelihood Ratio Test for Structural Models’, Econometrica 71(2003), 1027-1048;
Empirical Work:
  • Hansen, L.P. & Singleton, K.J: ‘Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models’, Econometrica 50(1982), 1269-1285;
  • Holman, J.A: ‘GMM Estimation of a Money in the Utility Function Model: the Implications of Functional Forms’, Journal of Money, Credit and Banking 30(1998), 679-698;
  • Hansen, H. & Tarp, F: ‘Aid and Growth Regressions’, Journal of Development Economics 64(2001), 547-570;
  • Yashiv, E: ‘The Determinants of Equilibrium Unemployment’, American Economic Review 90(2000), 1297-1322;
  • Nevo, A: ‘Using Weights to Adjust for Sample Selection When Auxiliary Information is Available’, Journal of Business and Economic Statistics 21(2003), 43-52;
4. (4 lectures) Alternatives to GMM: Empirical Likelihood and Extensions.
  • Mittelhammer et al., chapter 12;
  • * Owen, chapters 1-3;
  • * Qin, J. & Lawless, J: ‘Empirical Likelihood and General Estimating Equations’, The Annals of Statistics 22(1994), 300-325;
  • * Owen, A.B: ‘Empirical Likelihood Ratio Confidence Intervals for a Single Functional’, Biometrika 75(1988), 237-249;
  • * Owen, A.B: ‘Empirical Likelihood Ratio Confidence Regions’, The Annals of Statistics 18(1990), 90-120;
  • * Owen, A.B: ‘Empirical Likelihood for Linear Models’, The Annals of Statistics 19(1991), 1725-1747;
  • * Kitamura, Y. & Stutzer, M: ‘An Information-Theoretic Alternative to Generalized Method of Moments Estimation’, Econometrica 65(1997), 861-874;
  • * Imbens, G., Spady, R. & Johnson, P: ‘Information Theoretic Approaches to Inference in Moment Condition Models’, Econometrica 66(1998), 333-357;
  • Kitamura, Y: ‘Empirical Likelihood Methods in Econometrics: Theory and Practice’, Cowles Foundation Discussion Paper 1569 (June 2006). Available at http://cowles.econ.yale.edu;
  • Lazar, N. & Mykland, P.A: ‘An Evaluation of the Power and Conditionality Properties of Empirical Likelihood’, Biometrika 85(1998), 523-534;
  • * Smith, R.J: ‘Alternative Semi-Parametric Likelihood Approaches to Generalised Method of Moments Estimation’, The Economic Journal 107(1997), 503-519;
  • Newey, W.K. & Smith, R.J: ‘Higher Order Properties of GMM and Generalized Empirical Likelihood Estimators’, Econometrica 72(2004), 219-256.
5. (4 lectures) Quantile Regression: concepts and computation; asymptotic properties; instrumental variables.
  • * Koenker, chapters 1-3, 6;
  • Powell, J.L: ‘Estimation of Semiparametric Models’, Handbook of Econometrics, vol. IV, chapter 41;
  • * Koenker, R. & Basset, G: ‘Regression Quantiles’, Econometrica 46(1978), 33-50;
  • * Basset, G. & Koenker, R: ‘Asymptotic Theory of Least Absolute Error Regression’, Journal of the American Statistical Association 73(1978), 618-622;
  • Buchinsky, M: ‘Recent Advances in Quantile Regression Models’, The Journal of Human Resources 33(1998), 88-126;
  • * Powell, J.S: ‘Least Absolute Deviations Estimation for the Censored Regression Model’, Journal of Econometrics 25(1984), 303-325;
  • * Powell, J.S: ‘Censored Regression Quantiles’, Journal of Econometrics 32(1986), 143-155;
  • Buchinsky, M. & Hahn, J: ‘An Alternative Estimator for the Censored Quantile Regression Model’, Econometrica 66(1998), 653-671;
  • Abadie, A., Angrist, J. & Imbens, G: ‘Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings’, Econometrica 70(2002), 91-117;
  • * Chernozhukov, V. & Hansen, C: ‘An IV Model of Quantile Treatment Effects’, Econometrica 73(2005), 245-262;
     
Empirical Work: the March 2001 issue of Empirical Economics is entirely devoted to applications of quantile regression. Two papers in that issue are listed below.
  • Buchinsky, M: ‘Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression’, Econometrica 62(1994), 405-458;
  • Buchinsky, M: ‘The Dynamics of Changes in the Female Wage Distribution in the USA: A Quantile Regression Approach’, Journal of Applied Econometrics 13(1998), 1-30;
  • Machado, J.A.F. & Mata, J: ‘Earning Functions in Portugal 1982-1994: Evidence from Quantile Regressions’, Empirical Economics 26(2001), 115-134;
  • Levin, J: ‘For Whom the Reductions Counts: A Quantile Regression Analysis of Class Size and Peer Effects on Scholastic Achievement’, Empirical Economics 26(2001), 221-246.
6. (6 lectures) Bayesian econometrics: prior, likelihood and posterior; model checking; computation and Markov Chain Monte Carlo (MCMC); linear and nonlinear regression models, instrumental variables; semiparametric models.
Introduction and Computation.
  • * Lancaster, chapters 1-5;
  • Geweke, chapters 1-6;
  • Poirier, D.J.: ‘Revising Beliefs in Nonidentified Models’, Econometric Theory 14(1998), 483-509;
  • * Casella, G. & George, E.I.: ‘Explaining the Gibbs Sampler’, The American Statistician 46(1992), 167-174;
  • * Chib, S. & Greenberg, E.: ‘Understanding the Metropolis-Hastings Algorithm’, The American Statistician 49(1995), 327-335;
  • * Geweke, J.: ‘Bayesian Inference in Econometric Models Using Monte Carlo Integration’, Econometrica 57(1989), 1317-1339;
  • Tierney, L.: ‘Markov Chains for Exploring Posterior Distributions’, Annals of Statistics 22(1994), 1701-1728;
  • Chib, S.: ‘Marginal Likelihood from the Gibbs Output’, Journal of the American Statistical Association 90(1995), 1313-1321;
  • Chib, S. & Jeliazkov, I.: ‘Marginal Likelihood from the Metropolis-Hastings Algorithm’, Journal of the American Statistical Association 96(2001), 270-281;
  • Paap, R.: ‘What are the Advantages of MCMC Based Inference in Latent Variable Models?’, Statistica Neerlandica 56(2002), 2-22.
Selection of Specific Models
  • Yu, K. & Stander, J.: ‘Bayesian Analysis of a Tobit Quantile Regression Model’, Journal of Econometrics 137(2007), 260-276;
  • Fernández, C. & Steel, M.F.J.: ‘Bayesian Regression Analysis with Scale Mixtures of Normals’, Econometric Theory 16(2000), 80-101;
  • McCulloch, R.E. & Polson, N.G. & Rossi, P.E.: ‘A Bayesian Analysis of the Multinomial Probit Model with Fully Identified Parameters’, Journal of Econometrics 99(2000), 173-193;
  • * Albert, J.H. & Chib, S.: ‘Bayesian Analysis of Binary and Polychotomous Response Data’, Journal of the American Statistical Association 88(1993), 669-679;
  • * Geweke, J.: ‘Bayesian Treatment of the Independent Student-t Linear Model’, Journal of Applied Econometrics 8(1993), S19-S40;
  • Lancaster, T.: ‘Exact Structural Inference in Optimal Job-Search Models’, Journal of Business and Economic Statistics 15(1997), 165-179;
  • * Koop, G.: ‘Bayesian Inference in Models Based on Equilibrium Search Theory’, Journal of Econometrics 102(2001), 311-338.
Model Choice and Model Averaging
  • * Mitchell, T.J. & Beauchamp, J.J.: ‘Bayesian Variable Selection in Linear Regression’, Journal of the American Statistical Association 83(1988), 1023-1032;
  • * George, E.I. & McCulloch, R.E.: ‘Variable Selection via Gibbs Sampling’, Journal of the American Statistical Association 88(1993), 881-889;
  • Raftery, A.E. & Madigan, D. & Hoeting, J.A.: ‘Bayesian Model Averaging for Linear Regression Models’, Journal of the American Statistical Association 92(1997), 179-191;
  • Gelfand, A.E. & Dey, D.K.: ‘Bayesian Model Choice: Asymptotics and Exact Calculations’, Journal of the Royal Statistical Society, Series B 56(1994), 501-514;
  • Carlin, B.P. & Chib, S: ‘Bayesian Model Choice via Markov Chain Monte Carlo’, Journal of the Royal Statistical Society, Series B 57(1995), 473-484;
  • * Green, P.J.: ‘Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination’, Biometrika 82(1995), 711-732;
  • * Fernández, C. & Ley, E. & Steel, M.F.J.: ‘Benchmark Priors for Bayesian Model Averaging’, Journal of Econometrics 100(2001), 381-427.
Semiparametric Bayesian Models.
  • * Griffin, J.E. & Steel, M.F.J.: ‘Semiparametric Bayesian Inference for Stochastic Frontier Models’, Journal of Econometrics 123(2004), 121-152;
  • Hirano, K.: ‘Semiparametric Bayesian Inference in Autoregressive Panel Data Models’, Econometrica 70(2002), 781-799;
  • Bayarri, M.J. & Berger, J.: ‘Robust Bayesian Analysis of Selection Models’, The Annals of Statistics 26(1998), 645-659;
  • Lee, J. & Berger, J.: ‘Semiparametric Bayesian Analysis of Selection Models’, Journal of the American Statistical Association 96(2001), 1397-1409;
  • Koop, G. & Poirier, D.J.: ‘Bayesian Variants of Some Classical Semiparametric Regression Techniques’, Journal of Econometrics 123(2004), 259-282;
  • * Koop, G. & Tobias, J.L.: ‘Semiparametric Bayesian Inference in Smooth Coefficient Models’, Journal of Econometrics 134(2006), 283-315.

Important Dates

  • Tuesday, September 11: first class;
  • Tuesday, December 4: last class;
  • Tuesday, December 18: term papers due by noon.

Please Note

  • While students are encouraged to work together on the homework assignments, it is the students’ responsibility to ensure that the work handed in is, in fact, their own work. If it is discovered that someone has merely copied solutions, then both the student who did the copying and the one that allowed it, will receive a zero grade for the assignment.
  • All assignments will have a clearly marked due date and must be handed in on or before that date. No late assignments will be accepted.
  • Plagiarism: students must write their essays and assignments in their own words. Whenever a student takes an idea, or a passage from another author, they must acknowledge it by using quotation marks where appropriate and by proper referencing such as footnotes or citations. Plagiarism is a major academic offence (see Scholastic Offence Policy in the current UWO Academic Calendar). The University of Western Ontario uses software for plagiarism checking. Students may be required to submit their written work in electronic form for plagiarism checking.
  • The policy of the University is that, when a course instructor wishes to change the evaluation procedure, as outlined in his or her course outline at the beginning of the year, prior approval must be obtained from the dean of the faculty concerned

 

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