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388_syllabus_W_2012

388_syllabus_W_2012 - ECONOMICS 388 INTRODUCTION TO...

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ECONOMICS 388 INTRODUCTION TO ECONOMETRICS Winter 2012 PROFESSOR : James B. McDonald TA’s: Ian Lindsay [email protected] [email protected] Phone: (801) 422-3463 (303) 656-0927 TEXT : Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach , a recent edition (but not necessarily new), Southwestern College Publishing, 2006 or 2008 REFERENCE: Kennedy, Peter. A Guide to Econometrics , MIT Press. Provides motivation for many econometric procedures http://www.khanacademy.org Free online resource, founded by Bill Gates, which includes some useful mathematics and statistics tutorials. Some students have found this to be very helpful. COURSE OUTLINE : I. Introduction. (chapter 1) A. Models and basic concepts. B. Data. C. Econometric project D. Problem set II. Simple Regression--Classical Linear Regression Model (chapter 2) A. Introduction (W: 2.1) B. Alternative parameter estimators OLS: (W: 2.2) MLE and BLUE: (notes) Instrumental variables C. Properties of least squares estimators (W: 2.3, 2.5) D. Distribution of estimators (W: 2.5) E. Statistical inference (Classical Normal Linear Regression Model) (See Wooldridge chapter 4--extended to multiple variables)) F. Prediction (W: 6.4) G. Some basic Stata commands H. Functional forms (W: 2.4) I. Problem sets (1 and 2)
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III. Classical Normal Linear Regression Model Extended to the Case of K Independent Variables (Wooldridge: chapter 3 (estimation), chapter 4 (inference), and chapter 5(properties) in algebraic form. Appendix E for a matrix approach. ) A. Basic Concepts B. Basic model C. Estimation D. Distribution of estimators E. Statistical inference F. Stepwise regression G. Forecasting H. Problem sets (1 and 2) IV. Miscellaneous Topics (exercises are given at the end of the individual sections) A. Multicollinearity (W: pp. 95-102) B. Dummy variables 1. Independent variables (W: 7.1-7.4) 2. Dependent variables: LPM, Probit, Logit (W: 7.5 & 7.1) C. Lagged variables (W: 18.1) D. "Causality" or Exogeneity E. Differences in differences F. Regression discontinuities V. Violations of the Basic Assumptions and extensions of the Classical Normal Linear Regression Model A. Introduction B. Normality assumption C. Assumption of a zero mean (W: 5.1) D. Generalized regression model E. Heteroskedasticity (W: 8) F. Autocorrelation of the error terms (W: 12) G. Panel Data (W: 13) H. Stochastic independent variables (W: 5.1 & 15) & Inst.Var revisited I. Errors of measurement. (W: 9.4) J. Specification error. (9.1) K. Problem set VI. A Brief Introduction to Simultaneous Equations Models.(W: 15 and 16) A.
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