Theory and Practice of Econometrics
Spring 2020
Course Outline and Reading List
Not everything that can be counted counts, and not everything that counts can be counted. -- Albert Einstein
0. Review of basic statistics
1. Introduction to econometrics
2. The bivariate regression model
3. Basics of multiple regression
4. Inference and analysis in the multiple regression model
5. Qualitative data in regression models
6. Specification and model assessment
7. Heteroskedasticity, GLS, and robust standard errors
8. Regression models with time-series data
9. Advanced time-series analysis
10. Models for pooled and panel data
11. Endogenous regressors, instrumental variables, and simultaneous equations
12. Limited dependent variables
13. Advanced topics
Note that the material on the syllabus below will be adjusted as the semester proceeds. Dates are forecasts, not promises. Many Wednesdays have been reserved for discussion of project assignments. If these discussions can be abbreviated, we will move more quickly.
Some sections of the reading list have a Notes link at the bottom. These links will be activated as we proceed through the course and will link to the instructor's class notes for that topic.
Basic text materials
Most of the readings and assignments for Econ 312 will be taken from the following list of texts, which will be on reserve in the library. Notation varies some across texts, so be careful when switching among them. Class presentations will conform to the notation and sequencing of the main text, by Hill, Griffiths, and Lim. On the list, ** indicates texts at about the same level of mathematical complexity as the HGL text. Texts marked with *** are more difficult; those marked * are more basic. Those that are more difficult typically assume knowledge of "mathematical statistics" at the level of Reed's Math 392.
- **Wooldridge, Jeffrey, Introductory Econometrics: A Modern Approach, 7th ed. (The main textbook for the course this semester.)
- **Hill, R. Carter, William E. Griffiths, and Guan C. Lim, Principles of Econometrics, 4th ed., New York: John Wiley & Sons, 2012. (Formerly used as the main text for the course. About the same level as Wooldridge.)
- **Stock, James H., and Mark W. Watson, Introduction to Econometrics, 2nd or 3rd ed., Boston: Pearson Addison Wesley, 2007. (Another text formerly used in 312.)
- **Berndt, Ernst, The Practice of Econometrics: Classic and Contemporary, Reading, Mass.: Addison Wesley, 1990. (Not a traditional econometrics text. Contains topical chapters on applications of econometrics with data sets and exercises. Some weekly econometrics projects may be drawn from here.)
- **Griffiths, William E., R. Carter Hill, and George G. Judge, Learning and Practicing Econometrics, New York: John Wiley & Sons, 1993. (A former text by some of the same authors that is perfect for Econ 312 in level and detail, but very out of date.)
- ***Davidson, Russell, and James G. MacKinnon, Econometric Theory and Methods, Oxford: Oxford University Press, 2004. (A more advanced text that is quite excellent.)
- ***Greene, William, Econometric Analysis, any recent edition, Englewood Cliffs, N.J.: Prentice-Hall. (An excellent advanced text in econometrics. This uses more advanced mathematics and formal statistics than we will, but is a good reference for the theory underlying our estimators and for lots of extensions and variations.)
- ***Hamilton, James, Time Series Analysis, Princeton: Princeton University Press, 1994. (A specialized time-series book that is very difficult but authoritative.)
- ***Enders, Walter, Applied Econometric Time Series, New York: John Wiley & Sons, 1995. (Another time-series text that we may use for special topics toward the end of the course.)
- *Studenmund, A. H., Using Econometrics: A Practical Guide, 7th ed., Boston: Pearson Addison Wesley, 2017. (A somewhat easier text used for Econ 311 at Reed.)
- *Murray, Michael, Econometrics: A Modern Introduction, Boston: Pearson Addison Wesley, 2006. (Another simpler text.)
0. Review of basic statistics
Dates: January 22-24, 1-3pm in Vollum 228
Required readings
- None, but classes will be based on Math Refreshers B and C at the end of the Wooldridge text (pp. 684-748).
1. Introduction to econometrics
Date: January 27
Required readings
- Wooldridge, Chapter 1
Additional sources
- Hill, Griffiths, and Lim, Chapter 1
- Stock and Watson, Chapter 1
2. The bivariate regression model
Dates: January 29 through February 7
Topics
- What regression does
- Assumptions of the simple-regression model
- Strategies for obtaining regression estimators: method of least-squares, method of moments, method of maximum likelihood
- Least-squares regression model in matrix notation
- Sampling distribution of OLS estimator in finite samples
- Monte Carlo methods
- Asymptotic properties of the OLS estimator
- How good is the OLS estimator?
Required readings
- Wooldridge, Chapter 2, Appendix C-4, and Appendices D and E on matrix methods.
- Hill, Griffiths, and Lim, Appendix 2G on Monte Carlo methods.
Additional sources
- Griffiths, Hill, and Judge, Chapter 5.
- Stock and Watson (2nd or 3rd ed.), Chapter 4 and Sections 5.4 and 5.5.
- Hill, Griffiths, and Lim, Chapter 2.
- Davidson and MacKinnon, Section 1.3 on specification of regressions, Section 1.5 on method of moments.
3. Basics of multiple regression
Dates: February 12 through 13
Topics
- Omitted-variable bias
- Multiple-regression model
- OLS assumptions in multiple regression
- Distribution of OLS multiple-regression estimators
- Multicollinearity
Required readings
- Wooldridge, Chapter 3
Additional sources
- Stock and Watson (2nd or 3rd ed.), Chapter 6, Section 18.1 and 18.5.
- Griffiths, Hill, and Judge, Chapter 9.
- Hill, Griffiths, and Lim, Chapter 5.
4. Inference and analysis in the regression model
Dates: February 14 through 20
Topics
- Kinds of tests in a multiple regression
- Hypothesis tests on a single coefficient
- Single hypotheses involving multiple coefficients
- Testing joint hypotheses
- Multivariate confidence sets
- Goodness of fit in multiple regression
- Asymptotic properties
- Nonlinear specifications
Required readings
- Wooldridge, Chapter 4 through 6.
Additional sources
- Stock and Watson (2nd or 3rd ed.), Chapter 7.
- Griffiths, Hill, and Judge, Chapters 10 and 11.
- Hill, Griffiths, and Lim, Chapter 6.
5. Qualitative data in regression models
Dates: February 21 through 28
Topics
- Levels of measurement
- Dummy (binary or indicator) variables
- Interaction models
- LInear probability model
- Treatment effects
Required readings
- Woodridge, Chapter 7.
Additional sources
- Stock and Watson (2nd or 3rd ed.), Chapter 8.
- Hill, Griffiths, and Lim, Chapter 7.
- Griffiths, Hill, and Judge, Chapter 8.
- Greene, Chapters 6 and 7 (more advanced).
6. Specification and model assessment
Dates: March 2 through 4
Topics
- Internal vs. external validity
- Assessing external validity
- Assessing internal validity
- Validity in forecasting/prediction
Required readings
- Woodridge, Chapter 9.
- Stock and Watson, Chapter 9.
Midterm Exam
The midterm exam will probably occur on Thursday, March 5.
7. Heteroskedasticity, generalized least squares, and robust estimation
Date: March 6 and 9
Topics
- Nature of heteroskedasticity
- Tests for heteroskedasticity
- OLS with robust standard errors
- Generalized least squares/weighted least squares
Required readings
- Woodridge, Chapter 8.
Additional sources
- Stock and Watson, Sections 18,2 and 18.6.
- Hill, Griffiths, and Lim, Chapter 8.
- Griffiths, Hill, and Judge, Chapter 15.
- Greene, Chapter 8 (more advanced)
8. Regression models with time-series data
Date: March 11 through April 1
Topics
- Time-series data
- Using OLS with time series
- Simple lag models
- Trends and seasonality
- Stationarity and weak dependence
- Regression with persistent time series
- Correcting for serial correlation
- Koyck and rational lag models
Required readings
- Woodridge, Chapters 10, 11 and 12.
- Parker, Fundamental Concepts of Time-Series Econometrics
- Parker, Regression with Stationary Time Series
- Parker, Distributed-Lag Models
9. Advanced time-series methods
Date: April 3 and April 6
Topics
- Unit roots and spurious regression
- Cointegration and error-correction models
- Vector autoregression
Required readings
- Woodridge, Chapter 18.
- Parker, Regression with Non-Stationary Variables
- Parker, Vector Autoregression and Vector Error Correction
10. Models for pooled and panel data
Dates: April 8 and 10
Topics
- Pooled and panel data
- Fixed-effects estimators
- Random-effects estimators
- Tests of appropriateness of models
Required readings
- Wooldridge, Chapters 13 and 14.
11. Endogenous regressors, instrumental variables, and simultaneous equations
Dates: April 13 through April 20
Topics
- Theory of instrumental variables
- Two-stage least-squares regression
- Overidentification and generalized-method-of-moments estimators
- Instrument strength and specification tests
- System vs. single-equation estimation
- Identification
- Estimation of systems: seemingly-unrelated regressions and three-stage least squares
Required Readings
- Wooldridge, Chapters 15 and 16.
Notes on IV
Notes on simultaneous equations
12. Limited dependent variables
Dates: April 22 through 27
Topics
- Nature of limited dependent variables
- Probit and logit models for binary dependent variables
- Multinomial logit model for multiple discrete choices
- Ordered dependent variables
- Models for count data
- Censored and truncated dependent variables: tobit and heckit models
Required reading
- Wooldridge, Chapter 17.
13. Advanced topics in econometrics
Dates: Whatever time we have left
Topics chosen among
- Specification search and data mining
- Publication bias
- Monte Carlo and bootstrap methods
- Imputation methods for missing data
- Varying-parameter models
- Duration/hazard models
- Quantile regression
- Bayesian methods in econometrics
Readings
- Leamer, Edward E., "Let's Take the Con Out of Econometrics," American Economic Review, 73(1), March 1983, 31-43.
- Lovell, Michael C., "Data Mining," Review of Economics and Statistics, 65(1), February 1983, 1-12.
- Angrist, Joshua D., and Jörn-Steffen Pischke, "The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics," Journal of Economic Perspectives, 24(2), Spring 2010, 3-30.
- Leamer, Edward E., "Tantalus on the Road to Asymptopia," Journal of Economic Perspectives, 24(2), Spring 2010, 31-46.
- Hill, Griffiths, and Lim, Appendix 2G and Appendix 3C.
- Davidson and MacKinnon, Section 4.6: Simulation-Based Tests.
- Greene, Sections 17.1, 17.2, 17.4: Simulation-Based Inference.
- De Long, J. Bradford, and Kevin Lang, "Are All Economic Hypotheses False?" Journal of Political Economy 100(6), December 1992, 1257-72.
- Little, Roderick J. A., "Regression with Missing X's: A Review," Journal of the American Statistical Association 87(420), December 1992, 1227-37.
- Maddala, G. S., Econometrics, New York: McGraw-Hill, 1977, Chapter 17: Varying-Parameter Models.
- Greene, Section 25.6: Models for Duration Data.
- Koenker, Roger, and Kevin F. Hallock, "Quantile Regression," Journal of Economic Perspectives 15(4), Autumn 2001, 143-156.
- Mueller, Julie M., and John B. Loomis, "Spatial Dependence in Hedonic Property Models: Do Different Corrections for Spatial Dependence Result in Economically Significant Differences in Estimated Implicit Prices?" Journal of Agricultural and Resource Economics 33(2), August 2008, 212-231.
- van der Klaauw, Wilbert, "Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach," International Economic Review 43(4), November 2002, 1249-86.
- Manacorda, Marco, Edward Miguel, and Andrea Vigorito, "Government Transfers and Political Support," London: Centre for Economic Policy Research. CEPR Discussion Papers, No. 7163, February 2009.