Economics 312

Theory and Practice of Econometrics
Spring 2015
Course Outline and Reading List

 

Basic text materials

0. Review of basic statistics
1. Introduction to econometrics
2. The bivariate regression model
3. Inference in the bivariate regression model
4. Basics of multiple regression
5. Inference in the multiple regression model
6. Functional form and nonlinearities
7. Model assessment

Midterm exam

8. Heteroskedasticity, GLS, and robust standard errors

9. Regression with stochastic regressors
10. Endogenous regressors, instrumental variables, and simultaneous equations
11. Regression with stationary time series
12. Time-series regression with nonstationary variables
13. Models for pooled and panel data
14. Limited dependent variables
15. Advanced topics

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.

  • **Hill, R. Carter, William E. Griffiths, and Guan C. Lim, Principles of Econometrics, 4th ed., New York: John Wiley & Sons, 2012. (The main text for the course. Most reading and many assignments will be drawn from here.)
  • **Stock, James H., and Mark W. Watson, Introduction to Econometrics, 2nd ed., Boston: Pearson Addison Wesley, 2007. (This is the text I used in Econ 312 before switching to HGL. It is a good one. There is a 3rd edition that is now out, but the library has the 2nd so that's the one to which I will refer you.)
  • **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 will 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 (A former text by some of the same authors that is perfect in level and detail, but very out of date.)
  • **Wooldridge, Jeffrey, Introductory Econometrics: A Modern Approach, 5th ed., Mason, Ohio: Thomson South-Western. (A common textbook for this course. It is very up-to-date, but somewhat different in style than HGL.)
  • ***Davidson, Russell, and James G. MacKinnon, Econometric Theory and Methods, Oxford: Oxford University Press, 2004.
  • ***Greene, William, Econometric Analysis, 6th ed., Englewood Cliffs, N.J.: Prentice-Hall, 2008. (An excellent advanced text in econometrics. This uses more advanced mathematics and 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, 5th ed., Boston: Pearson Addison Wesley, 2006. (A somewhat easier text used for Econ 311 at Reed.)
  • *Murray, Michael, Econometrics: A Modern Introduction, Boston: Pearson Addison Wesley, 2006. (Another simpler text.)

Note that the material on the syllabus below will be adjusted as the semester proceeds. Dates are approximate.

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0. Review of basic statistics

January 22-23, 10am-12 noon in Vollum 302

Required readings

  • None

Notes

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1. Introduction to econometrics

January 26

Required readings

  • Hill, Griffiths, and Lim, Chapter 1

Additional sources

  • Wooldridge, Chapter 1
  • Stock and Watson, Chapter 1

Notes

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2. The bivariate regression model

January 28 through February 5

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

  • Hill, Griffiths, and Lim, Chapter 2, including all appendices
  • Griffiths, Hill, and Judge, Section 5.4, augmented by their Appendix 3B as necessary.
  • Stock and Watson, Sections 17.2, 17.3.

Additional sources

  • Griffiths, Hill, and Judge, rest of Chapter 5.
  • Stock and Watson, Chapters 4 and Sections 5.4 and 5.5.
  • Wooldridge, Chapter 2.
  • Davidson and MacKinnon, Section 1.3 on specification of regressions, Section 1.5 on method of moments.

 Notes

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3. Inference in the bivariate regression model

February 6 through 12

Topics

  • Interval estimators
  • Hypothesis tests about single regression coefficients
  • Prediction
  • Goodness of fit
  • Specification issues and function form
  • Analyzing residuals

Required readings

  • Hill, Griffiths, and Lim, Chapters 3 and 4.

Additional sources

  • Stock and Watson, Chapter 5.
  • Asympototic properties are covered in the multiple-regression context in Griffiths, Hill, and Judge, Chapter 14.

 Notes

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4. Basics of multiple regression

February 13 through 16

Topics

  • Omitted-variable bias
  • Multiple-regression model
  • OLS assumptions in multiple regression
  • Distribution of OLS multiple-regression estimators

Required readings

  • Hill, Griffiths, and Lim, Chapter 5.
  • Stock and Watson, Section 18.1 and 18.5.

Additional sources

  • Stock and Watson, Chapter 6.
  • Griffiths, Hill, and Judge, Chapter 9.
  • Wooldridge, Chapter 3.

 Notes

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5. Inference in the multiple regression model

February 18 through 20

Topics

  • Kinds of tests in a multiple regression
  • Hypothesis tests on a single coefficient
  • Testing joint hypotheses
  • Single hypotheses involving multiple coefficients
  • Multivariate confidence sets
  • Goodness of fit in multiple regression
  • Some specification issues
  • Multicollinearity
  • Applications of multiple regression

Required readings

  • Hill, Griffiths, and Lim, Chapter 6.

Additional sources

  • Stock and Watson, Chapter 7.
  • Griffiths, Hill, and Judge, Chapters 10 and 11.
  • Wooldridge, Chapter 4 through 6.

 Notes

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6. Functional form and nonlinearities

February 23 through 27

Topics

  • Levels of measurement
  • Dummy (binary or indicator) variables
  • Interaction models
  • Treatment effects
  • Nonlinearity in parameters vs. nonlinearity in variables
  • Quadratic and higher-order polynomial models
  • Nonlinear least squares

Required readings

  • Hill, Griffiths, and Lim, Chapter 7.

Additional sources

  • Stock and Watson, Chapter 8.
  • Woodridge, Chapter 7.
  • Griffiths, Hill, and Judge, Chapter 8.
  • Greene, Chapters 6 and 7 (more advanced).

 Notes

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7. Model assessment

March 2

Topics

  • Internal vs. external validity
  • Assessing external validity
  • Assessing internal validity
  • Validity in forecasting/prediction

Required readings

  • Stock and Watson, Chapter 9.

Additional sources

  • Woodridge, Chapter 9.

 Notes

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Midterm Exam

The midterm exam will occur on March 4.

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8. Heteroskedasticity, generalized least squares, and robust estimation

March 5 and 6

Topics

  • Nature of heteroskedasticity
  • Tests for heteroskedasticity
  • OLS with robust standard errors
  • Generalized least squares/weighted least squares

Required readings

  • Hill, Griffiths, and Lim, Chapter 8.
  • Griffiths, Hill, and Judge, Sections 15.1 and 15.4.

Additional sources

  • Stock and Watson, Sections 18,2 and 18.6.
  • Woodridge, Chapter 8.
  • Griffiths, Hill, and Judge, rest of Chapter 15.
  • Greene, Chapter 8 (more advanced)

 Notes

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9. Regression with stochastic regressors and instrumental variables

March 9 and 11

Topics

  • Implications of and assumptions for random regressors
  • Theory of instrumental variables
  • Two-stage least-squares regression
  • Overidentification and generalized-method-of-moments estimators
  • Instrument strength and specification tests

Required Readings

  • Hill. Griffiths, and Lim, Chapter 10.

 Notes

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10. Estimation of simultaneous equations

Dates: March 12 through 20

Topics

  • System vs. single-equation estimation
  • Identification
  • Estimation of systems: seemingly-unrelated regressions and three-stage least squares

Required Readings

  • Hill. Griffiths, and Lim, Chapter 11 and pages 566-570.
  • Griffiths, Hill, and Judge, Chapters 17 through 19.

 Notes

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Note: The time-series sections of the text will incorporate draft chapters written by the instructor. The sequence timing of material may change.

11. Models for stationary time series

March 30 through April 2

Topics

  • Stationary and non-stationary time series
  • Dynamic models and distributed lags
  • Finite distributed lags
  • Models with lagged dependent variables
  • Serial correlation of the error: implications, detection, correction

Required Readings

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12. Time-series regression with nonstationary variables

Dates: April 3 through 10

Topics

  • Unit roots and integration
  • Testing for stationarity
  • Cointegration and error-correction models
  • Vector autoregression (VAR) models
  • Vector error-correction (VEC) models
  • Time-varying volatility: Autoregressive conditional heteroskedasticity (ARCH) models

Required readings

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13. Models for pooled and panel data

Dates: April 13 through 16

Topics

  • Pooled and panel data
  • Fixed-effects estimators
  • Random-effects estimators
  • Tests of appropriateness of models

Required readings

  • Hill, Griffiths, and Lim, Chapter 15.

 Notes

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14. Limited dependent variables

Dates: April 17 through 24

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

  • Hill, Griffiths, and Lim, Chapter 16.

Notes

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15. Advanced topics in econometrics

Dates: April 25 through May 1

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

Notes

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