Economics 314                                  Econometrics

J.R. Carter

Fall 2004

 

Text:

 

Hill, Griffiths, and Judge, Undergraduate Econometrics, 2nd ed. (New York:  John Wiley, 2001).

Reiman and Hill, Using EViews for Undergraduate Econometrics, 2nd ed. (New York:  John Wiley, 2001).

 

Graded Assignments:

 

            Problem Sets                                                  10%

            First Exam                  October 14                  20%

            Second Exam             November 16              20%

            Paper                          December 8                30%

            Final Exam                  December 16              20%

 

Problem sets use the regression software package called EViews Student Version 3.1, which is provided on disk in Reiman and Hill.

 

Exemptions from problem sets and midterms require a note from the class dean.  Late problem sets are accepted with penalty until answer sheets are distributed.  Makeup exams are not given; assignment weights are redistributed.  The paper assignment is described separately.

 

Provisional Course Outline and Readings:

 

O.        Introduction

Econometrics

Summation Notation

Chap. 1 (skim).

 

I.          Probability and Statistics

 

Probability

Random Variables and Probability Functions: Marginal, Joint, and Conditional

Expected Values: Mean, Variance, Covariance, and Linear Transformations

Chap. 2, pp. 11-31.

Statistics

Estimators

Small Sample Properties: Bias and Efficiency

Large Sample Properties: Consistency, Slutsky Theorem, and Asymptotic Distribution

Domadar Gujarati, Essentials of Econometrics, 2d ed. (Boston: McGraw-Hill, 1999), Chap. 2, pp. 52-56, Chap. 3, pp. 66-81, and Chap. 4, pp. 101-105 (on reserve).

II.          Classical Least Squares Regression

 

Assumptions

Chap. 3, pp. 42-49.

Ordinary Least Squares and Goodness of Fit

Chap. 3, pp. 50-59.

Chap. 6, pp. 121-128.

Gauss Markov and Sampling Distributions

Chap. 4, pp. 68-79, 79-84.

Multiple Regression and Residualing Out

Chap. 7, pp. 145-156, 162-164.

Inference: t Tests

Chap. 2, pp. 31-35.

Chap. 5, pp. 92-94.

Chap. 5, pp. 90-91, 95-110, 110-113 (optional).

Chap. 7, pp. 156-162.

 

III.         Topics in Classical Regression

 

Functional Forms

Chap. 6, pp. 128-139.

Chap. 10, pp. 218-222.

Dummy Variables

Chap. 9, pp. 199-209.

Inference: F Tests

Chap. 8, pp. 170-179.

Chap. 9, pp. 209-213.

Multicollinearity and Specification Error

Chap. 8, pp. 184-191.

 

IV.        Heteroskedasticity (typically with cross-sectional data)

 

Nature and Consequences

Heteroskedasticity Tests:  2-Variance Sample Partition and White's Test

Weighted Least Squares Estimation

White's Heteroskedasticity-Consistent Standard Errors

Chap. 11.

 

V.         Autocorrelation (with time-series data)

 

Nature and Consequences

Durbin-Watson Test

AR(1) Estimation

Chap. 12, pp. 258-275.

 

VI.        Special Topics (for future study)

 

Simultaneous Equations

Chap. 14.

Qualitative Dependent Variables: Linear Probability, Logit, and Probit Models

Chap. 18, pp. 368-376.

Pooling Time-Series and Cross-Sectional Data

Chap. 17.