Boal's Econ 107

Introduction to Econometrics (Econ 107)
Drake University, Summer 2011 Web Course
William M. Boal

Course page: www.drake.edu/cbpa/econ/boal/107
Blackboard: bb.drake.edu
Email: william.boal@drake.edu

SYLLABUS

1. Resources | 2. Requirements | 3. Schedule

1. Resources

Prerequisites: ECON 1, ECON 2, STAT 72 or 141, and MATH 28 or 50. Students who have forgotten statistics should allocate extra time for weeks 1 and 2 of the course.

Who should take this course:

Economics majors. This course counts toward the 15 hours of 100-level elective courses in economics required for the Economics major at Drake.
Quantitative Economics majors. This course (or STAT 142) is required for the Quantitative Economics major at Drake.
Actuarial Science majors. This course substitutes for STAT 142 in the Actuarial Science major at Drake, and counts for ASA/FSA VEE credit for applied statistical methods (see www.soa.org/files/pdf/edu-vee-dir-approved-courses.pdf, page 100).
Finance majors. Regression methods studied in this course are covered by the CFA Level II exam.
Mathematics majors. This course may be counted toward the "application of mathematics" requirement, with approval from your Mathematics advisor.
Students interested in research. This course teaches data-analysis techniques useful for a senior paper at Drake, for graduate school, and for research jobs in business, consulting, or government.
Students preparing for graduate school. This course teaches data-analysis techniques widely used in graduate programs in economics, business, and public policy.

Lectures: This is an on-line course. Instead of attending lectures, students view materials through the internet and submit assignments electronically using a web browser. For Summer 2011, Econ 107 online runs from July 5 through August 13, with classroom final exam on August 18.

How to contact instructor:

Office: 319 Aliber Hall
Telephone and voice mail: 271-3129
Electronic mail: william.boal@drake.edu
U.S. mail: William M. Boal, College of Business and Public Administration,
Drake University, 2507 University Avenue, Des Moines, Iowa 50311-4505

Required textbooks and materials:

Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 4th edition, South-western Cengage Learning, 2009, ISBN-10: 0-324-66054-5, ISBN-13: 978-0-324-66054-8. Available at University Bookstore (go to http://www.drakebookstore.com/ or call 1-800-430-9875). Earlier editions are acceptable substitutes.
Your favorite introductory statistics textbook. Keep it handy, especially for Weeks 1 and 2 of the course.
Other materials will be posted online at Drake's Blackboard system (http://bb.drake.edu/).

Required software:

A web browser. Apple Safari and Mozilla Firefox are recommended. You can get a free copy of Safari from www.apple.com/safari/. You can get a free copy of Firefox from www.mozilla.org. Internet Explorer 8 and Google Chrome are not recommended because they sometimes have problems with Blackboard.
Adobe Acrobat Reader Version 8 or higher. Most computers come with Acrobat Reader already installed, but you can download a free copy from the Adobe Web Site, www.adobe.com.
Microsoft Excel. Computer exercises are to be completed in Excel. Drake students can get a free copy of Excel from the HelpDesk in the basement of Carnegie Hall. Excel for Windows has all the tools you need for this course, but you must install them. Excel 2008 for Mac does not have the tools you need. If you use Excel 2008 for Mac, you will need to download third-party tools. See www.drake.edu/cbpa/econ/boal/107/tools.html for instructions.

Course objectives:

After completing this course, students will be able

to compute two-variable least-squares estimates by hand;
to estimate population means and variances from a random sample, to form confidence intervals for them, and to test hypotheses about them;
to identify the desirable properties of least-squares estimators under the Gauss-Markov assumptions;
to estimate coefficients of two-variable or multiple regression equations by least-squares, to form confidence intervals for them, and to test hypotheses about them;
to compute least-squares predictions and, with the normality assumption, prediction intervals;
to interpret alternative functional forms for regression equations;
to recognize simple situations in which a regressor appears statistically significant without a causal relationship;
to detect and correct problems of heteroskedasticity in cross-section regression;
to interpret time-series regression models with trends and seasonal components;
to detect and correct serial correlation in time-series regression;
to identify the special properties of nonstationary time series;
to detect and correct problems of unit roots in time-series data, and to compute forecasts of time-series variables.

2. Requirements and grading

Course grade: All components of the course are graded on a range of zero to 100. The formula for the course grade is a weighted average of these components:

COURSE SCORE = 15% x Q + 5% x P + 10% x C + 35% x T + 35% x F

where: Q = average score on daily Quizzes; P = average score on daily Problems; C = average score on Computer exercises; T = average score on weekly Tests; F = Final exam score.

A course score of 92 or higher is required for an A, 84 or higher for a B, 76 or higher for a C, and 68 or higher for a D.

Daily quizzes: Every day, Monday through Thursday, a short on-line quiz must be completed before 11 PM. The quiz helps you review the key concepts for that day, and practice solving simple problems. You can retake the quiz as many as three times before 11 PM but the questions will change. Only your last score is recorded.

Daily problems: Every day, Monday through Thursday, students are assigned problems for solution and online discussion. Some students are required to post answers on the discussion board. Others are required to discuss and respond to the posted solutions. If the posted solution is incorrect, the discussant should give the correct solution. If the posted solution is correct, the discussant should explain intuitively why the posted solution makes sense, or show an alternate way of solving the problem.

Computer exercises: Computer exercises, requiring use of Excel, are frequently assigned to give you practice analyzing real data. In Blackboard, click on the "Computer Exercises" button to access the questions and data. Submit your Excel spreadsheet at the same place. Students must work independently on computer exercises. That means you MAY NOT GIVE AID, RECEIVE AID, OR SHARE FILES WITH ANYONE except me, the instructor. Violation of this rule may result in a zero on the assignment, a zero on the computer-exercises component of the course, or grade of "F" for the entire course.

Weekly tests: On the first five Fridays, all students must take an on-line test on the material for that week. Tests include both multiple-choice questions and problems.

Final examination: The final exam will be given on Thursday, August 18, 2011 from 9:00 to 10:50 AM in 101 Aliber Hall.

How to succeed in this course:

Check the prerequisites before enrolling. If you had difficulty in your statistics course, you are likely to have difficulty with this course. If you have forgotten statistics, you will have to work especially hard the first two weeks of the course.
Plan your time. Assignments are due each day. Work ahead if possible. Expect to spend at least 20 hours per week on this course.

Academic integrity: Drake University and the College of Business & Public Administration (CBPA) expect students to conduct themselves with academic integrity. The CBPA’s Academic Integrity Policy applies to this course. Cheating or plagiarism can result in a grade of zero for the test or assignment, a grade of F for the entire course, or even expulsion from the university, depending on the severity of the violation. Please read the policy and ask for clarification of any part that you do not understand.

3. Schedule of topics and textbook readings

Week 1: What is econometrics?

Monday: Introduction: - Read Wooldridge textbook: skim chapter 1.; - View slideshows on Blackboard: What is econometrics? Economic data sets.
Tuesday: The summation operator: - Read Wooldridge textbook: Appendix A sections A1 and A5.; - View slideshows on Blackboard: The summation operator. Derivatives of sums. Averages and weights
Wednesday: Fitting lines to data: - Read Wooldridge textbook: Chapter 2 appendix 2A, and Appendix A section A2.; - View slideshows on Blackboard: Definition of least-squares. Alternatives to least-squares.
Thursday: Random variables: - Read Wooldridge textbook: Appendix B sections B1-B2.; - View slideshows on Blackboard: Random variables. Joint distributions.
Friday: Weekly test 1

Week 2: Review of Probability and Statistics

Monday: Moments: - Read Wooldridge textbook: Appendix B sections B3-B4.; - View slideshows on Blackboard: Expected value or mean. Variance and standard deviation. Covariance, correlation, and conditional expectation.
Tuesday: Some important distributions: - Read Wooldridge textbook: Appendix B section B5.; - View slideshows on Blackboard: The Bernoulli distribution. The normal distribution. Distributions related to the normal distribution.
Wednesday: Samples and estimators: - Read Wooldridge textbook: Appendix C sections C1-C4.; - View slideshows on Blackboard: Random samples and estimators. Exact finite-sample properties of estimators. Asymptotic properties of estimators. Asymptotic normality. Reliable principles for finding good estimators.
Thursday: Confidence intervals and hypothesis tests: - Read Wooldridge textbook: Appendix C sections C5-C6.; - View slideshows on Blackboard: Standard errors. Confidence intervals. Basic concepts of hypothesis tests. Testing the mean of a distribution. P-values.
Friday: Weekly test 2

Week 3: Two-variable regression

Monday: Algebraic properties: - Read Wooldridge textbook: Chapter 2 sections 2.1-2.3.; - View slideshows on Blackboard: Algebraic properties of least-squares.
Tuesday: Properties under Gauss-Markov assumptions: - Read Wooldridge textbook: Chapter 2 section 2.5.; - View slideshows on Blackboard: Fundamental assumptions. Properties under fundamental assumptions. Additional useful assumptions. Properties under additional assumptions. Assymptotic confidence intervals and tests. Prediction with two-variable regression.
Wednesday: Properties under normally-distributed error terms: - Read Wooldridge textbook: (no reading).; - View slideshows on Blackboard: The assumption that the error terms are normally-distributed. Properties with normally-distributed error terms. Exact confidence intervals and tests. Prediction intervals. Summary of properties of least-squares estimators.
Thursday: Practical issues: - Read Wooldridge textbook: Chapter 2 section 2.4.; - View slideshows on Blackboard: Correlation versus causality. Units of measure for x and y. Slopes and elasticities. Alternative functional forms.
Friday: Weekly Test 3

Week 4: Multiple regression with cross-section data

LMonday: Algebraic properties with multiple regressors: - Read Wooldridge textbook: Chapter 3 sections 3.1-3.2.; - View slideshows on Blackboard: Why include more regressors? Definition of least-squares with two regressors. Algebraic properties of least-squares with multiple regressors. R² and adjusted R².
Tuesday: Properties under Gauss-Markov properties and normal error terms: - Read Wooldridge textbook: Chapter 3 sections 3.3-3.5, Chapter 4 sections 4.1-4.3, and chapter 6 sections 6.3-6.4.; - View slideshows on Blackboard: Fundamental assumptions and resulting LS properties. Additional assumptions and resulting LS properties. The normality assumption and resulting LS properties. Prediction and prediction intervals with multiple regression. The analysis-of-variance (ANOVA) table. Multicollinearity.
Wednesday: More tests and functional forms: - Read Wooldridge textbook: Chapter 4 sections 4.4-4.5, Chapter 6 section 6.2, and Chapter 7 sections 7.1-7.4.; - View slideshows on Blackboard: T-tests involving more than one coefficient. Tests of joint hypotheses. Alternative functional forms. Dummy variables. Structural change. Why are error terms sometimes correlated with regressors?
Thursday: Testing and correcting for heteroskedastic errors: - Read Wooldridge textbook: Chapter 8 sections 8.1-8.4.; - View slideshows on Blackboard: Heteroskedasticity: definition and consequences. Testing for heteroskedasticity: F-test and Goldfeld-Quandt test. Testing for heteroskedasticity: White test and Breusch-Godfrey test. Correcting for heteroskedasticity.
Friday: Weekly test 4

Week 5: Multiple regression with time-series data

Monday: Time-series regression with strictly exogenous errors: - Read Wooldridge textbook: Chapter 10 sections 10.1-10.3.; - View slideshows on Blackboard: Time series data and models. Properties of least-squares under classical assumptions.
Tuesday: Functional forms, trends, and seasonality: - Read Wooldridge textbook: Chapter 10 sections 10.4-10.5.; - View slideshows on Blackboard: Logarithms and dummy variables. Time trends. Seasonality.
Wednesday: Models of serial correlation: - Read Wooldridge textbook: Chapter 11 sections 11.1-11.2.; - View slideshows on Blackboard: Stationary and weakly dependent series. First-order moving-average process. First-order autoregressive process. Properties of least-squares without strict exogeneity.
Thursday: Testing and correcting for serially-correlated errors: - Read Wooldridge textbook: Chapter 12 sections 12.1-12.5.; - View slideshows on Blackboard: Autocorrelation: definition and consequences. Testing for serial correlation with strictly exogenous regressors. Testing for serial correlation without strictly exogenous regressors. Correcting for serial correlation.
Friday: Weekly test 5

Week 6: Highly persistent time series and forecasting

Monday: Models of highly persistent series: - Read Wooldridge textbook: Chapter 11 sections 11.3-11.5.; - View slideshows on Blackboard: Highly persistent time series. Least squares with highly persistent errors.
Tuesday: Testing for unit roots: - Read Wooldridge textbook: Chapter 18 sections 18.2-18.4.; - View slideshows on Blackboard: Spurious regression. Testing for unit roots. Cointegration. Error-correction models.
Wednesday: Forecasting: - Read Wooldridge textbook: Chapter 18 sections 18.5.; - View slideshows on Blackboard: The forecasting problem. One-step ahead forecasts. Vector autoregression and Granger causality. Multiple-step ahead forecasts. Forecasting trended and integrated processes.

Classroom final examination

The final exam will be given on Thursday, August 18, 2011 from 9:00 to 10:50 AM in 101 Aliber Hall. Students who will not be on campus on August 18 must make prior arrangement with the instructor for a makeup exam. The final exam is comprehensive and includes questions from all parts of the course. Books, notes, computers, cell phones, and internet devices are prohibited during the exam. Simple calculators are permitted, but not graphing calculators or calculators with alphabetical keyboards. Old exams are available for study on Blackboard (bb.drake.edu) under "Course documents" and at the course web page (www.drake.edu/cbpa/econ/boal/107).

[end of syllabus]