Economics 483
Applied Econometric Modeling

Eric Zivot
Office Hours: WF 9-10
Savory 310F or by appointment
Phone. 543-6715
E-Mail: ezivot@u.washington.edu

Course Description:

This is a new course in applied econometric modeling. This course is not a continuation of econ 482 (econometric theory), which concentrates on the statistical theory behind econometrics, but rather is a course on the application of economic modeling and econometric techniques. The main topics we will cover are (1) probability modeling; (2) data analysis and regression and (3) forecasting. Each topic will be discussed in the context of particular economic examples. We will illustrate probability modeling using portfolio theory and option pricing, data analysis and regression using the capital asset pricing theory (CAPM) and economic forecasting using the demand for transportation services on the NY subway and the Long Island Rail Road. We will use the EVIEWS statistics software program and Microsoft EXCEL spreadsheet program for the computer labs.

Course requirements:

There is no midterm or final exam! The computer labs comprise the core of the course and have been weighted accordingly for grading purposes. After each lab has been completed, it will be discussed in class by selected individuals.

The computer labs will take you through the practice of econometrics. I believe that one cannot obtain an adequate knowledge and appreciation of econometrics without "getting one's hands dirty" in the computer lab.

Required Texts

Recommended Texts

Course Outline and Reading List

Last updated: May 11, 1997.

Week 1: Review of Random Variables and Introduction to Portfolio Statistics

Reading

  1. CAPM Tutor, chapter 1 (The investment problem), 2 (Portfolio statistics) and 3 (Naive diversification).
  2. Mirer, Economic Statistics and Econometrics, chapter 9 (Random Variables and Probability Distributions) - in reading packet.
  3. Modern Investment Theory by Robert Haugen, chapter 3 (Some Statistical Concepts) - in reading packet.
  4. Corporate Finance by Ross, Westerfield and Jaffee, chapter 9 (Capital Market Theory: An Overview) - in reading packet.

Week 2: Portfolio Theory

Reading

  1. CAPM Tutor, chapter 4 (Markowitz Diversification), chapter 6 (CAPM) and chapter 7 (Capital Market Equilibrium).
  2. Corporate Finance by Ross, Westerfield and Jaffee, chapter 10 (Return and Risk: The CAPM) - in reading packet.

Week 3: Portfolio Theory and the CAPM

Reading

  1. CAPM Tutor, chapter 4 (Markowitz Diversification), chapter 6 (CAPM) and chapter 7 (Capital Market Equilibrium).
  2. Corporate Finance by Ross, Westerfield and Jaffee, chapter 10 (Return and Risk: The CAPM) - in reading packet.

Week 4: Review of Basic Data Analysis and Descriptive Statistics

Reading

  1. Mirer, Economic Statistics and Econometrics, chapter 1 (appendix - functions and graphs), chapter 2 (economic data), chapter 3 (descriptive statistics), chapter 4 (Frequency Distributions) and chapter 10 (the normal and t-distributions).
  2. CFA handbook, chapter 7 (regression, or how to express the relationships among variables).

Week 5: The CAPM and Simple Linear Regression

Reading

  1. CFA handbook, chapter 7 (regression, or how to express the relationships among variables).
  2. CAPM Tutor, chapter 8 (index models).
  3. E. Berndt, The Practice of Econometrics, chapter 2 (The CAPM: An application of bivariate regression analysis).
  4. Makiel, A Random Walk Down Wall Street, chapters 9 and 10.

Week 6: Statistical Inference with the Simple Linear Regression Model and Introduction to Forecasting

Reading

  1. Pepper, Business Fluctuations, chapters 4 and 5 (in second reading packet)

Week 7: Forecasting Ridership and Revenue on the New York City Subway

Reading

  1. The Development of Revenue Forecasting Models for Agencies of the MTA, 1991 Annual Report, Economic Analysis Group, Budget Division, MTA (in second reading packet)
  2. The Development of Revenue Forecasting Models for the MTA Agencies, 1992 Annual Report. (in second reading packet)
  3. Caliper corportation Report on Evaluation of NYCTA Revenue Forecasting Models, 1993. (in second reading packet)
  4. New York City Transit Authority Revenue Feasibility Study: Economic analyses and Projections, 1982, prepared by Charles Rivers Associates.

Week 8: Forecasting Using the Multiple Regression Model

Reading:

  1. Caliper corportation Report on Evaluation of NYCTA Revenue Forecasting Models, 1993. (in second reading packet)
  2. Pepper, Business Fluctuations, chapter 6 (Multiple Regression Models); chapter 7 (Advanced Topics in Regression Analysis), pages 289-312 (Dummy variables and log transformations)

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