Economics 4151
Applied Econometrics (Macroeconomics Module)
Spring 2010
Instructor: James Morley
Office: Seigle Hall 383
Office Hours: TTh 1:30-2:30
Email: morley@wustl.edu
Course Description
Introduction to econometrics as it is applied in microeconomics and macroeconomics (modular). Emphasis is on hands-on implementation of the models covered in the course. Topics related to the analysis of microeconomic data include cross-section and panel data linear models and robust inference; instrumental variables estimation; simultaneous equation models; models for discrete choice; and truncation, censoring and sample selection models. Topics related to the analysis of macroeconomic data include linear time series models; practical issues with likelihood-based inference; forecasting; structural identification based on timing restrictions; and computational methods for hypothesis testing.
Grading
The course has two modules. Your course grade is based on an average of your grade in each module.
Microeconomics Module50%
Macroeconomics Module 50%
You will be assigned numerical scores on your homework assignments and take-home exams. Your scores will be converted to an overall percentage grade. The letter grade for the course will be determined by converting your percentage score according to the following letter grade distribution:
A+ 95-100% B+ 80-84%C+ 67-69%D+ 57-59%F 0-49%
A 90-94% B 75-79%C 63-66% D 53-56%
A- 85-89%B- 70-74%C- 60-62% D- 50-52%
Requirements
There will be weekly homework assignments and a take-home exam at the end of the module. The weights in determining the grade for the macroeconomics module are given as follows:
Homework Assignments 40%
Take-Home Exam 60%
Readings
The required readings for the macroeconomics portion of the course will be collected in a reading package that will be available for purchase from the main office of the Economics Department. The readings (listed under topics below) include journal articles and chapters from the following textbooks:
Introduction to Econometrics, by James Stock and Mark Watson, 2003.
Econometric Theory and Methods, by Russell Davidson and James G. MacKinnon, Oxford University Press, 2004.
Time Series Analysis, by James D. Hamilton, Princeton University Press, 1994.
Topics
1. Time Series Data
Serial Correlation
Trends and Breaks
Methodology
(Readings: Stock and Watson, Ch. 12; Hansen, 2001; Hoover, 2001; Sims, 1996)
2. Basic Models
•ARMA Models
•Single-equation Dynamic Models
•GARCH Models
•VAR Models
(Readings: Stock and Watson, Ch. 12; Davidson and MacKinnon, Ch. 13)
3. Inference
•Maximum Likelihood Estimation
•The Kalman Filter and the Prediction Error Decomposition
•Numerical Optimization
•Asymptotic and Bootstrap Methods for Hypothesis Tests and Confidence Intervals
(Readings: Hamilton, Ch. 5; MacKinnon, 2006)
4. Forecasting
•Forecast Evaluation and Loss Functions
(Readings: Hamilton, Ch. 4; Diebold and Mariano, 1995; Elliott and Timmermann, 2004)
5. Structural Analysis
•Granger Causality
•Cointegration
•Impulse Response Functions
•Timing Restrictions
•Identification through Heteroskedasticity
(Readings: Diebold, 1998; Granger, 2004; Stock and Watson, 14.4, 2001; Hamilton, 11.6; Blanchard and Quah, 1989; Gravelle, Kichian, and Morley, 2006)
Syllabus