Applied Time Series Econometrics

 

Introduction

This is a course in applied time series econometrics for Ph.D. students that have completed Econometrics I and II. There is no single text, but I will refer to material in “Time Series Analysis”' by James Hamilton, and Fumio Hayashi’s “Econometrics”.

 

Course requirements

I will assign homework (20%), and there will be a take-home final exam (80%).

 

Reaching Me

My office is 565 in the Economics Department; phone, 7-1570. Office hours are by appointment.

 

Final Exam 2009 Data File

 

Outline

1.      Linear Time Series (Reading: Hamilton: Chs. 1 – 4, Hayashi Ch. 6)

a.      ARMA Models

b.      Lag operators

c.       Auto Correlation and Autocovariance Functions

d.      Prediction and Impulse Response Functions

e.      State Space Representations

f.       Stationarity and Wold’s Theorem

Problem set 1 data

 

2.      Estimation (Reading  Hamilton: Chs. 5 & 13)

a.      Conditional/Unconditional Maximum Likelihood

b.      The Kalman Filter

 

3.      VARs (Reading  Hamilton: Ch. 11)

a.      Identification

b.      Estimation and Inference

c.       Granger Causality

d.      Present Value Restrictions

e.      Application: Cochrane (1998), “What to the VARs mean? Measuring the output effects of monetary policy”' Journal of Monetary Economics.

f.        Application: Campbell, J. Y. and R. J. Shiller (1989), “The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors,”' Review of Financial Studies.

Problem set 3 data

 

 

4.      Spectral Representations (Reading  Hamilton: Ch. 6)

a.      Fourier Analysis

b.      Spectral Densities

c.       Filtering

d.      Band Spectrum Analysis

e.      Estimation and Inference

f.       Application: The H-P filter

 

5.      Unit Roots (Reading  Hamilton: Ch. 17, Hayashi Ch. 9)

a.      Spectral Implications

b.      Spurious Regression

c.       The Beverage-Nelson Decomposition

d.      Distribution Theory for I(1) processes

e.      Unit Root Tests

f.        Application: Campbell and Perron, (1991), “Pitfalls and Opportunities: What Macroeconomists should know about Unit Roots”' NBER Macroeconomics Annual

 

6.      Cointegration (Reading  Hamilton: Ch. 19, Hayashi Ch. 10)

a.      Definition and Spectral Implications

b.      Common Trends

c.       Impulse Responses

d.      Estimation and Inference

e.      Application: King, Plosser, Stock and Watson, (1991), “Stochastic Trends and Economic Fluctuations,”' American Economic Review.

f.        Application: Campbell and Shiller, (1987) “Cointegration Tests of Present Value Models,” Journal of Political Economy.

 

7.      Nonlinear Time Series

a.      Markov Switching Models (Reading  Hamilton: Ch. 22)

b.      Conditional Heteroskedasticity (Reading  Hamilton: Ch. 21)

c.       Threshold Autoregressive

d.      Application: Bollerslev, Chou and Kroner (1990), “ARCH Modeling in Finance,” manuscript, Kellogg School Northwestern University.

e.      Application: Evans, Martin D. (1998) “Dividend Variability and Stock Market Swings”' Review of Economic Studies.