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.

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

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

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

a. Fourier Analysis

b. Spectral Densities

c. Filtering

d. Band Spectrum Analysis

e. Estimation and Inference

f. 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

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

a. Definition and Spectral Implications

b. Common Trends

c. Impulse Responses

d. Estimation and Inference

7. Nonlinear Time Series

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

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

c. Threshold Autoregressions