# ch14-3 - Chapter 14 Introduction to Time Series Regression...

This preview shows pages 1–4. Sign up to view the full content.

Chapter 14 Introduction to Time Series Regression and Forecasting (Part 3)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Problems Caused by Stochastic Trends If a regressor has a stochastic trend, the OLS t-statistic can have nonnormal distribution even in large samples. I The estimator of the autoregressive coefﬁcient in an AR(1) is biased toward 0 if its true value is 1 I t-statistics on regressors with a stochastic trends can have a nonnormal distribution, even in large samples I An extreme example of the risks posed by stochastic trends is that two series that are independent will misleadingly appear to be related if they both have stochastic trends (spurious regression)
Problem #1: Autoregressive coefﬁcients that are biased toward zero Suppose that Y t follows the random walk but this is unknown, and you estimate the AR(1) model. In this case, OLS b β 1 is consistent but the asymptotic distribution of b β 1 is shifted toward zero (biased estimator) If Y t follows a random walk, the forecast based on AR(1) can perform substantially worse.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 11/20/2011 for the course ECONOMICS 220:322 taught by Professor Otusbo during the Fall '10 term at Rutgers.

### Page1 / 9

ch14-3 - Chapter 14 Introduction to Time Series Regression...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online