Learning Outcomes

In this chapter, you will learn how to

• Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation

• Explain the impact of heteroscedasticity or autocorrelation on the optimality of OLS parameter and standard error estimation

• Distinguish between the Durbin–Watson and Breusch–Godfrey tests for autocorrelation

• Highlight the advantages and disadvantages of dynamic models

• Test for whether the functional form of the model employed is appropriate

• Determine whether the residual distribution from a regression differs significantly from normality

• Investigate whether the model parameters are stable

• Appraise different philosophies of how to build an econometric model

• Conduct diagnostic tests in EViews

Introduction

Recall that five assumptions were made relating to the classical linear regression model (CLRM). These were required to show that the estimation technique, ordinary least squares (OLS), had a number of desirable properties, and also so that hypothesis tests regarding the coefficient estimates could validly be conducted. Specifically, it was assumed that:

1. E(ut) = 0

2. var(ut) = σ2 < ∞

3. cov(ui, uj) = 0

4. cov(ut, xt) = 0

5. ut ∼ N(0, σ2)

These assumptions will now be studied further, in particular looking at the following:

• How can violations of the assumptions be detected?

• What are the most likely causes of the violations in practice?

• […]