Some Properties of Tests for Specification Error in a Linear Regression Model

Abstract

This article considers the test reset which is intended to detect a nonzero mean of the disturbance in a linear regression model. Analysis of an approximation to the test statistic's distribution and Monte Carlo experiments reveal that the power of the test may decline as the size of the disturbance mean increases. However, the possibility is remote and declines with increasing sample size. Alternative sets of test variables are considered, and their effect on the power of the test is studied in Monte Carlo experiments. The best set seems to be composed of powers of the explanatory variables.

Key Words:

• Approximate doubly noncentral F distribution
• Incorrect functional form
• Monte Carlo experiment
• Omitted variables
• reset
• Specification-error tests