INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 111 4.1.3.3 Consistency
4.1.3.4 The Consequences of Measurement Errors
4.1.3.5 Measurement Errors in the Explanatory Variables Measurement Errors
in the Dependent Variable 4.1.4.0 Summary
4.1. 5.0 Conclusion
4.1.6.0
Tutor-Marked Assignment 4.1.7.0 References/Further Reading
4.1.1.0 INTRODUCTION The least squares regression model assumed that the explanatory
variables arenonstochastic, that is, that they do not have random components. Although relaxing this assumption does not in itself undermine the OLS regression technique, it is typically an unrealistic assumption, so it is important you know the consequences of relaxing it. We shall see that in some contexts
we can continue to use OLS, but in others, for example when one or more explanatory variables are subject to measurement error, it is a biased and inconsistent estimator.
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