Learning Outcomes In this chapter, you will learn how to ● Derive the OLS formulae for estimating parameters and their standard errors ● Explain the desirable properties that a good estimator should have ● Discuss the factors that affect the sizes of standard errors ● Test hypotheses using the test of significance and confidence interval approaches ● Interpret p-values ● Estimate regression models and test single hypotheses in EViews
2 A brief overview of the classical linear regression model 27 2.1 What is a regression model? 27 2.2 Regression versus correlation 28 2.3 Simple regression 28 2.4 Some further terminology 37 2.5 Simple linear regression in EViews -- estimation of an optimal hedge ratio 40 2.6 The assumptions underlying the classical linear regression model 43 2.7 Properties of the OLS estimator 44 2.8 Precision and standard errors 46 2.9 An introduction to statistical inference 51
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