'Irrelevant variables' diaporamas de présentation

Advanced Views: XML-Relational Storage and Datalog

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Linear Regression Basics III Violating Assumptions

Fin250f: Lecture 7.2 Spring 2010 Brooks, chapter 4(skim) 4.1-2, 4.4, 4.5, 4.7, 4.9-13. Linear Regression Basics III Violating Assumptions. Outline. Violating assumptions Parameter stability Model building. OLS Assumptions. Error variances Error correlations Error normality

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Further Inference in the Multiple Regression Model

Further Inference in the Multiple Regression Model. Hill et al Chapter 8. The F-Test. Used to test hypotheses on one or more parameters. Unrestricted model:. Restricted model. The F-statistic. Are the differences in SSE significant?.

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Problems with Incorrect Functional Form

Problems with Incorrect Functional Form. You cannot compare R 2 between two different functional forms. Why? TSS will be different. One should also remember that an incorrect functional form may work within sample but have large forecast errors outside of sample. Linear Functional Form.

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Linear Functional Form

Linear Functional Form. Y = ? 0 + ? 1 X 1 + ? 2 X 2 + ? Slope = ? 1 Impact of X 1 on Y is independent of the quantity of X 2 . Elasticity = ? 1 * [X 1/ Y]. Double-Log Functional Form. What if you wished to estimate the following model? Y = ? 0 X 1 ?1 X 2 ?2

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Assumption MLR.3 Notes (No Perfect Collinearity)

Assumption MLR.3 Notes (No Perfect Collinearity). Perfect Collinearity can exist if: One variable is a constant multiple of another Logs are used inappropriately One variable is a linear function of two or more other variables

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Assumption MLR.3 Notes (No Perfect Collinearity)

Assumption MLR.3 Notes (No Perfect Collinearity). Perfect Collinearity can exist if: One variable is a constant multiple of another Logs are used inappropriately One variable is a linear function of two or more other variables

170 views • 16 slides