Session7

Session7 - Finance 271 Session 7 Financial Modeling and...

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Finance 271 Session 7 Financial Modeling and Econometrics Philip W. Wirtz The George Washington University
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Administrivia
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Administrivia Midterm: in-class and online (Wednesday) protocol
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Homoskedasticity in Regression: An Introduction 375 325 350 Price 50 275 300 Sale P 225 250 400 500 600 700 800 900 1000 Square Footage ssumption: Disturbances ave the same variance Assumption: Disturbances u have the same variance
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Heteroskedasticity in Regression: An Example 50 375 300 325 350 e Price 250 275 Sal e 225 400 500 600 700 800 900 1000 Square Footage
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Prototypical Heteroskedastic Situation
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Prototypical Heteroskedastic Situation Error-learning models: As people learn, their errors of behavior become smaller over time.
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Prototypical Heteroskedastic Situation Error-learning models: As people learn, their errors of behavior become smaller over time. As incomes grow, people have more discretionary income and hence more scope for choice about the disposition of their income; as a sult, a regression of savings on income would likely becharacterized result, a regression of savings on income would likely becharacterized by larger variance in at higher income levels.
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Prototypical Heteroskedastic Situation Error-learning models: As people learn, their errors of behavior become smaller over time. As incomes grow, people have more discretionary income and hence more scope for choice about the disposition of their income; as a sult, a regression of savings on income would likely becharacterized result, a regression of savings on income would likely becharacterized by larger variance in at higher income levels. ividend ayout ratio is likely to be more variable among companies Dividend-payout ratio is likely to be more variable among companies that are more growth oriented.
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Heteroskedasticity, Desirable Properties of Estimators, and Detection
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Heteroskedasticity, Desirable Properties of Estimators, and Detection Heteroskedasticity and desirable properties:
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Heteroskedasticity, Desirable Properties of Estimators, and Detection Heteroskedasticity and desirable properties: Heteroskedasticity does not destroy the unbiasedness and consistency properties of OLS estimators.
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Heteroskedasticity, Desirable Properties of Estimators, and Detection Heteroskedasticity and desirable properties: Heteroskedasticity does not destroy the unbiasedness and consistency properties of OLS estimators. However, OLS esimators are no longer minimum variance or efficient in the context of heteroskedasticity.
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Heteroskedasticity, Desirable Properties of Estimators, and Detection Heteroskedasticity and desirable properties: Heteroskedasticity does not destroy the unbiasedness and consistency properties of OLS estimators. However, OLS esimators are no longer minimum variance or efficient in the context of heteroskedasticity.
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This note was uploaded on 02/29/2012 for the course FINA 6271 taught by Professor Phillipwirtz,refiksoyer during the Fall '11 term at GWU.

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Session7 - Finance 271 Session 7 Financial Modeling and...

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