# Test tends to have low power fail to reject h0

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Unformatted text preview: Model B Detection White Test This test is much more general. It looks for evidence of an association between variance of disturbance term and regressors. H0 : Homoscedasticity (σui =σu ) H1 : Heteroscedasticity (σui changes with X’s) Mechanics: 1 Run OLS and save residuals. 2 Regress squared residual on explanatory variables, their squares and cross-products → get R 2 from this second regression. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Detection White Test This test is much more general. It looks for evidence of an association between variance of disturbance term and regressors. H0 : Homoscedasticity (σui =σu ) H1 : Heteroscedasticity (σui changes with X’s) Mechanics: 1 Run OLS and save residuals. 2 Regress squared residual on explanatory variables, their squares and cross-products → get R 2 from this second regression. 3 Under H0 , nR 2 ∼ χ2k −1) where k is the number of parameters estimated ( in above step 2 regression. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Detection White Test This test is much more general. It looks for evidence of an association between variance of disturbance term and regressors. H0 : Homoscedasticity (σui =σu ) H1 : Heteroscedasticity (σui changes with X’s) Mechanics: 1 Run OLS and save residuals. 2 Regress squared residual on explanatory variables, their squares and cross-products → get R 2 from this second regression. 3 4 Under H0 , nR 2 ∼ χ2k −1) where k is the number of parameters estimated ( in above step 2 regression. Intuition for White Test: Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Detection White Test This test is much more general. It looks for evidence of an association between variance of disturbance term and regressors. H0 : Homoscedasticity (σui =σu ) H1 : Heteroscedasticity (σui changes with X’s) Mechanics: 1 Run OLS and save residuals. 2 Regress squared residual on explanatory variables, their squares and cross-products → get R 2 from this second regression. 3 4 Under H0 , nR 2 ∼ χ2k −1) where k is the number of parameters estimated ( in above step 2 regression. Intuition for White Test: Squared residual proxies for disturbance variance. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Detection White Test This test is much more general. It looks for evidence of an association between variance of disturbance term and regressors. H0 : Homoscedasticity (σui =σu ) H1 : Heteroscedasticity (σui changes with X’s) Mechanics: 1 Run OLS and save residuals. 2 Regress squared residual on explanatory variables, their squares and cross-products → get R 2 from this second regression. 3 4 Under H0 , nR 2 ∼ χ2k −1) where k is the number of parameters estimated ( in above step 2 regression. Intuition for White Test: Squared residual proxies for disturbance variance. 2 If σui related to regressors, R 2 will tend to be large. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Detection White Test T...
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## This document was uploaded on 03/12/2014 for the course ECON 202 at University of London University of London International Programmes (Distance Learning).

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