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3.7 Multicollinearity ‘Perfect’ case is of no interest  easily detectable

3.7 Multicollinearity ‘Perfect’ case is of no interest  easily detectable Consequences of quasi-perfect multicollinearity : Larger VAR & errors Larger confidence intervals (less precision) Non-significant t High r 2 but few significant t ’s LS very sensitive to changes in data

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3.7 Multicollinearity ‘Perfect’ case is of no interest  easily detectable

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  1. 3.7 Multicollinearity ‘Perfect’ case is of no interest  easily detectable Consequences of quasi-perfect multicollinearity : • Larger VAR & errors • Larger confidence intervals (less precision) • Non-significant t • High r2 but few significant t’s • LS very sensitive to changes in data • Wrong sign of some coefficients • Individual contribution not easy to assess How to detect? • High r2 and few significant t • High correlation among variables (r > 0.8)

  2. 3.8 Relaxing the CLRM basic assumptions • Before: errors cancel out (exogeneity). Now: they don’t, and will affect the dependent variable Consequence : endogeneity  LS is biased Hausman test; alternative: 2SLS (IV) 2. Before: same dispersion of errors (Homoskedasticity) Now: different dispersion (Heteroskedasticity) Consequence: inefficiency of LS  larger variances/errors White test; alternative: GLS 3. Before: no autocorrelation of errors (no serial correlation) Now: autocorrelation of errors Consequence : inefficiency of LS  larger variances/errors Durbin-Watson test ; alternative: GLS

  3. 3.8 Relaxing the CLRM basic assumptions • Before: normality of errors Now: absence of normality Consequence: hypothesis tests NOT valid Jarque-Bera test; central limit theorem • Before: absence of multicollinearity Now: multicollinearity Consequence: can’t calculate (perfect multicollinearity) or previous difficulties (quasi-perfect multicollinearity) Alternative: re-specify the model

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