Ekonometrika 1 Ekonomi Pembangunan Universitas Brawijaya.
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Transcript of Ekonometrika 1 Ekonomi Pembangunan Universitas Brawijaya.
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Ekonometrika 1Ekonomi Pembangunan
Universitas Brawijaya
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WHAT IS THAT..?
Assumption of the classical linear regression model (CLRM) is that there is no multicollinearity among the regressors included in the regression model.
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THE NATURE OF MULTICOLLINEARITY The term multicollinearity is due to
Ragnar Frisch. Originally it meant the existence of a “perfect,” or exact, linear relationship among some or all explanatory variables of a regression model
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Look at this picture..
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NEXT..
Why does the classical linear regression model assume that there is no multicollinearity among the X’s?
If multicollinearity is perfect in the sense of the regression coefficients of the X variables are indeterminate and their standard errors are infinite.
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THERE ARE SEVERAL SOURCES OF MULTICOLLINEARITY Constraints on the model or in the
population being sampled Model specification An overdetermined model
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PRACTICAL CONSEQUENCES OF MULTICOLLINEARITY Although BLUE, the OLS estimators have
large variance and covariance, making precise estimation difficult.
The confidence intervals tend to be much wider.
The t-ratio of one or more coefficients tend to be statistically insignificant.
R-square can be very high The OLS estimators and their standard
errors can be sensitive to small changes in the data
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DETECTION OF MULTICOLLINEARITY High R-square but few significant t-ratio High pair-wise correlation among
regressors Examination of partial correlations
(Farrar and Glauber) Auxiliary regressions (Fi) Klein’s rule of thumb (R2 aux; overall R2) Eigenvalues and condition index Tolerance and variance inflation factor
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REMEDIAL MEASURES
A priori information Combining cross-sectional and time-series
data Dropping a variable(s) and specification bias Transformation of variables Additional or new data Reducing collinearity in polynomial
regressions Factor analysis, principal component and
ridge regression
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