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					Homoscedasticity

    equal error variance
• One of the assumption of OLS regression is that error 
  terms have a constant variance across all value so f 
  independent variable.
• If not heteroscadisticity.
• standard errors underestimated so t ratos are larger

• More common in cross sectional data than time 
  series data
• Heteroskedasticity implies that the variances (i.e.
  - the dispersion around the expected mean of
  zero) of the residuals are not constant, but that
  they are different for different observations. This
  causes a problem: if the variances are unequal,
  then the relative reliability of each observation
  (used in the regression analysis) is unequal. The
  larger the variance, the lower should be the
  importance (or weight) attached to that
  observation.
Homoscedastic disturbances.
Heteroscedastic disturbances.
               
• Note that the problem of heteroscedasticity is
  likely to be more common in cross-sectional than
  in time series data. In cross-sectional data, one
                 Detection of Heteroscedasticity

• Graphical methods: Looking for patterns in the plot
  of the predicted dependent variable and the residual

• Formal tests: One of the best is White’s general
  test for heteroscedasticity. If the graphical inspection
  hints at heteroskedasticity, you must conduct a
  formal test like the White’s test.
No problem
          Consequences of Using OLS in the Presence of
                      Heteroscedasticity
• OLS estimation still gives unbiased coefficient estimates, but they are
  no longer BLUE.

• This implies that if we still use OLS in         the presence of
  heteroscedasticity, our standard errors could be inappropriate and
  hence any inferences we make could be misleading.

• Whether the standard errors calculated using the usual formulae are
  too big or too small will depend upon the form of the
  heteroscedasticity.

• In the presence of heteroscedasticity, the variances of OLS estimators
  are not provided by the usual OLS formulas. But if we persist in
  using the usual OLS formulas, the t and F tests based on them can be
  highly mislead- ing, resulting in erroneous conclusions
                      solve
• Use logarithm of dependent variable

• Use other method than OLS
SPSS

				
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posted:7/24/2013
language:Latin
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