* Heteroskedasticity * Serial correlation * Multicollinerity * Normality * Omitted variables.

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Presentation transcript:

* Heteroskedasticity * Serial correlation * Multicollinerity * Normality * Omitted variables

Prototype

* Error learning  misal: belajar mengetik * Sampel yang beragam  rumahtangga dgn pendptn, perusahaan berbagai level * Adanya outlier * Omitting variables * Sebaran data tidak normal * incorrect data transformation (e.g., ratio or first difference transformations) and * incorrect functional form (e.g., linear versus log–linear models) *  lebih sering terjadi pada data cross section

* BLUE? * Linear Unbiased but not efficient  LU Homoscedastic? Which is the Homoscedastic?

*B*B agaimana estimasi yg diperoleh terkait varians yg tidak konstan? *-*- Signifikansi ? *-*- CI ? ** misleading …

* Nature of problem (functional form review ) * Periksa Grafik residual * Tes statistik

H0: residuals are homoskedastic H1: residuals are heteroskedastic

* Goldfeld-Quandt Test: the heteroscedastic variance, σ 2 i, is positively related to one of the explanatory variables in the regression model, ex:  *  σ 2 i would be larger, the larger the values of Xi * Weakness: * - depend on which c is arbitrary, * - for X > 1 Var, which X is correct to be ordered?

* Y = Income, * X = Consumption, * n = 30, * c = 4

* Y = Income, X = Consumption, n = 30, c = 4

* Breusch–Pagan–Godfrey Test * Weakness: - large sample needed  for small sample, depend much on normality assumption Ex:  So, H0:  residuals are Homoskedastic

ESS = SSR

* White’s General Heteroscedasticity Test. * Weakness: more variables will consume more df. H0: residuals are homoskedastic Or H0: , df = # parameter -1

Obtain residual, then estimate

* Find other references…

Reparameterize before analize !

* Practically, run OLS first, then run: *  consistent estimator  large sample needed

* Run the following (weighted) regression: * Compare with the unweighted Apa perbedaan kedua model ini?

* White suggests: * For RLB:

* Pelajari Gujarati, Basic Econometrics, 14 th edition, * Ch. 11, section 11.7