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

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Prototype

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* 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

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

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

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

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H0: residuals are homoskedastic H1: residuals are heteroskedastic

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* 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?

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

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

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

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ESS = SSR

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

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Obtain residual, then estimate

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* Find other references…

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Reparameterize before analize !

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

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

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* White suggests: * For RLB:

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

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