1 / 33

KULIAH 11

Heteroskedasticity Serial correlation Multicollinerity Normality Omitted variables. KULIAH 11. What’s Heteroskedasticity ?. Varians residual tdk konstan. Prototype. Penyebab. Error learning  misal : belajar mengetik

babu
Download Presentation

KULIAH 11

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Heteroskedasticity • Serial correlation • Multicollinerity • Normality • Omitted variables KULIAH 11

  2. What’s Heteroskedasticity?

  3. Varians residual tdkkonstan Prototype

  4. Penyebab • Error learning  misal: belajarmengetik • Sampel yang beragam  rumahtanggadgnpendptn, perusahaanberbagai 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) •  lebihseringterjadipada data cross section

  5. Efekthdestimasi • BLUE? • Linear Unbiased but not efficient  LU Which is the Homoscedastic? Homoscedastic?

  6. KOnsekuensi • Bagaimanaestimasiygdiperolehterkaitvariansygtidakkonstan? • - Signifikansi ? • - CI ? •  misleading …

  7. Mendeteksiheteroskedasticity • Nature of problem (functional form review ) • PeriksaGrafik residual • Tesstatistik

  8. TesStatistik • Bahwa residual berkorelasidenganvarians • Park Test •  signifikan residuals are heteroskedastic •  weakness: may not satisfy the OLS assumptions and may itself be heteroscedastic • Glejser Test •  weakness: the error term vi has some problems in that its expected value is nonzero, it is serially correlated and ironically it is heteroscedastic, some models are non linear.

  9. Ex: Park & Glejser test

  10. H0: residuals are homoskedastic H1: residuals are heteroskedastic

  11. Goldfeld-QuandtTest: the heteroscedastic variance, σ2i , is positively related to one of the explanatory variables in the regression model, ex:  •  σ2i 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?

  12. Ex: • Y = Income, • X = Consumption, • n = 30, • c = 4

  13. Ex: • Y = Income, X = Consumption, n = 30, c = 4

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

  15. ESS = SSR

  16. Ex:

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

  18. Koenker–Bassett (KB) test. • H0: residuals are homoskedastic • Or H0: • Teshipotesis using t-test Obtain residual, then estimate

  19. Other tests….. • Find other references…

  20. Remedial Perhatikan1 & 2 Reparameterize before analize !

  21. Reparameterize before analize !

  22. Practically, run OLS first, then run: •  consistent estimator  large sample needed

  23.  measure the elasticity

  24. Other Remedial Procedure

  25. Run the following (weighted) regression: • Compare with the unweighted Apaperbedaankedua model ini?

  26. White suggests: • For RLB:

  27. Important notes

  28. Tugas Bonus • Pelajari Gujarati, Basic Econometrics, 14thedition, • Ch. 11, section 11.7

More Related