Violation of the assumption of homoscedasticity and detection of heteroscedasticity

Irena Đalić; Svetlana Terzić

doi:10.31181/dmame2104001d

Irena Đalić University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Republic of Srpska, Bosnia and Herzegovina
Svetlana Terzić University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Republic of Srpska, Bosnia and Herzegovina

DOI: https://doi.org/10.31181/dmame2104001d

Keywords: Economic phenomena; heteroskedasticity; homoskedasticity; random errors

Abstract

In this paper, it is assumed that there is a violation of homoskedasticity in a certain classical linear regression model, and we have checked this with certain methods. Model refers to the dependence of savings on income. Proof of the hypothesis was performed by data simulation. The aim of this paper is to develop a methodology for testing a certain model for the presence of heteroskedasticity. We used the graphical method in combination with 4 tests (Goldfeld-Quantum, Glejser, White and Breusch-Pagan). The methodology that was used in this paper showed that the assumption of homoskedasticity was violated and it showed existence of heteroskedasticity.

Downloads

Download data is not yet available.

References

Arbia, G. (2006). Spatial econometrics: statistical foundations and applications to regional convergence. Springer Science & Business Media.

Aue, A., Horváth, L., & F. Pellatt, D. (2017). Functional generalized autoregressive conditional heteroskedasticity. Journal of Time Series Analysis, 38(1), 3-21. DOI: https://doi.org/10.1111/jtsa.12192

Barreto, H., & Howland, F. (2006). Introductory econometrics: using Monte Carlo simulation with Microsoft excel. Cambridge University Press.

Baum, C., & Schaffer, M. (2019). IVREG2H: Stata module to perform instrumental variables estimation using heteroskedasticity-based instruments.

Brüggemann, R., Jentsch, C., & Trenkler, C. (2016). Inference in VARs with conditional heteroskedasticity of unknown form. Journal of econometrics, 191(1), 69-85. DOI: https://doi.org/10.1016/j.jeconom.2015.10.004

Cattaneo, M. D., Jansson, M., & Newey, W. K. (2018). Inference in Linear Regression Models with Many Covariates and Heteroskedasticity Supplemental Appendix. DOI: https://doi.org/10.1080/01621459.2017.1328360

Charpentier, A., Ka, N., Mussard, S., & Ndiaye, O. H. (2019). Gini Regressions and Heteroskedasticity. Econometrics, 7(1), 4.

Crudu, F., Mellace, G., & Sándor, Z. (2017). Inference in instrumental variables models with heteroskedasticity and many instruments. Manuscript, University of Siena.

Ferman, B., & Pinto, C. (2019). Inference in differences-in-differences with few treated groups and heteroskedasticity. Review of Economics and Statistics, 101(3), 452-467.

Halunga, A. G., Orme, C. D., & Yamagata, T. (2017). A heteroskedasticity robust Breusch–Pagan test for Contemporaneous correlation in dynamic panel data models. Journal of econometrics, 198(2), 209-230. DOI: https://doi.org/10.1016/j.jeconom.2016.12.005

Harris, D., & Kew, H. (2017). Adaptive long memory testing under heteroskedasticity. Econometric Theory, 33(3), 755-778. DOI: https://doi.org/10.1017/S0266466615000481

Im, K. S. (2000). Robustifying Glejser test of heteroskedasticity. Journal of Econometrics, 97(1), 179-188. DOI: https://doi.org/10.1016/S0304-4076(99)00061-5

Jovičić, M. (2011). Ekonometrijski metodi i modeli. Centar za izdavačku delatnost. Ekonomski fakultet. Beograd.

Kalina, J., & Peštová, B. (2017). Exact Inference in Robust Econometrics under Heteroscedasticity. 11th International Days of Statistics and Economics MSED 2017.[Proceedings.] Slaný: Melandrium, 636-645.Linton, O., & Xiao, Z. (2019). Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity. Journal of Econometrics, 213(2), 608-631.

Lütkepohl, H., & Netšunajev, A. (2017). Structural vector autoregressions with heteroskedasticity: A review of different volatility models. Econometrics and statistics, 1, 2-18. DOI: https://doi.org/10.1016/j.ecosta.2016.05.001

Lütkepohl, H., & Velinov, A. (2016). Structural Vector Autoregressions: Checking Identifying Long‐Run Restrictions via Heteroskedasticity. Journal of Economic Surveys, 30(2), 377-392. DOI: https://doi.org/10.1111/joes.12100

Mladenović, Z. (2011). Uvod u ekonometriju. Centar za izdavačku delatnost. Ekonomski fakultet. Beograd.

Mladenović, Z. i Nojković, A., (2017). Zbirka rešenih zadataka iz ekonometrije. Centar za izdavačku delatnost. Ekonomski fakultet. Beograd.

Mladenović, Z. i Petrović, P., (2017). Uvod u ekonometriju. Centar za izdavačku delatnost. Ekonomski fakultet. Beograd.

Moussa, R. K. (2019). Heteroskedasticity in One-Way Error Component Probit Models. Econometrics, 7(3), 35.

Ou, Z., Tempelman, R. J., Steibel, J. P., Ernst, C. W., Bates, R. O., & Bello, N. M. (2016). Genomic prediction accounting for residual heteroskedasticity. G3: Genes, Genomes, Genetics, 6(1), 1-13. DOI: https://doi.org/10.1534/g3.115.022897

Sato, T., & Matsuda, Y. (2017). Spatial autoregressive conditional heteroskedasticity models. Journal of the Japan Statistical Society, 47(2), 221-236. DOI: https://doi.org/10.14490/jjss.47.221

Taşpınar, S., Doğan, O., & Bera, A. K. (2019). Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models. Spatial Economic Analysis, 14(2), 241-268.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica: journal of the Econometric Society, 817-838. DOI: https://doi.org/10.2307/1912934