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Heteroskedasticity and Neglected Parameter Heterogeneity

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  • Joachim Zietz

Abstract

The paper studies the consequences of neglecting parameter heterogeneity for the linear regression model and cross‐sectional data. Monte‐Carlo experiments are used to illustrate that neglected parameter heterogeneity typically leads to (a) regression coefficients that are economically meaningless and (b)significant test statistics for heteroskedasticity and, possibly non‐normality. The paper concludes that evidence for heteroskedasticity should not routinely lead to the use of White's well‐known heteroskedasticity‐consistent variance covariance matrix estimator. If heteroskedasticity is caused by neglected parameter heterogeneity or other causes of heteroskedasticity, such as wrong functional form, White's estimator will not serve any useful purpose.

Suggested Citation

  • Joachim Zietz, 2001. "Heteroskedasticity and Neglected Parameter Heterogeneity," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 63(2), pages 263-273, May.
    • Handle: RePEc:bla:obuest:v:63:y:2001:i:2:p:263-273
    DOI: 10.1111/1468-0084.00220
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    Cited by:

    1. Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
    2. P. Dorian Owen, 2017. "Evaluating Ingenious Instruments for Fundamental Determinants of Long-Run Economic Growth and Development," Econometrics, MDPI, vol. 5(3), pages 1-33, September.
    3. Joachim Zietz, 2006. "Detecting neglected parameter heterogeneity with Chow tests," Applied Economics Letters, Taylor & Francis Journals, vol. 13(6), pages 369-374.
    4. Joachim Zietz & Bobby Newsome, 2002. "Agency Representation and the Sale Price of Houses," Journal of Real Estate Research, Taylor & Francis Journals, vol. 24(2), pages 165-192, January.

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