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In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance. The spellings homoskedasticity and heteroskedasticity are also frequently used. Assuming a variable is homoscedastic when in reality it is heteroscedastic results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient.
References
- ↑ Gordon, Roger Hall (1976). Essays on the causes and equitable treatment of differences in earnings and ability (Ph.D.). MIT . Retrieved 21 January 2017.
- ↑ Specification testing and estimation using a generalized method of moments
- ↑ Kling, Jeffrey Richard (1998). Identifying causal effects of public policies (Ph.D.). MIT. hdl:1721.1/10114.
- ↑ Nonparametric functional estimation with applications to financial models
- ↑ Jerry A. Hausman at the Mathematics Genealogy Project
- ↑ "Theoretical and empirical aspects of vintage capital models". British Library EThOS. Retrieved 17 May 2013.
