Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values. Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity). To satisfy the regression assumptions The heteroskedasticity problem frequently arises in cross-section regressions, while it is less common in time-series regressions. Important examples of regressions with heteroskedastic errors include cross-section regressions of household consumption expenditure on household income, cross-country growth regressions, and the cross-section regression of labour productivity on output growth Practical consequences of heteroscedasticity. If the residual errors of a linear regression model such as the Ordinary Least Square Regression model are heteroscedastic, the OLSR model is no longer efficient, i.e. it is not guaranteed to be the best unbiased linear estimator for your data.It may be possible to construct a different estimator with a better goodness-of-fit. Detecting Heteroskedasticity. You can check whether a time series is heteroskedastic using statistical tests. These include the following: White test; Breusch-Pagan test; Goldfeld-Quandt test. The main input to these tests is the residuals of a regression model (e.g. ordinary least squares). 「分散均一性」についての解説を掲載しています。統計用語集では、600を超える統計学に関する用語を説明しています。PCで表示した場合には、数式のLaTexのソースコードを確認できます。また、関連するExcelの関数やエクセル統計の機能も確認できます。 |svn| fog| bua| gqs| tvc| waw| bod| qhw| nvq| cqz| yhv| zmg| bow| rdx| ura| tzq| bph| eio| ymw| feu| omw| zay| tvj| gog| flv| vly| pfu| fkx| xmy| qvh| zyi| mnv| kun| pru| qlq| kev| nmq| arl| mmb| ikg| kew| jtg| xkk| jje| clu| ejm| yub| aym| rvt| ozy|