ArtsAutosBooksBusinessEducationEntertainmentFamilyFashionFoodGamesGenderHealthHolidaysHomeHubPagesPersonal FinancePetsPoliticsReligionSportsTechnologyTravel

Heteroskedasticity And Homoskedasticity

Updated on May 19, 2012

Both hetereoskedasticity and homoskedasticity are statistical terms used in econometric regression models. They are complementary notions. Both are useful to derive statistical pattern recognition and machine learning algorithms. Neither cause bias, but can indicate when bias is present, and may indicate unknown variables not being account for, may need to be accounted for.

Hetereoskedasticity

Hetereoskedasticity is the variance of the error term, given the explanatory (e.g dependent variable - variable to be explained in a multiple regression model) variables, is not constant. Hetereoskedasticity is an assumption that the variance of the unobservable, in an OLS (ordinary least squares) regression or other regression model, conditional on X, is not constant. Hetereoskedasticity is also known as the 'non-constant variance'.

Hetereoskedasticity fails whenever the variance of unobervables changes across different segments of the population, where the segments are determined by different values of explanatory variables. An example, in a savings equation, heteroskedasticity is present if the variance of the unobserved factors affecting savings increases with income.

Heteroskedasticity is a measure used by econometricians to infer whether an OLS regression is useful. If heteroskedasticity is present it indicates that bias (difference between the expected value of an estimator and the population value that the estimator is supposed to be estimating) could be influencing the regression. Heteroskedasticity does not create bias, but rather indicates it may be present.

Econometricians have learned to adjust standard erros, t, F and LM statistics so they are valid in the presence of heteroskedasticity of unknown form (hetereoskedasticity that may depend on the explanatory variables in an unknown, arbitary form). These methods are valid whether or not the errors have a constant variance, at least in large samples, and are known as heteroskedasticity-robust procedures.

Homoskedasticity

Homoskedasticity is defined as the errors in a regression model that have constant variance conditional on the explanatory variables. This assumption states that the variance of the unobservable, u, conditional on x, is constant. Homoskedasticity is also known as the 'constant variance'. This is distinct from from the zero conditional mean assumption, as there is a difference between the expected value of u and the variance of u. Homoskedasticity also plays no role in determining biasness.

working

This website uses cookies

As a user in the EEA, your approval is needed on a few things. To provide a better website experience, hubpages.com uses cookies (and other similar technologies) and may collect, process, and share personal data. Please choose which areas of our service you consent to our doing so.

For more information on managing or withdrawing consents and how we handle data, visit our Privacy Policy at: https://corp.maven.io/privacy-policy

Show Details
Necessary
HubPages Device IDThis is used to identify particular browsers or devices when the access the service, and is used for security reasons.
LoginThis is necessary to sign in to the HubPages Service.
Google RecaptchaThis is used to prevent bots and spam. (Privacy Policy)
AkismetThis is used to detect comment spam. (Privacy Policy)
HubPages Google AnalyticsThis is used to provide data on traffic to our website, all personally identifyable data is anonymized. (Privacy Policy)
HubPages Traffic PixelThis is used to collect data on traffic to articles and other pages on our site. Unless you are signed in to a HubPages account, all personally identifiable information is anonymized.
Amazon Web ServicesThis is a cloud services platform that we used to host our service. (Privacy Policy)
CloudflareThis is a cloud CDN service that we use to efficiently deliver files required for our service to operate such as javascript, cascading style sheets, images, and videos. (Privacy Policy)
Google Hosted LibrariesJavascript software libraries such as jQuery are loaded at endpoints on the googleapis.com or gstatic.com domains, for performance and efficiency reasons. (Privacy Policy)
Features
Google Custom SearchThis is feature allows you to search the site. (Privacy Policy)
Google MapsSome articles have Google Maps embedded in them. (Privacy Policy)
Google ChartsThis is used to display charts and graphs on articles and the author center. (Privacy Policy)
Google AdSense Host APIThis service allows you to sign up for or associate a Google AdSense account with HubPages, so that you can earn money from ads on your articles. No data is shared unless you engage with this feature. (Privacy Policy)
Google YouTubeSome articles have YouTube videos embedded in them. (Privacy Policy)
VimeoSome articles have Vimeo videos embedded in them. (Privacy Policy)
PaypalThis is used for a registered author who enrolls in the HubPages Earnings program and requests to be paid via PayPal. No data is shared with Paypal unless you engage with this feature. (Privacy Policy)
Facebook LoginYou can use this to streamline signing up for, or signing in to your Hubpages account. No data is shared with Facebook unless you engage with this feature. (Privacy Policy)
MavenThis supports the Maven widget and search functionality. (Privacy Policy)
Marketing
Google AdSenseThis is an ad network. (Privacy Policy)
Google DoubleClickGoogle provides ad serving technology and runs an ad network. (Privacy Policy)
Index ExchangeThis is an ad network. (Privacy Policy)
SovrnThis is an ad network. (Privacy Policy)
Facebook AdsThis is an ad network. (Privacy Policy)
Amazon Unified Ad MarketplaceThis is an ad network. (Privacy Policy)
AppNexusThis is an ad network. (Privacy Policy)
OpenxThis is an ad network. (Privacy Policy)
Rubicon ProjectThis is an ad network. (Privacy Policy)
TripleLiftThis is an ad network. (Privacy Policy)
Say MediaWe partner with Say Media to deliver ad campaigns on our sites. (Privacy Policy)
Remarketing PixelsWe may use remarketing pixels from advertising networks such as Google AdWords, Bing Ads, and Facebook in order to advertise the HubPages Service to people that have visited our sites.
Conversion Tracking PixelsWe may use conversion tracking pixels from advertising networks such as Google AdWords, Bing Ads, and Facebook in order to identify when an advertisement has successfully resulted in the desired action, such as signing up for the HubPages Service or publishing an article on the HubPages Service.
Statistics
Author Google AnalyticsThis is used to provide traffic data and reports to the authors of articles on the HubPages Service. (Privacy Policy)
ComscoreComScore is a media measurement and analytics company providing marketing data and analytics to enterprises, media and advertising agencies, and publishers. Non-consent will result in ComScore only processing obfuscated personal data. (Privacy Policy)
Amazon Tracking PixelSome articles display amazon products as part of the Amazon Affiliate program, this pixel provides traffic statistics for those products (Privacy Policy)
ClickscoThis is a data management platform studying reader behavior (Privacy Policy)