{"id":29510,"date":"2015-04-25T03:42:53","date_gmt":"2015-04-25T03:42:53","guid":{"rendered":"http:\/\/vskills.in\/certification\/blog\/?p=29510"},"modified":"2024-04-03T13:16:33","modified_gmt":"2024-04-03T07:46:33","slug":"classical-linear-regression-model","status":"publish","type":"post","link":"https:\/\/www.vskills.in\/certification\/blog\/classical-linear-regression-model\/","title":{"rendered":"Classical Linear Regression Model"},"content":{"rendered":"

\"Classical<\/a><\/p>\n

There are some assumptions relating to CLRM. These assumptions are necessary to show that the estimation technique, Ordinary Least Square Method has a number of desirable properties and also that the hypothesis tests regarding the coefficient estimates could be validly conducted.<\/p>\n

The Assumptions are:<\/p>\n

    \n
  1. Linear Regression Model, that is, the model is linear in parameters and not necessarily in variables.<\/li>\n
  2. X (explanatory variable) is fixed or known to us. Hence, X is non-stochastic.<\/li>\n
  3. Mean of disturbance term, u is 0.<\/li>\n
  4. u is homoscedastic, that is, u has equal variance for all the terms.<\/li>\n
  5. There is not auto correlation between two disturbance terms, u. That is, covariance between them is 0.<\/li>\n
  6. 0 correlation between u and X. Therefore, X is exogenous.<\/li>\n
  7. Number of sample observations (n) is greater than number of parameters to be estimated.<\/li>\n
  8. Variance of X is finite, that is, greater than 0.<\/li>\n
  9. There is no specification bias or error in model, which means, all the relevant variables must be included in the linear regression model.<\/li>\n
  10. There exists no perfect linear relationship between explanatory variables.<\/li>\n
  11. Random error term u, follows Normal Distribution with mean 0.<\/li>\n<\/ol>\n

    Click here for government certifications<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    There are some assumptions relating to CLRM. These assumptions are necessary to show that the estimation technique, Ordinary Least Square Method has a number of desirable properties and also that the hypothesis tests regarding the coefficient estimates could be validly conducted. The Assumptions are: Linear Regression Model, that is, the model is linear in parameters…<\/p>\n","protected":false},"author":505,"featured_media":29543,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[1560],"tags":[3402,1568,3400,3403,3401],"class_list":["post-29510","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-arbit","tag-assumptions","tag-economics","tag-linear","tag-properties","tag-regression"],"acf":[],"yoast_head":"\nClassical Linear Regression Model - Vskills Blog<\/title>\n<meta name=\"description\" content=\"Classical Linear Regression Model; assumptions Apply for a Vskills certification in Economics now. 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