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Classical Linear Regression Model: Assumptions and ...

Classical Linear Regression Model: Assumptions and Diagnostic TestsYan ZengVersion , last updated on 10/05/2016 AbstractSummary of statistical tests for the Classical Linear Regression Model (CLRM), based on Brooks [1],Greene [5] [6], Pedace [8], and Zeileis [10].Contents1 The Classical Linear Regression Model (CLRM)32 Hypothesis Testing: The t-test and The F-test43 Violation of Assumptions : Detection of multicollinearity.. Consequence of ignoring near multicollinearity.. Dealing with multicollinearity..64 Violation of Assumptions : Detection of heteroscedasticity.. The Goldfeld-Quandt test.. The White s general test.. The Breusch-Pagan test.. The Park test.. Consequences of using OLS in the presence of heteroscedasticity.. Dealing with heteroscedasticity.. The generalised least squares method.

Oct 05, 2016 · 6 6 6 4 1 1... 1 3 7 7 7 5 T 1 so that 1 is the constant term in the model. Let y be the T observations y1, , yT, and let " be the column vector containing the T disturbances. The Classical Linear Regression Model (CLRM) can be written as y = x1 1 + +xK K +"; xi = 2 6 6 6 4 xi1 xi2... xiT 3 7 7 7 5 T 1 or in matrix form yT 1 = XT K K 1 +"T 1:

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