Method of Detection Effects of the Departures/Violations on Assumptions violations on the model Remedial Measures Graphical Statistical The values of the regressors, the X's, are not fixed or random,...


Method of Detection<br>Effects of the<br>Departures/Violations on Assumptions<br>violations on the<br>model<br>Remedial Measures<br>Graphical<br>Statistical<br>The values of the regressors, the<br>X's, are not fixed or random, or X<br>values are not independent of the<br>error term.<br>1.<br>2. There is exact linear relationship or<br>exact collinearity between the X<br>variables.<br>3. For given X's, the mean value of<br>disturbance ɛ, is not equal to zero.<br>4. For given X's, there is<br>autocorrelation, or serial correlation,<br>between the disturbances.<br>5. The number of observations n is less<br>than or equal to the number of<br>parameters to be estimated.<br>6. The model is incorrectly specified, so<br>there is specification bias.<br>7. There are errors in the measurement<br>of variables of the model.<br>

Extracted text: Method of Detection Effects of the Departures/Violations on Assumptions violations on the model Remedial Measures Graphical Statistical The values of the regressors, the X's, are not fixed or random, or X values are not independent of the error term. 1. 2. There is exact linear relationship or exact collinearity between the X variables. 3. For given X's, the mean value of disturbance ɛ, is not equal to zero. 4. For given X's, there is autocorrelation, or serial correlation, between the disturbances. 5. The number of observations n is less than or equal to the number of parameters to be estimated. 6. The model is incorrectly specified, so there is specification bias. 7. There are errors in the measurement of variables of the model.

Jun 11, 2022
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