Errors in variables:

In ordinary least squares model we assume that sample observations  are measured accurately. All our formulae are based upon the presumption  that variables (both explained and explanatory) are measured without error. The only form of error admitted to our model is in the form of disturbance term. Here the error term represents  the influence of variphs explanatory variables that have not accurately been  included in  the model. However, the assumption may not be realistic, particularly in the case of secondary data.

Variables, both dependent and independent, are measured subject to error. In particular, the available data may not refer to the variable as specified, as in the case of proxy variable, or there may be systematic biases in the collection or publication of data.  If the measurement errors are systematic, in general, auxiliary equations  can be specified to capture  these errors. This unit will focus only on the impact of random measurement errors on the regression model.  

In ordinary least squares model we assume that sample  observations are measured without error, which  is  always not true. When this assumption does not hold, OLS estimators are biasedand  inconsistent. Errors may appear in the measurement of dependent variable, independent  vhable  or both. When there is  error  in dependent variable,  this does  not destroy the unbiased property of the OLS estimators  but the estimated variances are larger than  the case where  there is no such errors of measurement.