The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is,\nwhen multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified\nnew two-parameter estimator based on prior information for the vector of parameters is proposed to circumvent the problem of\nmulticollinearity. This new estimator includes the special cases of the ordinary least squares estimator (OLSE), the ridge estimator\n(RRE), the Liu estimator (LE), the modified ridge estimator (MRE), and the modified Liu estimator (MLE). Furthermore, the\nsuperiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and\nKaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a\nsimulation study were conducted to illustrate the theoretical results.
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