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The assumptions of the classical linear regression model are hardly satisfied in real life situation. Violation of the assumption of independent explanatory variables and error terms leads to the problems of multicollinearity and autocorrelation respectively. Estimators to handle each problem have been separately developed by different authors. Moreover, in practice, these two problems do co-exist but estimators to handle them jointly are rare. Consequently, this research proposed and validate two estimators, Feasible Ordinary Ridge Estimators (FORE) and Feasible Generalized Ridge Estimators (FGRE), to deal with multicollinearity and autocorrelation problems jointly. The existing and proposed estimators were categorized into five (5) groups namely: One–Stage Estimators (OSE), Two–Stage Estimators (TSE), Feasible Generalised Least Square Estimators (FGLSE), Two-Process Estimators (TPE) and Modified Ridge Estimators (MRE). Monte Carlo experiments were conducted one thousand (1000) times on a linear regression model exhibiting different degrees of multicollinearity ( 0.4, 0.6, 0.8, 0.95 and 0.99) with both normally and uniformly distributed regressors and autocorrelation ( ) at six sample sizes (n =10, 20, 30, 50, 100 and 250). Finite sampling properties of estimators namely; Bias (BAS), Mean Absolute Error (MAE), Variance (VAR) and Mean Square Error (MSE) were evaluated and compared at each specified level of multicollinearity, autocorrelation and sample size. These were done by ranking the estimators on the basis of their performances according to the MSE criteria so as to determine the best estimator. Real life application data collected were used to validate the findings of the simulation study. Results of the investigation showed that when both problems were in the model, the best estimator clearly depended on the nature of regressors. With normally distributed regressors, the best estimator is the proposed estimator in the category of Two-Process Estimator (TPE). Furthermore with uniformly distributed regressors, the best estimator is in the category of Feasible Generalized Least Square Estimator (FGLSE). In conclusion, two estimators for handling parameter estimation of linear regression model with multicollinearity and autocorrelation problems were developed and the results of the validation process were in agreement with the findings of the simulation study.

Keywords: Ordinary Ridge, Generalized Ridge, Multicollinearity, Autocorrelation, Estimator and Regressors