Enhancing Regression Accuracy: The Almost Unbiased Liu Principal Component Estimator under Multicollinearity and Autocorrelation

This research paper introduces the Almost Unbiased Liu Principal Component Estimator (AULPCE), a robust statistical technique designed to improve the accuracy of regression models in the presence of multicollinearity and autocorrelation. By combining the strengths of Liu and Principal Component Estimators, this method effectively reduces bias and variance, providing more reliable parameter estimates. The paper presents a comprehensive analysis, comparing the AULPCE with traditional estimators through extensive simulations and real-world applications, making it an essential reference for researchers and practitioners in econometrics and data science.