{'fraction_non_alphanumeric': 0.01859229747675963, 'fraction_numerical': 0.0, 'mean_word_length': 5.358649789029536, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Monte Carlo Study for Dealing with Multicollinearity and Autocorrelation Problems in Linear Regression Using Two Stage Ridge Regression Method

In the multiple linear regression model, the problem of multicollinearity may come together with autocorrelation; therefore, several methods of estimation are developed to deal with this case; Two-Stage Ridge Regression (TR) is one of them. This article's main objective is to run a Monte Carlo simulation to investigate the impact of both problems, Multicollinearity and Autocorrelation, in multiple linear regression model on the performance of the TR. The simulation is carried out under different levels of multicollinearity, and sets of autocorrelation coefficient, taking into account different sample sizes. Some new properties for the TR method, including expectation, variance and mean square error, are droved. In contrast, the study also has developed some techniques to estimate the biasing parameter for the TR by modifying some popular techniques used in ridge regression (RR). Moreover, Mean Square Error is used as a base for evaluation and comparison. The empirical findings from the simulations revealed that the TR estimator performs better than the RR, and the values of the biasing parameter under the TR are always less than that under the RR. This paper contributes to the existing literature on developing new estimation methods used to overcome the presence of mixed problems in a linear regression model and studying their properties.