Monte Carlo Simulations in Regression Analysis: Applications in Multicollinearity and Autocorrelation Studies
DOI:
https://doi.org/10.33003/fjs-2026-1022-6081Keywords:
Monte Carlo Simulation, Multicollinearity, Autocorrelation, Ridge Regression, Generalized Least SquaresAbstract
Monte Carlo simulations provide a controlled framework for evaluating regression estimators under multicollinearity and autocorrelation. This study evaluates Ordinary Least Squares (OLS), Ridge Regression, Cochrane-Orcutt and Generalized Least Squares (GLS) under varying degrees of predictor correlation and first-order autocorrelation. Predictor correlations of 0.7, 0.8 and 0.9 and autocorrelation parameter values of ρ = 0.5 and 0.7 are considered across the specified sample sizes, with 1,000 replications for each simulation condition. Model performance is evaluated using Mean Squared Error (MSE), Bias and Adjusted R2. The simulation results show how increasing predictor correlation affects coefficient stability and how increasing autocorrelation affects prediction performance. Ridge Regression provides more stable coefficient estimates under increased predictor correlation, while Cochrane-Orcutt and GLS improve performance under autocorrelated errors. The combined simulation results demonstrate differences in estimator performance when multicollinearity and autocorrelation occur simultaneously. The findings provide simulation-based evidence for interpreting regression estimator performance under the specified data conditions.
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