article · Communications in Statistics - Simulation and Computation
Distributed lag models frequently rely on the Almon procedure, yet this approach suffers from reduced reliability when lagged explanatory variables exhibit high multicollinearity. Existing biased Almon-type estimators attempt to resolve this issue, but they remain sensitive to strong correlations among regressors. To address this limitation, a new biased Almon-type estimator has been developed to enhance estimation robustness in the presence of multicollinearity. The theoretical conditions necessary and sufficient for this estimator to outperform established alternatives were established under the matrix mean squared error criterion. Extensive Monte Carlo simulations alongside two empirical applications validate these theoretical findings, showing that the proposed method consistently achieves lower mean squared error values than earlier alternatives.
Accurate modelling of delayed effects across time is vital when variables correlate strongly with one another. By reducing estimation errors caused by multicollinearity, this statistical method provides researchers and analysts with more dependable quantitative assessments when examining time-dependent data.
The method could be integrated into statistical software packages, econometric toolkits, or financial analytics platforms used by data analysts and forecasters. As theoretical and empirical testing is demonstrated via simulations and two practical applications, this represents an applied methodological development, though the abstract does not specify any direct commercialisation pathway.
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The Almon procedure is a standard method for estimating distributed lag models, but it is highly vulnerable to multicollinearity among lagged regressors, reducing estimation reliability. Although several biased Almon-type estimators have been proposed, they remain sensitive to strong correlations, limiting their effectiveness. This study introduces a new Almon-type biased estimator specifically designed to improve robustness under multicollinearity. We derive the necessary and sufficient conditions under which the proposed estimator surpasses existing alternatives using the matrix mean squared error criterion. Monte Carlo simulations and two empirical applications confirm its superiority, showing consistently lower mean squared error values.
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DOI: 10.1080/03610918.2026.2725846
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