Robust estimation of the distributed lag model with multicollinearity and outliers

Abstract

The use of Almon technique is encouraged for estimation of the distributed lag model (DLM) to avoid some serious problems associated with the direct application of the ordinary least squares (OLS) method. In Almon technique, the OLS procedure is applied on the transformed regressors which may be correlated themselves. To tackle this issue, the use of ordinary ridge regression estimator (ORRE) combined with the Almon technique has been considered as an alternative approach in the literature. However, the ORRE may be sensitive to outliers in the y-direction and thus, its use combined with the Almon technique may raise doubts on the estimated lag coefficients. The present work addresses this issue and suggests an alternative estimation method for the DLM, which is robust in the presence of multicollinearity and outliers. The performance of the proposed estimator has been evaluated through an extensive Monte Carlo simulation study and empirical findings are presented using the mean squared error criterion. The results reveal an attractive performance of the proposed estimator in the presence of outliers.