Improvement of the Liu-type Shiller estimator for distributed lag models

What is it about?

The problem of multicollinearity produces undesirable effects on ordinary least squares (OLS), Almon and Shiller estimators for distributed lag models. Therefore, we introduce a Liu-type Shiller estimator to deal with multicollinearity for distributed lag models. Moreover, we theoretically compare the predictive performance of the Liu-type Shiller estimator with OLS and the Shiller estimators by the prediction mean square error criterion under the target function. Furthermore, an extensive Monte Carlo simulation study is carried out to evaluate the predictive performance of the Liu-type Shiller estimator.

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Why is it important?

Our new Bayesian Liu type Shiller estimator is superior to the Almon, Shiller and ordinary least squares estimator according to the prediction mean square error under target function. Therefore, it is useful for the researchers who investigate biased estimation methods in the finite distributed lag models.

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