Due to the nature of the distributed lag model, researchers are likely to encounter the problem of multicollinearity in this model. Biased estimation techniques, one of which is Almon ridge estimation, are alternatively considered instead of Almon estimation with the aim of recovering the multicollinearity. Although estimation performance is often taken into consideration, predictive performance is rarely handled in the distributed lag model. The principal purpose of this paper is to investigate the predictive performance of the distributed lag model through target function. In this context, we employ Almon ridge estimation to define a new predictor that is more resistant to multicollinearity. For an extensive analysis of the prediction problem in the distributed lag model, we concentrate on the theoretical results and comparisons. Then, the issue of determining optimal parameters is considered by means of minimizing the prediction mean square error. Numerical analysis depending on global warming data is examined to validate our theoretical outcomes. Moreover, a Monte Carlo experiment is carried out to evaluate the predictive ability of the estimators.

由于分布式滞后模型的性质，研究人员可能会在该模型中遇到多重共线性问题。为了恢复多重共线性，可替代地考虑偏向估计技术，其中之一是阿尔蒙岭估计，而不是阿尔蒙估计。尽管经常考虑估计性能，但是在分布式滞后模型中很少处理预测性能。本文的主要目的是通过目标函数研究分布式滞后模型的预测性能。在这种情况下，我们采用Almon岭估计来定义一个新的预测器，该预测器对多重共线性的抵抗力更大。为了对分布式滞后模型中的预测问题进行广泛的分析，我们将重点放在理论结果和比较上。然后，通过最小化预测均方误差来考虑确定最佳参数的问题。根据全球变暖数据进行了数值分析，以验证我们的理论结果。此外，进行了蒙特卡洛实验以评估估计量的预测能力。