On the performance of autoregressive moving average polynomial distributed Lag model

Abstract

This study focused on the performance of Autoregressive Moving Average Polynomial Distributed Lag Model among all other distributed lag models. Four models  were considered; Distributed Lag (DL) model, Polynomial Distributed Lag (PDL) model, Autoregressive Polynomial Distributed Lag (ARPDL) model and  Autoregressive Moving Average Polynomial Distributed Lag (ARMAPDL) model. The parameters of these models were estimated using least squares and Newton  Raphson iterative methods. To determine the order of the models, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used. To  determine the best model, the residual variances attached to these models were studied and the model with the minimum  residual variance was considered to  perform better than others. Using numerical example, DL, PDL, ARPDL and ARMAPDL models were fitted. Autoregressive Moving Average Polynomial Distributed Lag Model (ARMAPDL) model performed better than the other models.

Keywords: Distributed Lag Model, Selection Criterion, Parameter Estimation, Residual Variance.