Efficient estimation of distributed lag model in presence of heteroscedasticity of unknown form: A Monte Carlo evidence

In the presence of heteroscedasticity, the ordinary least-squares (OLS) estimator remains no more efficient while the popular Almon technique is being considered for a finite distributed lag model (DLM). The available literature proposes few adaptive estimators which are more efficient than the OLS estimator when there is heteroscedasticity of unknown form. This study suggests the similar adaptation combined with the Almon technique in order to get more efficient estimator of vector of lag coefficients in the DLM. Performance of the proposed estimator has been evaluated through the Monte Carlo simulations. The simulation results show an attractive performance of the proposed estimator in terms of efficiency.