An Efficient Almon Two-parameter Estimator for the Heteroscedastic Distributed Lag Model: A Monte Carlo Evidence

Authors

  • Muhammad Aslam Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. Author https://orcid.org/0000-0003-2290-2307
  • Abdul Majid Pakistan Bureau of Statistics, Regional Office, Multan, Pakistan. Author https://orcid.org/0000-0002-3071-2610
  • Amra Younas Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. Author
  • Saima Altaf Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. Author

DOI:

https://doi.org/10.58575/0fyd3p55

Keywords:

Almon technique, Almon two-parameter estimator, Distributed lag model, Heteroscedasticity, Multicollinearity

Abstract

The distributed lag models (DLM) are very useful in econometrics and statistics. The technique of Almon polynomial distributed lag is a commonly used estimation method when dealing with the DLM. To circumvent the problem of multicollinearity associated with the Almon technique, the Almon two parameter estimator (ATPE) is recently proposed in the literature, which has some advantages over other available estimators. However, the ATPE may become severely inefficient when the DLM is plagued with the heteroscedasticity of unknown form. This study is intended to address this issue and propose an adaptive version of the ATPE which is more efficient than the ATPE in the presence of heteroscedasticity of unknown form. To gauge the performance of our proposed method, a Monte Carlo simulation scheme is used where mean squared error is used as the evaluation criteria. The simulation results witness the supremacy of our proposed method.

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Author Biographies

  • Muhammad Aslam, Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.

    Department of Statistics

  • Abdul Majid, Pakistan Bureau of Statistics, Regional Office, Multan, Pakistan.

    Statistical Officer

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Published

2025-06-16

How to Cite

An Efficient Almon Two-parameter Estimator for the Heteroscedastic Distributed Lag Model: A Monte Carlo Evidence. (2025). Journal of Statistics, 29, 95-109. https://doi.org/10.58575/0fyd3p55

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