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Likelihood-based quantile autoregressive distributed lag models and its applications.

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  • 1School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, People's Republic of China.

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This summary is machine-generated.

This study introduces a new quantile regression (QR) method for autoregressive distributed lag (ARDL) models to analyze time-lagged economic data. The approach combines asymmetric Laplace distribution (ALD) with the expectation-maximization (EM) algorithm for robust dynamic regression analysis.

Keywords:
Dynamic regression modelEM algorithmQR analysiselectricity consumptioninformation criterion

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Economic variables often exhibit time lag effects, influenced by past values and external factors.
  • Autoregressive Distributed Lag (ARDL) models are standard for dynamic regression but may not capture distributional heterogeneity.

Purpose of the Study:

  • To develop a quantile regression (QR) framework for autoregressive distributed lag (ARDL) models.
  • To address dynamic regression analysis in the presence of time lags and varying conditional quantiles.

Main Methods:

  • Integration of asymmetric Laplace distribution (ALD) working likelihood with the expectation-maximization (EM) algorithm.
  • Derivation of iterative weighted least square estimators (IWLSE) for the dynamic ARDL-QR model.

Main Results:

  • Monte Carlo simulations demonstrate the effectiveness of the proposed estimation method.
  • The method provides robust estimators for dynamic models with time lags.

Conclusions:

  • The proposed ARDL-QR model offers a powerful tool for analyzing economic data with time lags.
  • The methodology is illustrated with a real-world application on residential electricity consumption data.