ArticleScientific reports2025
Application of empirical likelihood ratio test in AR(1) time series model for PM
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
The Autoregressive model plays a vital role in time series analysis, as it efficiently captures short-term dependencies while maintaining simplicity. Accurate identification of the order of autoregressive models is essential for enhanced model performance and reliable forecasting. Traditional method of identifying AR (1) models relying on autocorrelation and partial autocorrelation plots, along with the Ljung-Box (LB) test, often suffer from subjectivity and potential overfitting. To overcome this limitation, this study proposes an Empirical Likelihood Ratio Test (ELRT) for assessing the suitability of AR (1) model in time series analysis. Through Monte Carlo simulation, performance of the ELRT is compared with the LB test in terms of empirical size and power. Simulation results indicate that the ELRT maintains empirical size more accurately while exhibiting superior power compared to LB test. The proposed test is further validated using major air pollutant, PM
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