Unraveling Time Series Dynamics: Evaluating Partial Autocorrelation Function Distribution and Its Implications

19974 7 107385 2 325351 document 107385 mca-29-00058.pdf application/pdf 85667108818bdb733dfac4e2d2adffe6 MD5 5274527 2024-09-05 08:08:14 https://pure.iiasa.ac.at/id/eprint/19974/1/mca-29-00058.pdf 19974 1 1 application/pdf text en public cc_by_4

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/107385 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/107385 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/107385 archive 353 disk0/00/01/99/74 2024-09-05 08:11:45 2025-03-12 05:01:17 2024-09-05 08:11:45 article show Hassani H. Marvian L. Yarmohammadi M. Yeganegi R. 2456 0000-0003-4109-0690 Unraveling Time Series Dynamics: Evaluating Partial Autocorrelation Function Distribution and Its Implications pub ASA CAT autocorrelation function (ACF); partial autocorrelation function (PACF); time series analysis; normality; PACF distribution; Hassani’s −12 theorem; Bank of England rate The objective of this paper is to assess the distribution of the Partial Autocorrelation Function (PACF), both theoretically and empirically, emphasizing its crucial role in modeling and forecasting time series data. Additionally, it evaluates the deviation of the sum of sample PACF from normality: identifying the lag at which departure occurs. Our investigation reveals that the sum of the sample PACF, and consequently its components, diverges from the expected normal distribution beyond a certain lag. This observation challenges conventional assumptions in time series modeling and forecasting, indicating a necessity for reassessment of existing methodologies. Through our analysis, we illustrate the practical implications of our findings using real-world scenarios, highlighting their significance in unraveling complex data patterns. This study delves into 185 years of monthly Bank of England Rate data, utilizing this extensive dataset to conduct an empirical analysis. Furthermore, our research paves the way for future exploration, offering insights into the complexities and potential revisions in time series analysis, modeling, and forecasting. 2024-07-19 published doi:10.3390/mca29040058 3692 public Mathematical and Computational Applications 29 4 e58 TRUE 2297-8747 no info:eu-repo/semantics/article