A Bayes Algorithm for Model Compatibility and Comparisonof ARMA(p,q) Models

• Autorzy: Praveen Kumar Tripathi , Rijji Sen , S.K. Upadhyay
• Języki publikacji: EN

Abstrakty

EN

The paper presents a Bayes analysis of an autoregressive-moving average model and its com-ponents based on exact likelihood and weak priors for the parameters where the priors aredefined so that they incorporate stationarity and invertibility restrictions naturally. A Gibbs-Metropolis hybrid scheme is used to draw posterior-based inferences for the models underconsideration. The compatibility of the models with the data is examined using the Ljung-Box-Pierce chi-square-based statistic. The paper also compares different compatible modelsthrough the posterior predictive loss criterion in order to recommend the most appropriateone. For a numerical illustration of the above, data on the Indian gross domestic productgrowth rate at constant prices are considered. Differencing the data once prior to conductingthe analysis ensured their stationarity. Retrospective short-term predictions of the data areprovided based on the final recommended model. The considered methodology is expectedto offer an easy and precise method for economic data analysis.(original abstract)

Słowa kluczowe

EN

ARMA model; Gross domestic product (GDP); Probability; Statistics

PL

Model ARMA; Produkt krajowy brutto (PKB); Prawdopodobieństwo; Statystyka

• Czasopismo: Statistics in Transition
• Rocznik: 2021
• Tom: 22
• Numer: nr 2
• Strony: 95--123

Twórcy

autor

Praveen Kumar Tripathi

• Dept. of Mathematics and Statistics, Banasthali Vidyapith, Rajasthan, India

autor

Rijji Sen

• Calcutta University

autor

S.K. Upadhyay

• Department of Statistics, Banaras Hindu University, Varanasi, India

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Identyfikatory

DOI

10.21307/stattrans-2021-018