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Model ARIMA (Autoregressive Integrated Moving Average)×Bayesovská vektorová autoregresia (BVAR)×
OdborEkonometriaEkonometria
RodinaRegression modelRegression model
Rok vzniku20151986
TvorcaBox & Jenkins (Box-Jenkins methodology)Litterman (1986); Bańbura, Giannone & Reichlin (2010)
TypUnivariate time-series modelBayesian multivariate time-series model
Pôvodný zdrojBox, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗
Ďalšie názvyBox-Jenkins model, ARIMA(p,d,q), ARIMA ModeliBVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)
Príbuzné55
ZhrnutieARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.
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ScholarGatePorovnať metódy: ARIMA · Bayesian VAR. Získané 2026-06-19 z https://scholargate.app/sk/compare