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Bayesian Vector Autoregression (BVAR)×Strukturni model vremenskih nizova (Osnovni strukturni model)×
PodručjeEkonometrijaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka19861990
TvoracLitterman (1986); Bańbura, Giannone & Reichlin (2010)Andrew C. Harvey
VrstaBayesian multivariate time-series modelState-space (unobserved components) time series model
Temeljni izvorLitterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
Drugi naziviBVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Srodne54
SažetakBayesian 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.The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.
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ScholarGateUsporedite metode: Bayesian VAR · Structural Time Series Model. Preuzeto 2026-06-19 s https://scholargate.app/hr/compare