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Autoregresi Vektor Bayesian (BVAR)×Penghalusan Eksponensial Tiga Kali Lipat Holt-Winters×
BidangEkonometrikaEkonometrika
KeluargaRegression modelRegression model
Tahun asal19861960
PencetusLitterman (1986); Bańbura, Giannone & Reichlin (2010)Charles C. Holt and Peter R. Winters
TipeBayesian multivariate time-series modelExponential smoothing forecasting model
Sumber perintisLitterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342. DOI ↗
AliasBVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme
Terkait54
RingkasanBayesian 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.Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series.
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ScholarGateBandingkan metode: Bayesian VAR · Holt-Winters. Diakses 2026-06-19 dari https://scholargate.app/id/compare