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Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods
http://hdl.handle.net/20.500.12000/0002019561
http://hdl.handle.net/20.500.12000/00020195615cb72639-1936-4ee6-b0bd-b4a780458dbd
名前 / ファイル | ライセンス | アクション |
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杉田 勝弘-10131604-10-1108_AJEB-04-2022-0044.pdf (201 KB)
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Item type | 琉球大学リポジトリ登録用アイテムタイプ(1) | |||||||
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公開日 | 2022-11-14 | |||||||
タイトル | ||||||||
タイトル | Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods | |||||||
言語 | en | |||||||
作成者 |
Sugita, Katsuhiro
× Sugita, Katsuhiro
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アクセス権 | ||||||||
アクセス権 | open access | |||||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
権利情報 | ||||||||
言語 | en | |||||||
権利情報 | © Katsuhiro Sugita | |||||||
権利情報 | ||||||||
言語 | en | |||||||
権利情報Resource | http://creativecommons.org/licences/by/4.0/legalcode | |||||||
権利情報 | This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode. | |||||||
主題 | ||||||||
言語 | en | |||||||
主題Scheme | Other | |||||||
主題 | Forecasting | |||||||
主題 | ||||||||
言語 | en | |||||||
主題Scheme | Other | |||||||
主題 | Bayesian econometrics | |||||||
主題 | ||||||||
言語 | en | |||||||
主題Scheme | Other | |||||||
主題 | VAR model | |||||||
内容記述 | ||||||||
内容記述タイプ | Abstract | |||||||
内容記述 | Purpose – The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models. Design/methodology/approach – The paper adopts Bayesian VAR models with three different priors – independent Normal-Wishart prior, the Minnesota prior and the stochastic search variable selection (SSVS). Monte Carlo simulations are conducted to compare forecasting performances. An empirical study using US macroeconomic data are shown as an illustration. Findings – In theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data. Originality/value – The paper finds that iterated forecasts using model with the SSVS prior generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts. |
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言語 | en | |||||||
出版者 | ||||||||
言語 | en | |||||||
出版者 | Emerald Publishing Limited | |||||||
言語 | ||||||||
言語 | eng | |||||||
資源タイプ | ||||||||
資源タイプ | journal article | |||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||
出版タイプ | ||||||||
出版タイプ | VoR | |||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
関連情報 | ||||||||
識別子タイプ | DOI | |||||||
関連識別子 | https://doi.org/10.1108/AJEB-04-2022-0044 | |||||||
収録物識別子 | ||||||||
収録物識別子タイプ | EISSN | |||||||
収録物識別子 | 2615-9821 | |||||||
収録物名 | ||||||||
言語 | en | |||||||
収録物名 | Asian Journal of Economics and Banking | |||||||
書誌情報 |
巻 6, 号 2, p. 142-154, 発行日 2022-02 |