ログイン
言語:

WEKO3

  • トップ
  • ランキング
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

{"_buckets": {"deposit": "b20f394f-6ade-4c5b-9b7c-3e4dc90aa18e"}, "_deposit": {"created_by": 8, "id": "2019561", "owner": "8", "owners": [8], "owners_ext": {"displayname": "ryukyu_lib", "username": null}, "pid": {"revision_id": 0, "type": "depid", "value": "2019561"}, "status": "published"}, "_oai": {"id": "oai:u-ryukyu.repo.nii.ac.jp:02019561", "sets": ["1642838338003", "1642838404033"]}, "author_link": [], "item_1617186331708": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods", "subitem_1551255648112": "en"}]}, "item_1617186419668": {"attribute_name": "Creator", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Sugita, Katsuhiro", "creatorNameLang": "en"}]}]}, "item_1617186476635": {"attribute_name": "Access Rights", "attribute_value_mlt": [{"subitem_1522299639480": "open access", "subitem_1600958577026": "http://purl.org/coar/access_right/c_abf2"}]}, "item_1617186499011": {"attribute_name": "Rights", "attribute_value_mlt": [{"subitem_1522650717957": "en", "subitem_1522651041219": "\u00a9 Katsuhiro Sugita"}, {"subitem_1522650717957": "en", "subitem_1522650727486": "http://creativecommons.org/licences/by/4.0/legalcode", "subitem_1522651041219": "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."}]}, "item_1617186609386": {"attribute_name": "Subject", "attribute_value_mlt": [{"subitem_1522299896455": "en", "subitem_1522300014469": "Other", "subitem_1523261968819": "Forecasting"}, {"subitem_1522299896455": "en", "subitem_1522300014469": "Other", "subitem_1523261968819": "Bayesian econometrics"}, {"subitem_1522299896455": "en", "subitem_1522300014469": "Other", "subitem_1523261968819": "VAR model"}]}, "item_1617186626617": {"attribute_name": "Description", "attribute_value_mlt": [{"subitem_description": "Purpose \u2013 The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.\nDesign/methodology/approach \u2013 The paper adopts Bayesian VAR models with three different priors \u2013 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.\nFindings \u2013 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.\nOriginality/value \u2013 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.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_1617186643794": {"attribute_name": "Publisher", "attribute_value_mlt": [{"subitem_1522300295150": "en", "subitem_1522300316516": "Emerald Publishing Limited"}]}, "item_1617186702042": {"attribute_name": "Language", "attribute_value_mlt": [{"subitem_1551255818386": "eng"}]}, "item_1617186920753": {"attribute_name": "Source Identifier", "attribute_value_mlt": [{"subitem_1522646500366": "EISSN", "subitem_1522646572813": "2615-9821"}]}, "item_1617186941041": {"attribute_name": "Source Title", "attribute_value_mlt": [{"subitem_1522650068558": "en", "subitem_1522650091861": "Asian Journal of Economics and Banking"}]}, "item_1617187056579": {"attribute_name": "Bibliographic Information", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2022-02", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "2", "bibliographicPageEnd": "154", "bibliographicPageStart": "142", "bibliographicVolumeNumber": "6"}]}, "item_1617258105262": {"attribute_name": "Resource Type", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_1617265215918": {"attribute_name": "Version Type", "attribute_value_mlt": [{"subitem_1522305645492": "VoR", "subitem_1600292170262": "http://purl.org/coar/version/c_970fb48d4fbd8a85"}]}, "item_1617353299429": {"attribute_name": "Relation", "attribute_value_mlt": [{"subitem_1522306287251": {"subitem_1522306382014": "DOI", "subitem_1522306436033": "https://doi.org/10.1108/AJEB-04-2022-0044"}}]}, "item_1617605131499": {"attribute_name": "File", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2022-11-14"}], "download_preview_message": "", "file_order": 0, "filename": "\u6749\u7530\u3000\u52dd\u5f18-10131604-10-1108_AJEB-04-2022-0044.pdf", "filesize": [{"value": "201 KB"}], "future_date_message": "", "is_thumbnail": false, "mimetype": "", "size": 201000.0, "url": {"objectType": "fulltext", "url": "https://u-ryukyu.repo.nii.ac.jp/record/2019561/files/\u6749\u7530\u3000\u52dd\u5f18-10131604-10-1108_AJEB-04-2022-0044.pdf"}, "version_id": "a01ce896-4634-44ad-84fb-2b04450664f3"}]}, "item_title": "Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods", "item_type_id": "59", "owner": "8", "path": ["1642838338003", "1642838404033"], "permalink_uri": "http://hdl.handle.net/20.500.12000/0002019561", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2022-11-14"}, "publish_date": "2022-11-14", "publish_status": "0", "recid": "2019561", "relation": {}, "relation_version_is_last": true, "title": ["Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods"], "weko_shared_id": -1}
  1. 学術雑誌論文
  2. その他
  1. 部局別インデックス
  2. 国際地域創造学部

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/20.500.12000/0002019561
5cb72639-1936-4ee6-b0bd-b4a780458dbd
名前 / ファイル ライセンス アクション
杉田 勝弘-10131604-10-1108_AJEB-04-2022-0044.pdf 杉田 勝弘-10131604-10-1108_AJEB-04-2022-0044.pdf (201 KB)
Item type 琉球大学リポジトリ登録用アイテムタイプ(1)
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

en Sugita, Katsuhiro

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
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
言語 en
主題Scheme Other
主題 Bayesian econometrics
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.
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
関連識別子
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
戻る
0
views
See details
Views

Versions

Ver.1 2022-11-14 02:31:29.376033
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON

確認


Powered by WEKO3


Powered by WEKO3