{"_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}