{"created":"2021-03-01T07:02:51.423593+00:00","id":42906,"links":{},"metadata":{"_buckets":{"deposit":"4534f97b-ba63-4998-a401-4afdbbb26216"},"_deposit":{"id":"42906","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42906"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042906","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2011-01","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-782","bibliographic_titles":[{"bibliographic_title":"Discussion paper series. CIRJE-F"}]}]},"item_8_description_13":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"A new state space approach is proposed to model the time-dependence in an extreme value process. The generalized extreme value distribution is extended to incorporate the time-dependence using a state space representation where the state variables either fol- low an autoregressive (AR) process or a moving average (MA) process with innovations arising from a Gumbel distribution. Using a Bayesian approach, an efficient algorithm is proposed to implement Markov chain Monte Carlo method where we exploit an accu- rate approximation of the Gumbel distribution by a ten-component mixture of normal distributions. The methodology is illustrated using extreme returns of daily stock data. The model is  tted to a monthly series of minimum returns and the empirical results support strong evidence of time-dependence among the observed minimum returns","subitem_description_type":"Abstract"}]},"item_8_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"Revised version of CIRJE-F-689 (2009); subsequently published in Computational Statistics and Data Analysis, 56-11, 3241-3259. November 2012.","subitem_description_type":"Other"},{"subitem_description":"本文フィルはリンク先を参照のこと","subitem_description_type":"Other"}]},"item_8_publisher_20":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"日本経済国際共同センター"}]},"item_8_relation_25":{"attribute_name":"関係URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.cirje.e.u-tokyo.ac.jp/research/dp/2011/2011cf782ab.html","subitem_relation_type_select":"URI"}}]},"item_8_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11450569","subitem_source_identifier_type":"NCID"}]},"item_8_subject_15":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"335","subitem_subject_scheme":"NDC"}]},"item_8_text_21":{"attribute_name":"出版者別名","attribute_value_mlt":[{"subitem_text_value":"Center for International Research on the Japanese Economy"}]},"item_8_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Statistical Science, Duke University"},{"subitem_text_value":"Faculty of Economics, University of Tokyo"},{"subitem_text_value":"Department of Applied Statistics, Johannes Kepler University Linz"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Nakajima, Jouchi"}],"nameIdentifiers":[{"nameIdentifier":"98662","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kunihama, Tsuyoshi"}],"nameIdentifiers":[{"nameIdentifier":"98663","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Omori, Yasuhiro"}],"nameIdentifiers":[{"nameIdentifier":"98664","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Frühwirth-Schnatter, Sylvia"}],"nameIdentifiers":[{"nameIdentifier":"98665","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Extreme values","subitem_subject_scheme":"Other"},{"subitem_subject":"Generalized extreme value distribution","subitem_subject_scheme":"Other"},{"subitem_subject":"Markov chain","subitem_subject_scheme":"Other"},{"subitem_subject":"Monte Carlo","subitem_subject_scheme":"Other"},{"subitem_subject":"Mixture sampler","subitem_subject_scheme":"Other"},{"subitem_subject":"State space model","subitem_subject_scheme":"Other"},{"subitem_subject":"Stock returns","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"technical report","resourceuri":"http://purl.org/coar/resource_type/c_18gh"}]},"item_title":"Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form"}]},"item_type_id":"8","owner":"1","path":["7436","7434"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-01-17"},"publish_date":"2017-01-17","publish_status":"0","recid":"42906","relation_version_is_last":true,"title":["Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:18:01.412417+00:00"}