{"created":"2023-06-20T13:20:44.663586+00:00","id":781,"links":{},"metadata":{"_buckets":{"deposit":"bb48ce40-715e-4851-9832-62f6bbe6130c"},"_deposit":{"created_by":1,"id":"781","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"781"},"status":"published"},"_oai":{"id":"oai:ir.soken.ac.jp:00000781","sets":["2:429:17"]},"author_link":["0","0","0"],"item_1_creator_2":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"矢野, 浩一"}],"nameIdentifiers":[{"nameIdentifier":"0","nameIdentifierScheme":"WEKO"}]}]},"item_1_creator_3":{"attribute_name":"フリガナ","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"ヤノ, コウイチ"}],"nameIdentifiers":[{"nameIdentifier":"0","nameIdentifierScheme":"WEKO"}]}]},"item_1_date_granted_11":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2007-03-23"}]},"item_1_degree_grantor_5":{"attribute_name":"学位授与機関","attribute_value_mlt":[{"subitem_degreegrantor":[{"subitem_degreegrantor_name":"総合研究大学院大学"}]}]},"item_1_degree_name_6":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"博士（統計科学）"}]},"item_1_description_12":{"attribute_name":"要旨","attribute_value_mlt":[{"subitem_description":"Financial markets and the economy are changing rapidly. On financial markets, many <br />financial time series exhibit changes of volatility (variance) over time. Moreover, many <br />financial time series are well known to have non-Gaussian heavy-tailed distributions. <br />These facts indicate that a nonlinear non-Gaussian time series analysis is needed. <br />Regarding the economy, as one example, the Japanese economy has the experience of <br />the \"bubble economy\" in the late 1980s. After bursting of the \"bubble economy\", the <br />economy entered a decade o,f economic stagnation, which is often called \"the lost <br />decade\". These facts indicate that conventional linear regression based on ordinary <br />least squares might be ineffective to analyze a non-stationary economy because the <br />coefficients of linear regression are fixed. This paper shows several statistical <br />approaches based on nonlinear non-Gaussian state space modeling and time-varying <br />coefficient autoregressive modeling. These approaches are novel studies of financial <br />markets and the economy. <br />　In chapter 1, the Monte Carlo filter is introduced. It is a minimal introduction to <br />nonlinear non-Gaussian state-space modeling. <br />　In chapter 2, we propose a method to seek initial distributions of parameters for a <br />self-organizing state space model proposed by Kitagawa]. Our method is based on the <br />simplex Nelder-Mead algorithm for solving nonlinear and discontinuous optimization <br />problems. We show the effectiveness of our method by applying it to a linear Gaussian <br />model, a linear non-Gaussian Model, a nonlinear Gaussian model, and a stochastic <br />volatility model. <br />　In chapter 3, we propose a smoothing algorithm based on the Monte Carlo filter and <br />the inverse function of a system equation (an inverse system function). Our method is <br />applicable to any nonlinear non-Gaussian state space model if an inverse system <br />equation is given analytically. Moreover, we propose a filter initialization algorithm <br />based on a smoothing distribution obtained by our smoothing algorithm and an <br />inverse system equation. <br />　In chapter 4, we illustrate the effectiveness of our approach by applying it to <br />stochastic volatility models and stochastic volatility models with heavy-tailed <br />distributions for the daily return of the Yen/Dollar exchange rate. <br />　In chapter 5, we propose a method that estimates a time-varying linear system <br />equation based on time-varying coefficients' vector autoregressive modeling <br />(time-varying VAR), and which controls the system. In our framework, an optimal <br />feedback is determined using linear quadratic dynamic programming in each period.<br />The coeffients of time-varying VAR are assumed to change gradually (this <br />assumption is widely known as smoothness priors of the Bayesian procedure). The <br />coefficients are estimated using the Kalman filter. In our empirical analyses, we show <br />the effectiveness of our approach by applying it to monetary policy, in particular, the <br />inflation targeting of the United Kingdom and the nominal growth rate targeting of <br />Japan. Furthermore, we emphasize that monetary policy must be forecast-based <br />because transmission lags pertain from monetary policy to the economy. Our approach <br />is convenient and effective for central bank practitioners when they are unaware of <br />the true model of the economy. Additionally, we find that the coefficients of <br />time-varying VAR change in response to changes of monetary policy. <br />　In chapter 6, we estimate the β of a single factor model that is ofben used by <br />financial practitioners. In this chapter, we assume that β changes \"gradually\" over <br />time; this assumption is identical to that in chapter 5. Using our approach, we can <br />estimate β, even if it is time varying. We apply our approach to the Japanese Stock <br />Markets and show its effectiveness. Although we adopt a very restrictive method (we <br />assume smoothness priors and use the Kalman fiker, which is based on linear state <br />space modeling and the Gaussian distribution), we can obtain good estimates of β.","subitem_description_type":"Other"}]},"item_1_description_7":{"attribute_name":"学位記番号","attribute_value_mlt":[{"subitem_description":"総研大甲第1043号","subitem_description_type":"Other"}]},"item_1_select_14":{"attribute_name":"所蔵","attribute_value_mlt":[{"subitem_select_item":"有"}]},"item_1_select_8":{"attribute_name":"研究科","attribute_value_mlt":[{"subitem_select_item":"複合科学研究科"}]},"item_1_select_9":{"attribute_name":"専攻","attribute_value_mlt":[{"subitem_select_item":"15 統計科学専攻"}]},"item_1_text_10":{"attribute_name":"学位授与年度","attribute_value_mlt":[{"subitem_text_value":"2006"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"YANO, Koiti","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"0","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2016-02-17"}],"displaytype":"simple","filename":"甲1043_要旨.pdf","filesize":[{"value":"245.4 kB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"要旨・審査要旨","url":"https://ir.soken.ac.jp/record/781/files/甲1043_要旨.pdf"},"version_id":"c581c9d6-1b60-4b10-b278-70297b72d204"},{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2016-02-17"}],"displaytype":"simple","filename":"甲1043_本文.pdf","filesize":[{"value":"1.4 MB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"本文","url":"https://ir.soken.ac.jp/record/781/files/甲1043_本文.pdf"},"version_id":"55f0e445-e9d9-4298-a01f-8e5a29b8695f"}]},"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":"thesis","resourceuri":"http://purl.org/coar/resource_type/c_46ec"}]},"item_title":"Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series"},{"subitem_title":"Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series","subitem_title_language":"en"}]},"item_type_id":"1","owner":"1","path":["17"],"pubdate":{"attribute_name":"公開日","attribute_value":"2010-02-22"},"publish_date":"2010-02-22","publish_status":"0","recid":"781","relation_version_is_last":true,"title":["Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series"],"weko_creator_id":"1","weko_shared_id":1},"updated":"2023-06-20T16:00:00.961587+00:00"}