{"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":[{}]}]},"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":[{}]}]},"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
financial time series exhibit changes of volatility (variance) over time. Moreover, many
financial time series are well known to have non-Gaussian heavy-tailed distributions.
These facts indicate that a nonlinear non-Gaussian time series analysis is needed.
Regarding the economy, as one example, the Japanese economy has the experience of
the \"bubble economy\" in the late 1980s. After bursting of the \"bubble economy\", the
economy entered a decade o,f economic stagnation, which is often called \"the lost
decade\". These facts indicate that conventional linear regression based on ordinary
least squares might be ineffective to analyze a non-stationary economy because the
coefficients of linear regression are fixed. This paper shows several statistical
approaches based on nonlinear non-Gaussian state space modeling and time-varying
coefficient autoregressive modeling. These approaches are novel studies of financial
markets and the economy.
In chapter 1, the Monte Carlo filter is introduced. It is a minimal introduction to
nonlinear non-Gaussian state-space modeling.
In chapter 2, we propose a method to seek initial distributions of parameters for a
self-organizing state space model proposed by Kitagawa]. Our method is based on the
simplex Nelder-Mead algorithm for solving nonlinear and discontinuous optimization
problems. We show the effectiveness of our method by applying it to a linear Gaussian
model, a linear non-Gaussian Model, a nonlinear Gaussian model, and a stochastic
volatility model.
In chapter 3, we propose a smoothing algorithm based on the Monte Carlo filter and
the inverse function of a system equation (an inverse system function). Our method is
applicable to any nonlinear non-Gaussian state space model if an inverse system
equation is given analytically. Moreover, we propose a filter initialization algorithm
based on a smoothing distribution obtained by our smoothing algorithm and an
inverse system equation.
In chapter 4, we illustrate the effectiveness of our approach by applying it to
stochastic volatility models and stochastic volatility models with heavy-tailed
distributions for the daily return of the Yen/Dollar exchange rate.
In chapter 5, we propose a method that estimates a time-varying linear system
equation based on time-varying coefficients' vector autoregressive modeling
(time-varying VAR), and which controls the system. In our framework, an optimal
feedback is determined using linear quadratic dynamic programming in each period.
The coeffients of time-varying VAR are assumed to change gradually (this
assumption is widely known as smoothness priors of the Bayesian procedure). The
coefficients are estimated using the Kalman filter. In our empirical analyses, we show
the effectiveness of our approach by applying it to monetary policy, in particular, the
inflation targeting of the United Kingdom and the nominal growth rate targeting of
Japan. Furthermore, we emphasize that monetary policy must be forecast-based
because transmission lags pertain from monetary policy to the economy. Our approach
is convenient and effective for central bank practitioners when they are unaware of
the true model of the economy. Additionally, we find that the coefficients of
time-varying VAR change in response to changes of monetary policy.
In chapter 6, we estimate the β of a single factor model that is ofben used by
financial practitioners. In this chapter, we assume that β changes \"gradually\" over
time; this assumption is identical to that in chapter 5. Using our approach, we can
estimate β, even if it is time varying. We apply our approach to the Japanese Stock
Markets and show its effectiveness. Although we adopt a very restrictive method (we
assume smoothness priors and use the Kalman fiker, which is based on linear state
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":[{}]}]},"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"}