A Modified Box-Cox Transformation in the Multivariate ARMA Model
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- Terasaka Takahiro
- Department of Economics, Otaru University of Commerce
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- Hosoya Yuzo
- Department of Economics, Meisei University
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説明
The Box-Cox transformation has been used as a simple method of transforming dependent variable in ordinary-linear regression circumstances for improving the Gaussian-likelihood fit and making the disturbance terms of a model reasonably homoscedastic. The paper introduces a new version of the Box-Cox transformation and investigates how it works in terms of asymptotic performance and application, focusing in particular on inference on stationary multivariate ARMA models. The paper proposes a computational estimation procedure which extends the three-step Hannan and Rissanen method so as to accommodate the transformation and, for the purpose of parameter testing, the paper proposes a Monte-Carlo Wald test. The allied algorithm is applied to a bivariate series of the Tokyo stock-price index (Topix) and the call rate.
収録刊行物
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- JOURNAL OF THE JAPAN STATISTICAL SOCIETY
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JOURNAL OF THE JAPAN STATISTICAL SOCIETY 37 (1), 1-28, 2007
日本統計学会
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詳細情報 詳細情報について
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- CRID
- 1390282680264486656
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- NII論文ID
- 110006317395
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- NII書誌ID
- AA1105098X
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- ISSN
- 13486365
- 18822754
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- NDL書誌ID
- 9304107
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- 本文言語コード
- en
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- 資料種別
- journal article
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- NDLサーチ
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