WEKO3
アイテム
Efficient Estimation and Inference in Cointegrating Regressions with Structural Change
http://hdl.handle.net/10086/16920
http://hdl.handle.net/10086/1692018f9a702-efd1-48b8-a2a9-dafb9ddf57d9
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
070econDP04-09.pdf (366449 bytes)
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| アイテムタイプ | デフォルトアイテムタイプ（フル）その２(1) | |||||||||||||||
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| 公開日 | 2017-05-20 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Efficient Estimation and Inference in Cointegrating Regressions with Structural Change | |||||||||||||||
| 言語 | en | |||||||||||||||
| 作成者 |
黒住, 英司
× 黒住, 英司
WEKO
27
× 荒井, 洋一
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| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||
| 出版者 | ||||||||||||||||
| 出版者 | Graduate School of Economics, Hitotsubashi University | |||||||||||||||
| 日付 | ||||||||||||||||
| 日付 | 2005-01 | |||||||||||||||
| 日付タイプ | Issued | |||||||||||||||
| 言語 | ||||||||||||||||
| 言語 | eng | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||||||||
| 資源タイプ | technical report | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| 関連情報 | ||||||||||||||||
| 関連タイプ | isPartOf | |||||||||||||||
| 関連名称 | Discussion papers ; No. 2004-09 | |||||||||||||||
| ページ数 | ||||||||||||||||
| ページ数 | 28 | |||||||||||||||
| 抄録（第三者提供不可） | ||||||||||||||||
| 値 | This paper investigates an efficient estimation method for a cointegrating regression model with structural change. Our proposal is that we first estimate the break point by minimizing the sum of squared residuals and then, by replacing the break fraction with the estimated one, we estimate the regression model by the canonical cointegrating regression (CCR) method proposed by Park (1992). We show that the estimator of the break fraction is consistent and of order faster than T -1/2 and that the CCR estimator with the estimated break fraction has the same asymptotic property as the estimator with the known break point. Simulation experiments show how the finite sample distribution gets close to the limiting distribution as the magnitude of the break and/or the sample size increases. | |||||||||||||||
