### Abstract

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 [Econometrica (1992) Vol. 60, pp. 119-143]. We show that the estimator of the break fraction has the same convergence rate as obtained in Bai, Lumsdaine and Stock [Review of Economic Studies (1998) Vol. 65, pp. 395-432] and that the CCR estimator with the estimated break fraction has the same asymptotic property as the estimator with the known break point. However, we also show that our method breaks down when the magnitude of structural change is very small. Simulation experiments reveal how the finite sample distribution approaches the limiting distribution as the magnitude of the break and or the sample size increases.

Original language | English |
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Pages (from-to) | 545-575 |

Number of pages | 31 |

Journal | Journal of Time Series Analysis |

Volume | 28 |

Issue number | 4 |

DOIs | |

Publication status | Published - 2007 Jul 1 |

Externally published | Yes |

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### Keywords

- Canonical cointegrating regression
- Cointegration
- Estimation
- Single equation
- Statistical inference
- Structural change

### ASJC Scopus subject areas

- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics

### Cite this

**Efficient estimation and inference in cointegrating regressions with structural change.** / Kurozumi, Eiji; Arai, Yoichi.

Research output: Contribution to journal › Article

*Journal of Time Series Analysis*, vol. 28, no. 4, pp. 545-575. https://doi.org/10.1111/j.1467-9892.2006.00524.x

}

TY - JOUR

T1 - Efficient estimation and inference in cointegrating regressions with structural change

AU - Kurozumi, Eiji

AU - Arai, Yoichi

PY - 2007/7/1

Y1 - 2007/7/1

N2 - 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 [Econometrica (1992) Vol. 60, pp. 119-143]. We show that the estimator of the break fraction has the same convergence rate as obtained in Bai, Lumsdaine and Stock [Review of Economic Studies (1998) Vol. 65, pp. 395-432] and that the CCR estimator with the estimated break fraction has the same asymptotic property as the estimator with the known break point. However, we also show that our method breaks down when the magnitude of structural change is very small. Simulation experiments reveal how the finite sample distribution approaches the limiting distribution as the magnitude of the break and or the sample size increases.

AB - 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 [Econometrica (1992) Vol. 60, pp. 119-143]. We show that the estimator of the break fraction has the same convergence rate as obtained in Bai, Lumsdaine and Stock [Review of Economic Studies (1998) Vol. 65, pp. 395-432] and that the CCR estimator with the estimated break fraction has the same asymptotic property as the estimator with the known break point. However, we also show that our method breaks down when the magnitude of structural change is very small. Simulation experiments reveal how the finite sample distribution approaches the limiting distribution as the magnitude of the break and or the sample size increases.

KW - Canonical cointegrating regression

KW - Cointegration

KW - Estimation

KW - Single equation

KW - Statistical inference

KW - Structural change

UR - http://www.scopus.com/inward/record.url?scp=34250667450&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34250667450&partnerID=8YFLogxK

U2 - 10.1111/j.1467-9892.2006.00524.x

DO - 10.1111/j.1467-9892.2006.00524.x

M3 - Article

AN - SCOPUS:34250667450

VL - 28

SP - 545

EP - 575

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 4

ER -