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 |
|---|---|
| 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.
In: Journal of Time Series Analysis, Vol. 28, No. 4, 01.07.2007, p. 545-575.Research output: Contribution to journal › Article
}
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 -