scholarly journals Tests for Parameter Instability and Structural Change With Unknown Change Point

Econometrica ◽  
1993 ◽  
Vol 61 (4) ◽  
pp. 821 ◽  
Author(s):  
Donald W. K. Andrews
Keyword(s):  
Structural Change ◽  
Change Point ◽  
Parameter Instability

Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum

Econometrica ◽  
2003 ◽  
Vol 71 (1) ◽  
pp. 395-397 ◽  
Author(s):  
Donald W. K. Andrews
Keyword(s):  
Structural Change ◽  
Change Point ◽  
Parameter Instability

Finding division points for a time series corpus based on structural change point detection

2016 ◽  
Vol 21 (2) ◽  
pp. 160-164
Author(s):  
Hiroshi Kobayashi ◽  
Ryosuke Saga
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Change Point ◽  
Change Point Detection ◽  
Point Detection

scholarly journals Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions

2021 ◽  
Author(s):  
Torben Andersen ◽  
Rasmus Varneskov
Keyword(s):  
Structural Change ◽  
Parameter Instability ◽  
Predictive Regressions

TESTS FOR STRUCTURAL CHANGE IN COINTEGRATED SYSTEMS

1998 ◽  
Vol 14 (2) ◽  
pp. 222-259 ◽  
Author(s):  
Byeongseon Seo
Keyword(s):  
Structural Change ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Error Correction ◽  
Change Point ◽  
Likelihood Estimation ◽  
Asymptotic Distributions ◽  
Correction Model ◽  
Cointegrating Vector ◽  
Economic Statistics

This paper considers tests for structural change of the cointegrating vector and the adjustment vector in the error correction model with an unknown change point. This paper derives new tests for structural change, which are applicable to maximum likelihood estimation. Our tests for structural change of the cointegrating vector have the same nonstandard asymptotic distributions that have been found by Hansen (1992a, Journal of Business and Economic Statistics 10, 321–335). In contrast, the tests on the adjustment vector have the same asymptotic distributions that have been found by Andrews and Ploberger (1994, Econometrica 62, 1383–1414) for models with stationary variables. Asymptotic critical values are provided.

STRUCTURAL CHANGE IN AR(1) MODELS

2001 ◽  
Vol 17 (1) ◽  
pp. 87-155 ◽  
Author(s):  
Terence Tai-Leung Chong
Keyword(s):  
Structural Change ◽  
Sample Size ◽  
Least Squares ◽  
Unit Root ◽  
Change Point ◽  
Structural Break ◽  
Break Point ◽  
Point Estimate ◽  
Least Squares Estimators ◽  
Point Estimator

This paper investigates the consistency of the least squares estimators and derives their limiting distributions in an AR(1) model with a single structural break of unknown timing. Let β1 and β2 be the preshift and postshift AR parameter, respectively. Three cases are considered: (i) |β1| < 1 and |β2| < 1; (ii) |β1| < 1 and β2 = 1; and (iii) β1 = 1 and |β2| < 1. Cases (ii) and (iii) are of particular interest but are rarely discussed in the literature. Surprising results are that, in both cases, regardless of the location of the change-point estimate, the unit root can always be consistently estimated and the residual sum of squares divided by the sample size converges to a discontinuous function of the change point. In case (iii), [circumflex over beta]2 does not converge to β2 whenever the change-point estimate is lower than the true change point. Further, the limiting distribution of the break-point estimator for shrinking break is asymmetric for case (ii), whereas those for cases (i) and (iii) are symmetric. The appropriate shrinking rate is found to be different in all cases.

scholarly journals The structural change of mutual fund herding in China stock market

2015 ◽  
Vol 12 (3) ◽  
pp. 48-54
Author(s):  
Wanbin Pan ◽  
Jun Shan
Keyword(s):  
Structural Change ◽  
Stock Market ◽  
Mutual Fund ◽  
Change Point ◽  
Change Points ◽  
Investment Behavior ◽  
China Stock Market ◽  
Kolmogorov Smirnov ◽  
Financial Environment ◽  
Smirnov Test

This paper examines the structural change of mutual fund herding in china stock market，an important investment behavior of institutional investors. A self-normalization based Kolmogorov-Smirnov test is employed to test the change point of herding from 2002 to 2011, the results suggest that there really be structural change points in mutual fund herding. The mutual fund herding changed at December 2004, June 2007 and December 2008. The structural change of the mutual fund herding can be explained by the financial environment of China stock market.

scholarly journals Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions

2020 ◽  
Author(s):  
Torben G. Andersen ◽  
Rasmus Tangsgaard Varneskov
Keyword(s):  
Structural Change ◽  
Parameter Instability ◽  
Predictive Regressions
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