1995
DOI: 10.1080/03610919508813291
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Asymptotically exact confidence intervals of cusum and cusumsq tests: a numerical derivation using simulation technique

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Cited by 22 publications
(17 citation statements)
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“…The study further employs the cumulative sum of recursive residual (CUSUM) and the cumulative sum of squares of recursive residual (CUSUMsq) to test stability of the long-run and the short-run parameters of the model. According to Tanizaki (2001), the CUSUM and CUSUMsq are of paramount importance if one is not certain on when the structural change might have taken place and the methods are perfect for stationary data. The null hypothesis of both the CUSUM and CUSUMsq is that coefficient vectors are the same in every period.…”
Section: Diagnostic Test and Stability Testsmentioning
confidence: 99%
“…The study further employs the cumulative sum of recursive residual (CUSUM) and the cumulative sum of squares of recursive residual (CUSUMsq) to test stability of the long-run and the short-run parameters of the model. According to Tanizaki (2001), the CUSUM and CUSUMsq are of paramount importance if one is not certain on when the structural change might have taken place and the methods are perfect for stationary data. The null hypothesis of both the CUSUM and CUSUMsq is that coefficient vectors are the same in every period.…”
Section: Diagnostic Test and Stability Testsmentioning
confidence: 99%
“…We will partially implement the techniques used in (Tanizaki, 1995) for confidence intervals calculation. The simulation algorithm may be carried out as follows.…”
Section: Modification Of Cusum Testmentioning
confidence: 99%
“…Sinceθ * i is computed based on the artificially simulated data, θ is numerically derived by the simulation technique (see, for example, Tanizaki (1995) for the numerical optimization procedure). Using equation (4), we update the parameter θ as follows:…”
Section: Appendix 2: Optimization Proceduresmentioning
confidence: 99%