A comparison of alternative methods to construct confidence intervals for the estimate of a break date in linear regression models

Chang, Seong Yeon; Perron, Pierre

doi:10.1080/07474938.2015.1122142

Cited by 27 publications

(31 citation statements)

References 30 publications

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“…This statistic can be used to construct con dence sets for the breaks. This issue has recently received some attention in linear models; e.g., see Elliott and Mueller (2007), Eo and Morley (2015), and Chang and Perron (2015). Unlike these previous methods, our approach treats the breaks jointly rather than constructing individual intervals for each break.…”

Section: Introductionmentioning

confidence: 99%

The asymptotic behaviour of the residual sum of squares in models with multiple break points

Hall

Osborn

Sakkas

2017

Econometric Reviews

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Models with multiple discrete breaks in parameters are usually estimated via least squares. This paper, rst, derives the asymptotic expectation of the residual sum of squares and shows that the number of estimated break points and the number of regression parameters a ect the expectation di erently. Second, we propose a statistic for testing the joint hypothesis that the breaks occur at speci ed points in the sample. Our analytical results cover models estimated by the ordinary, nonlinear, and two-stage least squares. An application to U.S. monetary policy rejects the assumption that breaks are associated with changes in the chair of the Fed.

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Section: Introductionmentioning

confidence: 99%

The asymptotic behaviour of the residual sum of squares in models with multiple break points

Hall

Osborn

Sakkas

2017

Econometric Reviews

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“…5 The wild fixed-regressor bootstrap is also included in the recent simulation study exploring the finite sample properties of inference methods about the location of the break-point in models estimated via OLS reported in Chang and Perron (2018). 6 Chang and Perron (2018) report results from a comprehensive simulation study that investigates the finite sample methods of various methods for constructing confidence intervals for the break fractions in linear regression models with exogenous regressors. They consider variants of the intervals based on i.i.d., wild and sieve bootstraps.…”

Section: The Modelmentioning

confidence: 99%

Bootstrapping structural change tests

Boldea

Cornea‐Madeira

Hall

2019

Journal of Econometrics

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This paper analyses the use of bootstrap methods to test for parameter change in linear models estimated via Two Stage Least Squares (2SLS). Two types of test are considered: one where the null hypothesis is of no change and the alternative hypothesis involves discrete change at k unknown break-points in the sample; and a second test where the null hypothesis is that there is discrete parameter change at l break-points in the sample against an alternative in which the parameters change at l + 1 break-points. In both cases, we consider inferences based on a sup-W ald-type statistic using either the wild recursive bootstrap or the wild fixed bootstrap. We establish the asymptotic validity of these bootstrap tests under a set of general conditions that allow the errors to exhibit conditional and/or unconditional heteroskedasticity, and report results from a simulation study that indicate the tests yield reliable inferences in the sample sizes often encountered in macroeconomics. The analysis covers the cases where the first-stage estimation of 2SLS involves a model whose parameters are either constant or themselves subject to discrete parameter change. If the errors exhibit unconditional heteroscedasticity and/or the reduced form is unstable then the bootstrap methods are particularly attractive because the limiting distributions of the test statistics are not pivotal.JEL classification: C12, C13, C15, C22concluding the proof.where M i is defined as in Lemma 6, andFor the proofs of Lemma 10-11, it suffices to consider S b † = S u or S b † =β x,# , therefore considering m = 0. If S b † = (β x,(i) ) # , by Lemma 7 followed by standard 2SLS theory,β x,(i) = β 0 x,(i) +O p (T −1/2 ) so S b † = S † +O p (T −1/2 ),

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“…In this case, the crucial assumption made for the construction of the confidence interval is that the magnitude of the structural break shrinks to 0 at a rate slower than

1 / \sqrt{T}

, as also assumed in BLS and Kejriwal and Perron (). However, as demonstrated by Elliott and Müller () and Chang and Perron (), a confidence interval based on the limiting distribution of the break point estimator tends to be too liberal when the magnitude of the break is not so large. Instead of using the limiting distribution of the change point estimator, Elliott and Müller () propose constructing a confidence interval by inverting the locally best invariant test for the break location, which helps control the coverage rate .…”

Section: Introductionmentioning

confidence: 99%

Confidence Sets for the Break Date in Cointegrating Regressions

Kurozumi¹,

Skrobotov

2017

Oxf Bull Econ Stat

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In this paper, we propose constructing confidence sets for a break date in cointegrating regressions by inverting a test for the break location, which is obtained by maximizing the weighted average of power. It is found that the limiting distribution of the test depends on the number of I(1) regressors whose coefficients sustain structural change and the number of I(1) regressors whose coefficients are fixed throughout the sample. By Monte Carlo simulations, we then show that compared with a confidence interval developed by using the existing method based on the limiting distribution of the break point estimator under the assumption of the shrinking shift, the confidence set proposed in the present paper has a more accurate coverage rate, while the length of the confidence set is comparable. By using the method developed in this paper, we then investigate the cointegrating regressions of Russian macroeconomic variables with oil prices with a break.JEL classification: C12, C21

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A comparison of alternative methods to construct confidence intervals for the estimate of a break date in linear regression models

Cited by 27 publications

References 30 publications

The asymptotic behaviour of the residual sum of squares in models with multiple break points

The asymptotic behaviour of the residual sum of squares in models with multiple break points

Bootstrapping structural change tests

Confidence Sets for the Break Date in Cointegrating Regressions

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