### Abstract

Original language | English |
---|---|

Place of Publication | Ithaca |

Publisher | Cornell University Library |

Publication status | Published - 13 Nov 2018 |

### Publication series

Name | ArXiv |
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Volume | 1811.04125 |

### Fingerprint

### Keywords

- multiple break points
- instrumental variables estimation
- two-stage least squares
- wild bootstrap
- recursive bootstrap
- fixed-regressor bootstrap
- heteroskedasticity

### Cite this

*Bootstrapping Structural Change Tests*. (ArXiv; Vol. 1811.04125). Ithaca: Cornell University Library.

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**Bootstrapping Structural Change Tests.** / Boldea, Otilia; Cornea-Madeira, Adriana; Hall, Alastair R.

Research output: Working paper › Other research output

TY - UNPB

T1 - Bootstrapping Structural Change Tests

AU - Boldea, Otilia

AU - Cornea-Madeira, Adriana

AU - Hall, Alastair R.

PY - 2018/11/13

Y1 - 2018/11/13

N2 - 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 heteroskedasticity 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.

AB - 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 heteroskedasticity 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.

KW - multiple break points

KW - instrumental variables estimation

KW - two-stage least squares

KW - wild bootstrap

KW - recursive bootstrap

KW - fixed-regressor bootstrap

KW - heteroskedasticity

M3 - Working paper

T3 - ArXiv

BT - Bootstrapping Structural Change Tests

PB - Cornell University Library

CY - Ithaca

ER -