A new structural break test for panels with common factors

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing GMM methodology and develops Distance-type and LM-type tests for detecting a structural break, both when the breakpoint is known as well as when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002-2014.
Original languageEnglish
Number of pages20
JournalEconometrics Journal
DOIs
Publication statusAccepted/In press - 2019

Keywords

  • Method of moments
  • Unobserved heterogeneity
  • Break-point detection
  • Fixed T asymptotics

Cite this

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title = "A new structural break test for panels with common factors",
abstract = "This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing GMM methodology and develops Distance-type and LM-type tests for detecting a structural break, both when the breakpoint is known as well as when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002-2014.",
keywords = "Method of moments, Unobserved heterogeneity, Break-point detection, Fixed T asymptotics",
author = "Huanjun Zhu and Vasilis Sarafidis and Silvapulle, {Mervyn J.}",
year = "2019",
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A new structural break test for panels with common factors. / Zhu, Huanjun; Sarafidis, Vasilis; Silvapulle, Mervyn J.

In: Econometrics Journal, 2019.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A new structural break test for panels with common factors

AU - Zhu, Huanjun

AU - Sarafidis, Vasilis

AU - Silvapulle, Mervyn J.

PY - 2019

Y1 - 2019

N2 - This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing GMM methodology and develops Distance-type and LM-type tests for detecting a structural break, both when the breakpoint is known as well as when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002-2014.

AB - This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing GMM methodology and develops Distance-type and LM-type tests for detecting a structural break, both when the breakpoint is known as well as when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002-2014.

KW - Method of moments

KW - Unobserved heterogeneity

KW - Break-point detection

KW - Fixed T asymptotics

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JO - Econometrics Journal

JF - Econometrics Journal

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