Modelling structural breaks, long memory and stock market volatility: an overview

Banerjee, Anindya; Urga, Giovanni

doi:10.1016/j.jeconom.2004.09.001

Cited by 158 publications

(82 citation statements)

References 162 publications

(131 reference statements)

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“…This is standard in the structural change literature (e.g. Bai and Perron, 1998;Banerjee and Urga 2005). where T* is a compact subset of the R q Euclidean space.…”

Section: The Australian Rainfall Datamentioning

confidence: 99%

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Modelling Australian annual mean rainfall data: a new approach based on fractional integration

Gil-Alana¹

2009

AMOJ

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This paper analyses Australian annual rainfall data from 1900 to 2006. We use new statistical techniques based on the concept of fractional integration, which allow us to examine the dynamic behaviour of the series in a much richer way than the classic approaches based on stationary or nonstationary processes. Moreover, the possibility of a structural break at an unknown date is also taken into account. The results indicate that a break takes place at 1973, the time trend coefficients being insignificant in the two subsamples, and the degree of dependence between the observations is higher after the break. Regional data are also explored and the results show significant differences in the degree of persistence across the regions, especially after the breaks.

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“…This is standard in the structural change literature (e.g. Bai and Perron, 1998;Banerjee and Urga 2005). where T* is a compact subset of the R q Euclidean space.…”

Section: The Australian Rainfall Datamentioning

confidence: 99%

“…It is in fact a standard practice in the time series literature to remove the first and last 10% of observations to detect structural breaks (e.g. Bai and Perron 1998;Banerjee and Urga 2005).…”

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confidence: 99%

Modelling Australian annual mean rainfall data: a new approach based on fractional integration

Gil-Alana¹

2009

AMOJ

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“…Granger and Hyung (2004) explained long memory phenomenon of asset returns by structural changes in GARCH and suggested that the time series with structural breaks can induce a strong persistence in the autocorrelation function and hence generate spurious long memory. Banerjee and Urga (2005) provide a comprehensive survey of the literature on both long memory and structural breaks, features of which are almost observationally equivalent.…”

Section: Introductionmentioning

confidence: 99%

Long memory in return structures from developed markets

Bhattacharya¹,

Bhattacharya²

2013

cuadernosgestion

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“…Useful surveys can be found in Banerjee and Urga (2005) and Perron (2006). When extending the framework to a multivariate setting, the literature has shown that the cross sectional dimension can lead to better inference; for example, Bai, Lumsdaine and Stock (1998) show that the estimation of the changepoint in a VAR improves with the dimension of the VAR, due to the presence of cross sectional information.…”

Section: Introductionmentioning

confidence: 99%

Testing for Breaks in Cointegrated Panels with Common and Idiosyncratic Stochastic Trends

2011

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In this paper, we develop tests for structural change in cointegrated panel regressions with common and idiosyncratic trends. We consider both the cases of observable and nonobservable common trends, deriving a Functional Central Limit Theorem for the partial sample estimators under the null of no break. We show that tests based on sup-Wald statistics are powerful versus breaks of size   Abstract In this paper, we develop tests for structural change in cointegrated panel regressions with common and idiosyncratic trends. We consider both the cases of observable and nonobservable common trends, deriving a Functional Central Limit Theorem for the partial sample estimators under the null of no break. We show that test based on sup-Wald statistics are powerful versus breaks of size   (1 √  ), also proving that power is present when the time of change differs across units and when only some units have a break. Our framework is extended to the case of cross correlated regressors and endogeneity. Monte Carlo evidence shows that the tests have the correct size and good power properties.JEL codes: C23.

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Modelling structural breaks, long memory and stock market volatility: an overview

Abstract: The main aim of this volume is to present key recent developments in the fields of modelling structural breaks, and the analysis of long memory and stock market volatility.

Cited by 158 publications

References 162 publications

Modelling Australian annual mean rainfall data: a new approach based on fractional integration

Modelling Australian annual mean rainfall data: a new approach based on fractional integration

Long memory in return structures from developed markets

Testing for Breaks in Cointegrated Panels with Common and Idiosyncratic Stochastic Trends

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