Testing for co-integration in vector autoregressions with non-stationary volatility

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Testing for co-integration in vector autoregressions with non-stationary volatility. / Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.

In: Journal of Econometrics, Vol. 158, No. 1, 2010, p. 7-24.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Cavaliere, G, Rahbek, AC & Taylor, RM 2010, 'Testing for co-integration in vector autoregressions with non-stationary volatility', Journal of Econometrics, vol. 158, no. 1, pp. 7-24. https://doi.org/10.1016/j.jeconom.2010.03.003

APA

Cavaliere, G., Rahbek, A. C., & Taylor, R. M. (2010). Testing for co-integration in vector autoregressions with non-stationary volatility. Journal of Econometrics, 158(1), 7-24. https://doi.org/10.1016/j.jeconom.2010.03.003

Vancouver

Cavaliere G, Rahbek AC, Taylor RM. Testing for co-integration in vector autoregressions with non-stationary volatility. Journal of Econometrics. 2010;158(1):7-24. https://doi.org/10.1016/j.jeconom.2010.03.003

Author

Cavaliere, Giuseppe ; Rahbek, Anders Christian ; Taylor, Robert M. / Testing for co-integration in vector autoregressions with non-stationary volatility. In: Journal of Econometrics. 2010 ; Vol. 158, No. 1. pp. 7-24.

Bibtex

@article{64a87ec0c53b11debda0000ea68e967b,
title = "Testing for co-integration in vector autoregressions with non-stationary volatility",
abstract = "Many key macroeconomic and financial variables are characterized by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special cases. We show that the conventional rank statistics computed as in (Johansen, 1988) and (Johansen, 1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, or to assume that the pattern of volatility is common to, or independent across, the vector of series under analysis. The bootstrap is shown to perform very well in practice.",
keywords = "Faculty of Social Sciences, co-integration, non-stationary volatility, trace and maximum eigenvalue tests, wild bootstrap",
author = "Giuseppe Cavaliere and Rahbek, {Anders Christian} and Taylor, {Robert M.}",
note = "JEL classification: C30, C32",
year = "2010",
doi = "10.1016/j.jeconom.2010.03.003",
language = "English",
volume = "158",
pages = "7--24",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Testing for co-integration in vector autoregressions with non-stationary volatility

AU - Cavaliere, Giuseppe

AU - Rahbek, Anders Christian

AU - Taylor, Robert M.

N1 - JEL classification: C30, C32

PY - 2010

Y1 - 2010

N2 - Many key macroeconomic and financial variables are characterized by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special cases. We show that the conventional rank statistics computed as in (Johansen, 1988) and (Johansen, 1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, or to assume that the pattern of volatility is common to, or independent across, the vector of series under analysis. The bootstrap is shown to perform very well in practice.

AB - Many key macroeconomic and financial variables are characterized by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special cases. We show that the conventional rank statistics computed as in (Johansen, 1988) and (Johansen, 1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, or to assume that the pattern of volatility is common to, or independent across, the vector of series under analysis. The bootstrap is shown to perform very well in practice.

KW - Faculty of Social Sciences

KW - co-integration

KW - non-stationary volatility

KW - trace and maximum eigenvalue tests

KW - wild bootstrap

U2 - 10.1016/j.jeconom.2010.03.003

DO - 10.1016/j.jeconom.2010.03.003

M3 - Journal article

VL - 158

SP - 7

EP - 24

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -

ID: 15456840