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Identification of vector AR models with recursive structural errors using conditional independence graphs.

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Identification of vector AR models with recursive structural errors using conditional independence graphs. / Tunnicliffe Wilson, Granville; Reale, Marco.
In: Statistical Methods and Applications, Vol. 10, No. 1-3, 01.2001, p. 49-65.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tunnicliffe Wilson, G & Reale, M 2001, 'Identification of vector AR models with recursive structural errors using conditional independence graphs.', Statistical Methods and Applications, vol. 10, no. 1-3, pp. 49-65. https://doi.org/10.1007/BF02511639

APA

Tunnicliffe Wilson, G., & Reale, M. (2001). Identification of vector AR models with recursive structural errors using conditional independence graphs. Statistical Methods and Applications, 10(1-3), 49-65. https://doi.org/10.1007/BF02511639

Vancouver

Tunnicliffe Wilson G, Reale M. Identification of vector AR models with recursive structural errors using conditional independence graphs. Statistical Methods and Applications. 2001 Jan;10(1-3):49-65. doi: 10.1007/BF02511639

Author

Tunnicliffe Wilson, Granville ; Reale, Marco. / Identification of vector AR models with recursive structural errors using conditional independence graphs. In: Statistical Methods and Applications. 2001 ; Vol. 10, No. 1-3. pp. 49-65.

Bibtex

@article{9287725f4b9d4ed984519917b55eb954,
title = "Identification of vector AR models with recursive structural errors using conditional independence graphs.",
abstract = "In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such models having a recursive structure can be described by a directed acyclic graph. We show, with the use of a real example, how the identification of these models may be assisted by examination of the conditional independence graph of contemporaneous and lagged variables. In this example we identify the causal dependence of monthly Italian bank loan interest rates on government bond and repurchase agreement rates. When the number of series is larger, the structural modelling of the canonical errors alone is a useful initial step, and we first present such an example to demonstrate the general approach to identifying a directed graphical model.",
keywords = "Partial correlation - moralization - causality - graphical modelling - lending channel",
author = "{Tunnicliffe Wilson}, Granville and Marco Reale",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2001",
month = jan,
doi = "10.1007/BF02511639",
language = "English",
volume = "10",
pages = "49--65",
journal = "Statistical Methods and Applications",
issn = "1618-2510",
publisher = "Physica-Verlag",
number = "1-3",

}

RIS

TY - JOUR

T1 - Identification of vector AR models with recursive structural errors using conditional independence graphs.

AU - Tunnicliffe Wilson, Granville

AU - Reale, Marco

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2001/1

Y1 - 2001/1

N2 - In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such models having a recursive structure can be described by a directed acyclic graph. We show, with the use of a real example, how the identification of these models may be assisted by examination of the conditional independence graph of contemporaneous and lagged variables. In this example we identify the causal dependence of monthly Italian bank loan interest rates on government bond and repurchase agreement rates. When the number of series is larger, the structural modelling of the canonical errors alone is a useful initial step, and we first present such an example to demonstrate the general approach to identifying a directed graphical model.

AB - In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such models having a recursive structure can be described by a directed acyclic graph. We show, with the use of a real example, how the identification of these models may be assisted by examination of the conditional independence graph of contemporaneous and lagged variables. In this example we identify the causal dependence of monthly Italian bank loan interest rates on government bond and repurchase agreement rates. When the number of series is larger, the structural modelling of the canonical errors alone is a useful initial step, and we first present such an example to demonstrate the general approach to identifying a directed graphical model.

KW - Partial correlation - moralization - causality - graphical modelling - lending channel

U2 - 10.1007/BF02511639

DO - 10.1007/BF02511639

M3 - Journal article

VL - 10

SP - 49

EP - 65

JO - Statistical Methods and Applications

JF - Statistical Methods and Applications

SN - 1618-2510

IS - 1-3

ER -