Department of Economics and Business Economics

Noncausal vector autoregression

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Noncausal vector autoregression. / Lanne, M.; Saikkonen, P.

In: Econometric Theory, Vol. 29, No. 3, 01.06.2013, p. 447-481.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Lanne, M & Saikkonen, P 2013, 'Noncausal vector autoregression', Econometric Theory, vol. 29, no. 3, pp. 447-481. https://doi.org/10.1017/S0266466612000448

APA

Lanne, M., & Saikkonen, P. (2013). Noncausal vector autoregression. Econometric Theory, 29(3), 447-481. https://doi.org/10.1017/S0266466612000448

CBE

Lanne M, Saikkonen P. 2013. Noncausal vector autoregression. Econometric Theory. 29(3):447-481. https://doi.org/10.1017/S0266466612000448

MLA

Lanne, M. and P. Saikkonen. "Noncausal vector autoregression". Econometric Theory. 2013, 29(3). 447-481. https://doi.org/10.1017/S0266466612000448

Vancouver

Lanne M, Saikkonen P. Noncausal vector autoregression. Econometric Theory. 2013 Jun 1;29(3):447-481. https://doi.org/10.1017/S0266466612000448

Author

Lanne, M. ; Saikkonen, P. / Noncausal vector autoregression. In: Econometric Theory. 2013 ; Vol. 29, No. 3. pp. 447-481.

Bibtex

@article{0fb99847cefc43dbbcee358ec3758999,
title = "Noncausal vector autoregression",
abstract = "In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.",
author = "M. Lanne and P. Saikkonen",
year = "2013",
month = jun,
day = "1",
doi = "10.1017/S0266466612000448",
language = "English",
volume = "29",
pages = "447--481",
journal = "Econometric Theory",
issn = "0266-4666",
publisher = "Cambridge University Press",
number = "3",

}

RIS

TY - JOUR

T1 - Noncausal vector autoregression

AU - Lanne, M.

AU - Saikkonen, P.

PY - 2013/6/1

Y1 - 2013/6/1

N2 - In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.

AB - In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.

UR - http://www.scopus.com/inward/record.url?scp=84878309522&partnerID=8YFLogxK

U2 - 10.1017/S0266466612000448

DO - 10.1017/S0266466612000448

M3 - Journal article

AN - SCOPUS:84878309522

VL - 29

SP - 447

EP - 481

JO - Econometric Theory

JF - Econometric Theory

SN - 0266-4666

IS - 3

ER -