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Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

Published online by Cambridge University Press:  24 August 2021

Szabolcs Blazsek ,
Alvaro Escribano  and
Adrian Licht
Szabolcs Blazsek
Affiliation:
School of Business, Universidad Francisco Marroquín, Guatemala City (Guatemala), Guatemala
Alvaro Escribano*
Affiliation:
Department of Economics, Universidad Carlos III de Madrid, Getafe (Madrid), Spain
Adrian Licht
Affiliation:
School of Business, Universidad Francisco Marroquín, Guatemala City (Guatemala), Guatemala
*
*Corresponding author. Email: [email protected].
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Abstract

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Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

Keywords

Nonlinear co-integrationnonlinear common trendsdynamic conditional score (DCS)generalized autoregressive score (GAS)quasi-vector autoregressive moving average (QVARMA)62M1091B84
Type
Articles
Information
Macroeconomic Dynamics , Volume 27 , Issue 1 , January 2023 , pp. 203 - 223
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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