Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71426
Title: A mixed copula-based vector autoregressive model for econometric analysis
Authors: Woraphon Yamaka
Sukrit Thongkairat
Keywords: Computer Science
Engineering
Issue Date: 1-Sep-2020
Abstract: © 2020 World Scientific Publishing Company. In many practical applications, the dynamics of different quantities is reasonably well described by linear equations. In economics, such linear dynamical models are known as vector autoregressive (VAR) models. These linear models are, however, only approximate. The deviations of the actual value of each quantity from the predictions of the linear model are usually well described by normal or Student-t distributions. To complete the description of the joint distribution of all these deviations, we need to supplement these marginal distributions with the information about the corresponding copula. To describe this dependence, in the past, researchers followed the usual idea of trying copulas from several standard families: Gaussian, Student, Clayton, Frank, Gumbel, and Joe families. To get a better description, we propose to also use convex combinations of copulas from different families; such convex combinations are known as mixed copulas. On the example of the dynamics of US macroeconomic data, including GDP, unemployment, consumer price index, and the real effective exchange rate, we show that mixed copulas indeed lead to a better description of the actual data. Specifically, it turns out that the best description is obtained if we use a convex combination of Student and Frank copulas.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85095966048&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/71426
ISSN: 02184885
Appears in Collections:CMUL: Journal Articles

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