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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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Volume 17, Issue 3


Common large innovations across nonlinear time series

Philip Hans Franses / Richard Paap
Published Online: 2013-02-16 | DOI: https://doi.org/10.1515/snde-2012-0047


We propose a multivariate nonlinear econometric time series model, which can be used to examine if there is common nonlinearity across economic variables. The model is a multivariate censored latent effects autoregression. The key feature of this model is that nonlinearity appears as separate innovation-like variables. Common nonlinearity can then be easily defined as the presence of common innovations. We discuss representation, inference, estimation and diagnostics. We illustrate the model for US and Canadian unemployment and find that US innovation variables have an effect on Canadian unemployment, and not the other way around, and also that there is no common nonlinearity across the unemployment variables.

Keywords: nonlinearity; common features; censored latent effects autoregression


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About the article

Corresponding author: Philip Hans Franses, Erasmus University of Rotterdam, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands, Phone: +3110 4081273, Fax: +3110 4089162

Published Online: 2013-02-16

Published in Print: 2013-05-01

It is easy to see that σu12 is not identified if either v1t or v2t is positive. Hence, if one wants to allow for correlation between u1t and u2t its identification will be based on observations where both v1t and v2t are zero in which case there is limited information about the values of u1t and u2t Unreported estimation results show that the covariance is empirically badly identified. Therefore we opt for σu12.

Citation Information: Studies in Nonlinear Dynamics and Econometrics, Volume 17, Issue 3, Pages 251–263, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2012-0047.

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