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.

Additional Metadata
Keywords censored latent effects autoregression, common features, nonlinearity
Persistent URL dx.doi.org/10.1515/snde-2012-0047, hdl.handle.net/1765/40232
Series Econometric Institute Reprint Series
Journal Studies in Nonlinear Dynamics & Econometrics
Citation
Franses, Ph.H.B.F, & Paap, R. (2013). Common large innovations across nonlinear time series . Studies in Nonlinear Dynamics & Econometrics, 17(3), 251–263. doi:10.1515/snde-2012-0047