Common large innovations across nonlinear time series
AbstractWe propose a multivariate nonlinear econometric time series model, which can beused to examine if there is common nonlinearity across economic variables. Themodel is a multivariate censored latent effects autoregression. The key featureof this model is that nonlinearity appears as separate innovation-likevariables. Common nonlinearity can then be easily defined as the presence ofcommon innovations. We discuss representation, inference, estimation anddiagnostics. We illustrate the model for US and Canadian unemployment and findthat US innovation variables have an effect on Canadian unemployment, and notthe other way around, and also that there is no common nonlinearity across theunemployment variables.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2002-09.
Date of creation: 01 Jan 2002
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Nonlinearity; Censored latent effects autoregression; Common features;
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- J. M. C. Santos Silva, 2001.
"A score test for non-nested hypotheses with applications to discrete data models,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 16(5), pages 577-597.
- J M C Santos Silva, 1996. "A Score Test for Non-nested Hypotheses with Applications to Discrete Data Models," Discussion Papers 96-28 ISSN 1350-6722, University College London, Department of Economics.
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