.png){kind=icon}
.png){kind=icon}
Return
Testing multiple equation systems for common nonlinear components
.png){kind=icon}
PRE
AI
DOI：10.1016/S0304-4076(97)00076-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper provides a generalized method of moments test for common nonlinear components in multiple time series. The test statistic is presented in terms of the canonical correlations between the multiple series and a judicially chosen set of test regressors, and its performance in small samples is evaluated using Monte Carlo simulations. Two applications highlight the usefulness of this test in multivariate modelling. The first shows that the asymmetries which characterize the US and Canadian business cycles can be attributed to a common nonlinear factor, while the second shows that a single nonlinear factor can account for neglected nonlinearity in an empirical real business cycle model. Each of the implied nonlinear factor models shows potential for forecasting. (C) 1998 Elsevier Science S.A.
Keywords:
canonical correlations
common-nonlinear components
generalized methods of moments
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W
Organization
No organization information available