We introduce a multi-level smooth transition model for a panel of time series variables, which can be used to examine the presence of common non-linear features across many such variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which might render estimation problems for some of the panel members. To keep the model tractable, we introduce a second-stage model, which links the parameters in the transition functions with observable explanatory variables. We discuss representation, estimation by concentrated simulated maximum likelihood and inference. We illustrate our model for data on industrial production of 18 US manufacturing sectors, and document that there are subtle differences across sectors in leads and lags for business cycle recessions and expansions.

Additional Metadata
Keywords business cycle, non-linearity, panel of time series
JEL Models with Panel Data (jel C23), Model Construction and Estimation (jel C51), Business Fluctuations; Cycles (jel E32)
Persistent URL hdl.handle.net/1765/1054
Series Econometric Institute Research Papers
Fok, D, van Dijk, D.J.C, & Franses, Ph.H.B.F. (2003). A multi-level panel smooth transition autoregression for US sectoral production (No. EI 2003-43). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1054