A multi‐level panel STAR model for US manufacturing sectors

We introduce a multi-level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second-stage model linking the parameters that determine the timing of the switches between business cycle regimes to observable explanatory variables, thereby allowing for lead-lag relationships across panel members. We discuss representation, estimation by concentrated simulated maximum likelihood and inference. We illustrate our model using quarterly industrial production in 19 US manufacturing sectors, and document that there are subtle differences across sectors in leads and lags for switches between business cycle recessions and expansions. Copyright © 2005 John Wiley & Sons, Ltd.

[1]  J. MacKinnon,et al.  Estimation and inference in econometrics , 1994 .

[2]  Philip Hans Franses,et al.  Testing for Smooth Transition Nonlinearity in the Presence of Outliers , 1996 .

[3]  M. Pesaran,et al.  Persistence, cointegration, and aggregation : A disaggregated analysis of output fluctuations in the U.S. economy , 1993 .

[4]  Clive W. J. Granger,et al.  The effect of aggregation on nonlinearity , 1999 .

[5]  Michael T. Owyang,et al.  Business Cycle Phases in U.S. States , 2003 .

[6]  T. Teräsvirta,et al.  Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models , 1992 .

[7]  Suzanne J. Cooper.  Multiple Regimes in U.S. Output Fluctuations , 1998 .

[8]  C. Granger,et al.  Modelling Nonlinear Economic Relationships , 1995 .

[9]  Alain Monfort,et al.  Simulation-based inference: A survey with special reference to panel data models , 1993 .

[10]  G. Janacek.  Non‐linear Time Series Models in Empirical Finance , 2003 .

[11]  Farshid Vahid,et al.  Testing multiple equation systems for common nonlinear components , 1998 .

[12]  Frederic S. Mishkin,et al.  Predicting U.S. Recessions: Financial Variables as Leading Indicators , 1995, Review of Economics and Statistics.

[13]  Prasad V. Bidarkota.  Sectoral Investigation of Asymmetries in the Conditional Mean Dynamics of the Real U.S. GDP , 1999 .

[14]  Timo Teräsvirta,et al.  Smooth transition autoregressive models - A survey of recent developments , 2000 .

[15]  Kevin Lee,et al.  Modelling economic growth in the UK: An econometric case for disaggregated sectoral analysis , 1997 .

[16]  Paul A. Ruud,et al.  Handbook of Econometrics: Classical Estimation Methods for LDV Models Using Simulation , 1993 .

[17]  Timo Teräsvirta,et al.  Modelling Economic Relationships with Smooth Transition Regressions , 1996 .

[18]  Farshid Vahid.  Partial pooling: A possible answer to "To pool or not to pool" , 1999 .

[19]  Aggregate and regional disaggregate fluctuations , 1996 .

[20]  Lung-fei Lee,et al.  Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models , 1995, Econometric Theory.

[21]  P. Franses,et al.  Does Africa grow slower than Asia, Latin America and the Middle East? Evidence from a new data-based classification method , 2005 .

[22]  W. Gray,et al.  The NBER Manufacturing Productivity Database , 1996 .

[23]  Marco Del Negro.  Asymmetric shocks among U.S. states , 2002 .

[24]  Robert F. Engle,et al.  Estimating common sectoral cycles , 1995 .

[25]  Todd E. Clark.  Employment Fluctuations in U.S. Regions and Industries: The Roles of National, Region‐Specific, and Industry‐Specific Shocks , 1998, Journal of Labor Economics.

[26]  Lucrezia Reichlin,et al.  Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics , 1998 .

[27]  Frank A. Wolak,et al.  Local and Global Testing of Linear and Nonlinear Inequality Constraints in Nonlinear Econometric Models , 1989, Econometric Theory.

[28]  T. Teräsvirta.  Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models , 1994 .