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A Multi-Level Panel Smooth Transition Autoregression for US Sectoral Production

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Author Info

  • P.H. Franses
  • D. Fok
  • D. van Dijk

Abstract

Macroeconomic time series are often obtained as an aggregate across regions or economic sectors. Even when the ultimate goal is to forecast the aggregate series it may be beneficial to consider the underlying disaggregate series. This especially holds when the disaggregate series are generated by a non-linear process. The aggregate of such series follows a very complicated process. Aggregating a number of relatively simple models for individual regions or sectors to a model of the macro series may lead to a more accurate description than when a model for the aggregate is considered. 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.

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 267.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:267

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Keywords: Panel of time series; business cycle; non-linearity;

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References

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  1. Mario Forni & Lucrezia Reichlin, 1998. "Let's get real: a factor analytical approach to disaggregated business cycle dynamics," ULB Institutional Repository 2013/10147, ULB -- Universite Libre de Bruxelles.
  2. Robert F. Engle & João Victor Issler, 1993. "Common trends and common cycles in Latin America," Revista Brasileira de Economia, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 47(2), pages 149-176, April.
  3. Engle, Robert F. & Issler, Joao Victor, 1995. "Estimating common sectoral cycles," Journal of Monetary Economics, Elsevier, vol. 35(1), pages 83-113, February.
  4. Del Negro, Marco, 2002. "Asymmetric shocks among U.S. states," Journal of International Economics, Elsevier, vol. 56(2), pages 273-297, March.
  5. Vahid, Farshid & Engle, Robert F., 1997. "Codependent cycles," Journal of Econometrics, Elsevier, vol. 80(2), pages 199-221, October.
  6. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207, September.
  7. Lee, Kevin, 1997. "Modelling economic growth in the UK: An econometric case for disaggregated sectoral analysis," Economic Modelling, Elsevier, vol. 14(3), pages 369-394, July.
  8. Wolak, Frank A., 1989. "Local and Global Testing of Linear and Nonlinear Inequality Constraints in Nonlinear Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(01), pages 1-35, April.
  9. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
  10. Bidarkota Prasad V., 1999. "Sectoral Investigation of Asymmetries in the Conditional Mean Dynamics of the Real U.S. GDP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(4), pages 1-12, January.
  11. Eric J. Bartelsman & Wayne Gray, 1996. "The NBER Manufacturing Productivity Database," NBER Technical Working Papers 0205, National Bureau of Economic Research, Inc.
  12. Lee, Lung-Fei, 1995. "Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models," Econometric Theory, Cambridge University Press, vol. 11(03), pages 437-483, June.
  13. Vahid, F & Engle, Robert F, 1993. "Common Trends and Common Cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 341-60, Oct.-Dec..
  14. Gourieroux, Christian & Monfort, Alain, 1993. "Simulation-based inference : A survey with special reference to panel data models," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 5-33, September.
  15. Anderson, Heather M. & Vahid, Farshid, 1998. "Testing multiple equation systems for common nonlinear components," Journal of Econometrics, Elsevier, vol. 84(1), pages 1-36, May.
  16. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, September.
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Cited by:
  1. Oleg Korenok & Bruce Mizrach & Stan Radchenko, 2004. "The Microeconomics of Macroeconomic Asymmetries: Sectoral Driving Forces and Firm Level Characteristics," Departmental Working Papers 200405, Rutgers University, Department of Economics.

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