Forecasting aggregates using panels of nonlinear time series
AbstractMacroeconomic time series such as total unemployment or total industrial production concern data which are aggregated across regions, sectors, or age categories. In this paper we examine if forecasts for these aggregates can be improved by considering panel models for the disaggregate series. As many macroeconomic variables have nonlinear properties, we specifically focus on panels of nonlinear time series. We discuss the representation of such models, parameter estimation and a method to generate forecasts. We illustrate the usefulness of our approach for simulated data and for the US coincident index, making use of state-specific component series.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2004-44.
Date of creation: 05 Nov 2004
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business cycle; panel of time series; nonlinearity; multi-level models; data aggregation;
Other versions of this item:
- Fok, Dennis & van Dijk, Dick & Franses, Philip Hans, 2005. "Forecasting aggregates using panels of nonlinear time series," International Journal of Forecasting, Elsevier, vol. 21(4), pages 785-794.
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- Michael T. Owyang & Jeremy M. Piger & Howard J. Wall, 2004.
"Business cycle phases in U.S. states,"
2003-011, Federal Reserve Bank of St. Louis.
- Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
- Marcelle Chauvet & Jeremy Piger, 2002.
"Identifying business cycle turning points in real time,"
2002-27, Federal Reserve Bank of Atlanta.
- Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 47-61.
- A. Pagan & J. Engel & D. Haugh, 2004. "Some Methods for Assessing the Need for Non-linear Models in Business Cycle Analysis and Forecasting," Econometric Society 2004 Australasian Meetings 284, Econometric Society.
- Maximo Camacho, 2004. "Vector smooth transition regression models for US GDP and the composite index of leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 173-196.
- Pesaran, H.M. & Potter, S.M., 1995.
"A Floor and Ceiling Model of U.S. Output,"
Cambridge Working Papers in Economics
9407, Faculty of Economics, University of Cambridge.
- Theodore M. Crone & Alan Clayton-Matthews, 2004. "Consistent economic indexes for the 50 states," Working Papers 04-9, Federal Reserve Bank of Philadelphia.
- Clive Granger & Tae-Hwy Lee, 1999. "The effect of aggregation on nonlinearity," Econometric Reviews, Taylor and Francis Journals, vol. 18(3), pages 259-269.
- Dick Dijk & Philip Hans Franses, 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 727-744, December.
- Chang, Tsangyao & Chiang, Gengnan, 2012. "Transitional Behavior of Government Debt Ratio on Growth: The Case of OECD Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 24-37, June.
- Cai, Charlie X. & Kyaw, Khine & Zhang, Qi, 2012. "Stock index return forecasting: The information of the constituents," Economics Letters, Elsevier, vol. 116(1), pages 72-74.
- Badi H. Baltagi, 2007.
"Forecasting with Panel Data,"
Center for Policy Research Working Papers
91, Center for Policy Research, Maxwell School, Syracuse University.
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
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