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Forecasting aggregates using panels of nonlinear time series

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  • Fok, Dennis
  • van Dijk, Dick
  • Franses, Philip Hans

Abstract

Macroeconomic 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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Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:21:y:2005:i:4:p:785-794
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    References listed on IDEAS

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    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. 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.
    3. 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.
    4. 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.
    5. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    6. Theodore M. Crone & Alan Clayton-Matthews, 2004. "Consistent economic indexes for the 50 states," Working Papers 04-9, Federal Reserve Bank of Philadelphia.
    7. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    8. Pesaran, M. Hashem & Potter, Simon M., 1997. "A floor and ceiling model of US output," Journal of Economic Dynamics and Control, Elsevier, vol. 21(4-5), pages 661-695, May.
    9. 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.
    10. Clive Granger & Tae-Hwy Lee, 1999. "The effect of aggregation on nonlinearity," Econometric Reviews, Taylor & Francis Journals, vol. 18(3), pages 259-269.
    11. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
    12. Dick van Dijk & Dennis Fok & Philip Hans Franses, 2005. "A multi-level panel STAR model for US manufacturing sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 811-827.
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    Cited by:

    1. Allenby, Greg M., 2017. "Structural forecasts for marketing data," International Journal of Forecasting, Elsevier, vol. 33(2), pages 433-441.
    2. repec:sbe:breart:v:38:y:2018:i:1:a:66264 is not listed on IDEAS
    3. 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.
    4. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    5. 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.
    6. 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.
    7. Oliveira, André Barbosa & Pereira, Pedro L. Valls, 2018. "Uncertainty times for portfolio selection at financial market," Textos para discussão 473, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    8. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, Elsevier.
    9. 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.
    10. Oliveira, André Barbosa & Valls Pereira, Pedro Luiz, 2018. "Asset Allocation with Markovian Regime Switching: Efficient Frontier and Tangent Portfolio with Regime Switching," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(1), May.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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