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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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  • 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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    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.
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    2. 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.
    3. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    4. 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.
    5. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    6. 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.
    7. Nina Vujanovic & Bruno Casella & Richard Bolwijn, . "Forecasting global FDI: a panel data approach," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.
    8. Allenby, Greg M., 2017. "Structural forecasts for marketing data," International Journal of Forecasting, Elsevier, vol. 33(2), pages 433-441.
    9. Kausik Chaudhuri & Saumitra N. Bhaduri, 2019. "Inflation Forecast: Just use the Disaggregate or Combine it with the Aggregate," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 331-343, June.
    10. 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.
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    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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