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Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights

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  • Lütkepohl Helmut

    () (Department of Economics, European University Institute,Via della Piazzola 43, 50133 Firenze, Italy)

Abstract

Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a framework for nonlinear contemporaneous aggregation with possibly stochastic or time-varying weights is developed and different predictors for an aggregate are compared theoretically as well as with simulations. Two examples based on European unemployment and inflation series are used to illustrate the virtue of the theoretical setup and the forecasting results.

Suggested Citation

  • Lütkepohl Helmut, 2011. "Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 107-133, February.
  • Handle: RePEc:jns:jbstat:v:231:y:2011:i:1:p:107-133
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    References listed on IDEAS

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    1. Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-26.
    2. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    3. Heather Anderson & Mardi Dungey & Denise Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," Money Macro and Finance (MMF) Research Group Conference 2006 99, Money Macro and Finance Research Group.
    4. Beyer, Andreas & Doornik, Jurgen A & Hendry, David F, 2001. "Constructing Historical Euro-Zone Data," Economic Journal, Royal Economic Society, vol. 111(469), pages 102-121, February.
    5. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    6. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
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    Citations

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    Cited by:

    1. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
    2. Edward S. Knotek Ii & Saeed Zaman, 2017. "Nowcasting U.S. Headline and Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 931-968, August.
    3. Brüggemann, Ralf & Lütkepohl, Helmut, 2013. "Forecasting contemporaneous aggregates with stochastic aggregation weights," International Journal of Forecasting, Elsevier, vol. 29(1), pages 60-68.

    More about this item

    Keywords

    Forecasting; stochastic aggregation; autoregression; moving average; vector autoregressive process;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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