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Real time forecasts of inflation: the role of financial variables

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Author Info

  • Libero Monteforte

    ()
    (Bank of Italy)

  • Gianluca Moretti

    ()
    (Bank of Italy)

Abstract

We present a mixed-frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real-time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed-frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives.

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Bibliographic Info

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 767.

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Date of creation: Jul 2010
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Handle: RePEc:bdi:wptemi:td_767_10

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Keywords: forecasting inflation; real time forecasts; dynamic factor models; MIDAS regression; economic derivatives;

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Cited by:
  1. Mehrotra, Aaron & Funke, Michael & Yu, Hao, 2011. "Tracking Chinese CPI inflation in real time," BOFIT Discussion Papers 35/2011, Bank of Finland, Institute for Economies in Transition.
  2. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
  3. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
  4. Eric Ghysels & Andros Kourtellos & Elena Andreou, 2012. "Should macroeconomic forecasters use daily financial data and how?," 2012 Meeting Papers 1196, Society for Economic Dynamics.
  5. Cecilia Frale & Libero Monteforte, . "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
  6. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.
  7. Modugno, Michele, 2011. "Nowcasting inflation using high frequency data," Working Paper Series 1324, European Central Bank.

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