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Real‐Time Forecasts of Inflation: The Role of Financial Variables


  • Libero Monteforte
  • Gianluca Moretti


(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Libero Monteforte & Gianluca Moretti, 2013. "Real‐Time Forecasts of Inflation: The Role of Financial Variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 51-61, January.
  • Handle: RePEc:wly:jforec:v:32:y:2013:i:1:p:51-61

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    References listed on IDEAS

    1. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2005. "Model confidence sets for forecasting models," FRB Atlanta Working Paper 2005-07, Federal Reserve Bank of Atlanta.
    2. Christian Dreger & Christian Schumacher, 2005. "Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 71-87.
    3. Acemoglu, Daron & Scott, Andrew, 1994. "Consumer Confidence and Rational Expectations: Are Agents' Beliefs Consistent with the Theory?," Economic Journal, Royal Economic Society, vol. 104(422), pages 1-19, January.
    4. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    5. Jeff Dominitz & Charles F. Manski, 2004. "How Should We Measure Consumer Confidence?," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 51-66, Spring.
    6. Jason Bram & Sydney Ludvigson, 1998. "Does consumer confidence forecast household expenditure? a sentiment index horse race," Economic Policy Review, Federal Reserve Bank of New York, issue Jun, pages 59-78.
    7. Christian Dreger & Hans-Eggert Reimers, 2009. "The Role of Asset Markets for Private Consumption: Evidence from Paneleconometric Models," Discussion Papers of DIW Berlin 872, DIW Berlin, German Institute for Economic Research.
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    Cited by:

    1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    2. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, vol. 48(4), pages 1619-1641, June.
    3. 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.
    4. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    5. Andrade, Philippe & Fourel, Valère & Ghysels, Eric & Idier, Julien, 2014. "The financial content of inflation risks in the euro area," International Journal of Forecasting, Elsevier, vol. 30(3), pages 648-659.
    6. repec:eee:eneeco:v:67:y:2017:i:c:p:83-90 is not listed on IDEAS
    7. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    8. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    9. repec:eee:intfor:v:33:y:2017:i:3:p:679-693 is not listed on IDEAS
    10. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 584-614.
    11. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    12. Modugno, Michele, 2013. "Now-casting inflation using high frequency data," International Journal of Forecasting, Elsevier, vol. 29(4), pages 664-675.
    13. repec:bla:jorssa:v:180:y:2017:i:2:p:353-407 is not listed on IDEAS
    14. repec:eee:ecmode:v:68:y:2018:i:c:p:586-598 is not listed on IDEAS
    15. LUPU, Radu & CALIN, Adrian Cantemir, 2014. "A Mixed Frequency Analysis Of Connections Between Macroeconomic Variables And Stock Markets In Central And Eastern Europe," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(2), pages 69-79.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G19 - Financial Economics - - General Financial Markets - - - Other


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