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Realistic evaluation of real-time forecasts in the Survey of Professional Forecasters

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  • Tom Stark

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  • Tom Stark, 2010. "Realistic evaluation of real-time forecasts in the Survey of Professional Forecasters," Research Rap Special Report, Federal Reserve Bank of Philadelphia, issue May.
    • Handle: RePEc:fip:fedprr:y:2010:i:may:x:1
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    References listed on IDEAS

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    1. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    2. Leonard I. Nakamura & Tom Stark, 2007. "Mismeasured personal saving and the permanent income hypothesis," Working Papers 07-8, Federal Reserve Bank of Philadelphia.
    3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    6. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    7. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September.
    8. Swanson, Norman R., 1998. "Money and output viewed through a rolling window," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 455-474, May.
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    Cited by:

    1. Breach, Tomas & D’Amico, Stefania & Orphanides, Athanasios, 2020. "The term structure and inflation uncertainty," Journal of Financial Economics, Elsevier, vol. 138(2), pages 388-414.
    2. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    3. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    4. Knotek, Edward S. & Zaman, Saeed, 2019. "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1708-1724.
    5. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    6. Dr. Christian Hepenstrick & Jason Blunier, 2022. "What were they thinking? Estimating the quarterly forecasts underlying annual growth projections," Working Papers 2022-05, Swiss National Bank.
    7. Alexandre Kohlhas, 2018. "Asymmetric Attention," 2018 Meeting Papers 1040, Society for Economic Dynamics.
    8. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    9. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    10. Cifuentes, Sebastián & Cortazar, Gonzalo & Ortega, Hector & Schwartz, Eduardo S., 2020. "Expected prices, futures prices and time-varying risk premiums: The case of copper," Resources Policy, Elsevier, vol. 69(C).
    11. Matei Demetrescu & Christoph Hanck & Robinson Kruse, 2016. "Fixed-b Inference in the Presence of Time-Varying Volatility," CREATES Research Papers 2016-01, Department of Economics and Business Economics, Aarhus University.
    12. Konchitchki, Yaniv & Patatoukas, Panos N., 2014. "Accounting earnings and gross domestic product," Journal of Accounting and Economics, Elsevier, vol. 57(1), pages 76-88.
    13. Galimberti, Jaqueson K. & Moura, Marcelo L., 2016. "Improving the reliability of real-time output gap estimates using survey forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 358-373.
    14. João Valle e Azevedo & João Tovar Jalles, 2011. "Rational vs. Professional Forecasts," Working Papers w201114, Banco de Portugal, Economics and Research Department.
    15. Amy Y. Guisinger & Michael W. McCracken & Michael T. Owyang, 2022. "Reconsidering the Fed's Inflation Forecasting Advantage," Working Papers 2022-001, Federal Reserve Bank of St. Louis, revised 23 Oct 2023.
    16. Cortazar, Gonzalo & Ortega, Hector & Valencia, Consuelo, 2021. "How good are analyst forecasts of oil prices?," Energy Economics, Elsevier, vol. 102(C).
    17. Kieran Mc Morrow & Werner Roeger & Valerie Vandermeulen, 2017. "Evaluating Medium Term Forecasting Methods and their Implications for EU Output Gap Calculations," European Economy - Discussion Papers 070, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    18. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.

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