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On the normality of probability distributions of inflation and GNP forecasts

Citations

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Cited by:

  1. Wallis, Kenneth F., 2003. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," International Journal of Forecasting, Elsevier, vol. 19(2), pages 165-175.
  2. Joseph E. Gagnon, 2008. "Inflation regimes and inflation expectations," Review, Federal Reserve Bank of St. Louis, vol. 90(May), pages 229-243.
  3. Richhild Moessner & Feng Zhu & Colin Ellis, 2011. "Measuring disagreement in UK consumer and central bank inflation forecasts," BIS Working Papers 339, Bank for International Settlements.
  4. Tara M. Sinclair & H. O. Stekler & Warren Carnow, 2012. "A new approach for evaluating economic forecasts," Economics Bulletin, AccessEcon, vol. 32(3), pages 2332-2342.
  5. Song, ChiUng & Boulier, Bryan L. & Stekler, Herman O., 2009. "Measuring consensus in binary forecasts: NFL game predictions," International Journal of Forecasting, Elsevier, vol. 25(1), pages 182-191.
  6. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
  7. Giordani, Paolo & Soderlind, Paul, 2003. "Inflation forecast uncertainty," European Economic Review, Elsevier, vol. 47(6), pages 1037-1059, December.
  8. Christian Grimme & Steffen Henzel & Elisabeth Wieland, 2014. "Inflation uncertainty revisited: a proposal for robust measurement," Empirical Economics, Springer, vol. 47(4), pages 1497-1523, December.
  9. Robert W. Rich & Joseph Tracy, 2006. "The relationship between expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts," Staff Reports 253, Federal Reserve Bank of New York.
  10. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
  11. Christian Bauer & Sebastian Weber, 2016. "The Efficiency of Monetary Policy when Guiding Inflation Expectations," Research Papers in Economics 2016-14, University of Trier, Department of Economics.
  12. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
  13. Kajal Lahiri & Fushang Liu, 2006. "ARCH Models for Multi-period Forecast Uncertainty: A Reality Check Using a Panel of Density Forecasts," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 321-363, Emerald Group Publishing Limited.
  14. Harvey, David I. & Newbold, Paul, 2003. "The non-normality of some macroeconomic forecast errors," International Journal of Forecasting, Elsevier, vol. 19(4), pages 635-653.
  15. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
  16. Marián Vávra, 2020. "Assessing distributional properties of forecast errors for fan-chart modelling," Empirical Economics, Springer, vol. 59(6), pages 2841-2858, December.
  17. Markus Eyting & Patrick Schmidt, 2019. "Belief Elicitation with Multiple Point Predictions," Working Papers 1818, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 16 Nov 2020.
  18. repec:lan:wpaper:470 is not listed on IDEAS
  19. Thomas Maag, 2009. "On the accuracy of the probability method for quantifying beliefs about inflation," KOF Working papers 09-230, KOF Swiss Economic Institute, ETH Zurich.
  20. repec:lan:wpaper:425 is not listed on IDEAS
  21. Eyting, Markus & Schmidt, Patrick, 2021. "Belief elicitation with multiple point predictions," European Economic Review, Elsevier, vol. 135(C).
  22. Gao, Chuanming & Lahiri, Kajal, 2000. "Further consequences of viewing LIML as an iterated Aitken estimator," Journal of Econometrics, Elsevier, vol. 98(2), pages 187-202, October.
  23. Nolte, Ingmar & Pohlmeier, Winfried, 2007. "Using forecasts of forecasters to forecast," International Journal of Forecasting, Elsevier, vol. 23(1), pages 15-28.
  24. Marinovic, Iván & Ottaviani, Marco & Sorensen, Peter, 2013. "Forecasters’ Objectives and Strategies," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 690-720, Elsevier.
  25. Fushang Liu & Kajal Lahiri, 2006. "Modelling multi-period inflation uncertainty using a panel of density forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1199-1219.
  26. repec:lan:wpaper:539557 is not listed on IDEAS
  27. Lauren K. Fine & Stephen K. McNees, 1994. "Diversity, uncertainty, and accuracy of inflation forecasts," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 33-44.
  28. Clements, Michael P., 2008. "Consensus and uncertainty: Using forecast probabilities of output declines," International Journal of Forecasting, Elsevier, vol. 24(1), pages 76-86.
  29. Kajal Lahiri & Fushang Liu, 2009. "On the Use of Density Forecasts to Identify Asymmetry in Forecasters' Loss Functions," Discussion Papers 09-03, University at Albany, SUNY, Department of Economics.
  30. repec:lan:wpaper:413 is not listed on IDEAS
  31. Chih-Hung Peng & Nicholas H. Lurie & Sandra A. Slaughter, 2019. "Using Technology to Persuade: Visual Representation Technologies and Consensus Seeking in Virtual Teams," Information Systems Research, INFORMS, vol. 30(3), pages 948-962, September.
  32. Pilar Poncela & Eva Senra, 2017. "Measuring uncertainty and assessing its predictive power in the euro area," Empirical Economics, Springer, vol. 53(1), pages 165-182, August.
  33. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
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