How to deal with missing observations in surveys of professional forecasters

References

• Andrade, P., & Le Bihan, H. (2013). Inattentive professional forecasters. Journal of Monetary Economics, 60(8), 967–21. https://doi.org/10.1016/j.jmoneco.2013.08.005
   Web of Science ®Google Scholar
• Andridge, R. R., & Little, R. J. (2010). A review of hot deck imputation for survey non- response. International Statistical Review, 78(1), 40–64. https://doi.org/10.1111/j.1751-5823.2010.00103.x
   PubMed Web of Science ®Google Scholar
• Batchelor, R. A. (1990). All forecasters are equal. Journal of Business & Economic Statistics, 8(1), 143–144. https://doi.org/10.1080/07350015.1990.10509784
   Web of Science ®Google Scholar
• Bates, J. M., & Granger, C. W. (1969). The combination of forecasts. The Journal of the Operational Research Society, 20(4), 451–468. https://doi.org/10.1057/jors.1969.103
   Google Scholar
• Bürgi, C. (2017). Bias, rationality and asymmetric loss functions. Economics Letters, 154, 113–116. https://doi.org/10.1016/j.econlet.2017.03.002
   Web of Science ®Google Scholar
• Bürgi, C. (2020). Expectation Formation and the Persistence of Shocks. Working Papers 2020-005. The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
   Google Scholar
• Bürgi, C., & Sinclair, T. M. (2017). A nonparametric approach to identifying a subset of forecasters that outperforms the simple average. Empirical Economics, 53(1), 101–115. https://doi.org/10.1007/s00181-016-1152-y
   Web of Science ®Google Scholar
• Capistrán, C., & Timmermann, A. (2009). Forecast combination with entry and exit of experts. Journal of Business & Economic Statistics, 27(4), 428–440. https://doi.org/10.1198/jbes.2009.07211
   Web of Science ®Google Scholar
• Coibion, O., & Gorodnichenko, Y. (2015). Information rigidity and the expectations formation process: A simple framework and new facts. The American Economic Review, 105(8), 2644–2678. https://doi.org/10.1257/aer.20110306
   Web of Science ®Google Scholar
• Conflitti, C., De Mol, C., & Giannone, D. (2015). Optimal combination of survey forecasts. International Journal of Forecasting, 31(4), 1096–1103. https://doi.org/10.1016/j.ijforecast.2015.03.009
   Web of Science ®Google Scholar
• D’agostino, A., McQuinn, K., & Whelan, K. (2012). Are some forecasters really better than others? Journal of Money, Credit, and Banking, 44(4), 715–732. https://doi.org/10.1111/j.1538-4616.2012.00507.x
   Web of Science ®Google Scholar
• Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3), 253–263. https://doi.org/10.1080/07350015.1995.10524599
   Web of Science ®Google Scholar
• Diebold, F. X., & Shin, M. (2019). Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives. International Journal of Forecasting, 35(4), 1679–1691. https://doi.org/10.1016/j.ijforecast.2018.09.006
   Web of Science ®Google Scholar
• Genre, V., Kenny, G., Meyler, A., & Timmermann, A. (2013). Combining expert forecasts: Can anything beat the simple average? International Journal of Forecasting, 29(1), 108–121. https://doi.org/10.1016/j.ijforecast.2012.06.004
   Web of Science ®Google Scholar
• Ghysels, E., & Wright, J. H. (2009). Forecasting professional forecasters. Journal of Business & Economic Statistics, 27(4), 504–516. https://doi.org/10.1198/jbes.2009.06044
   Web of Science ®Google Scholar
• Giacomini, R., & Rossi, B. (2010). Forecast comparisons in unstable environments. Journal of Applied Econometrics, 25(4), 595–620. https://doi.org/10.1002/jae.1177
   Web of Science ®Google Scholar
• Grishchenko, O., Mouabbi, S., & Renne, J. -P. (2019). Measuring inflation anchoring and uncertainty: A US and euro area comparison. Journal of Money, Credit, and Banking, 51(5), 1053–1096. https://doi.org/10.1111/jmcb.12622
   Web of Science ®Google Scholar
• Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4
   Web of Science ®Google Scholar
• Issler, J. V., & Lima, L. R. (2009). A panel data approach to economic forecasting: The bias-corrected average forecast. Journal of Econometrics, 152(2), 153–164. https://doi.org/10.1016/j.jeconom.2009.01.002
   Web of Science ®Google Scholar
• Kenny, G., Kostka, T., & Masera, F. (2015a). Can macroeconomists forecast risk? Event- based evidence from the euro-area SPF. International Journal of Central Banking, 11(4), 1–46.
   Google Scholar
• Kenny, G., Kostka, T., & Masera, F. (2015b). Density characteristics and density forecast performance: A panel analysis. Empirical Economics, 48(3), 1203–1231. https://doi.org/10.1007/s00181-014-0815-9
   Web of Science ®Google Scholar
• Lahiri, K., Peng, H., & Zhao, Y. (2017). Online learning and forecast combination in unbalanced panels. Econometric Reviews, 36(1–3), 257–288. https://doi.org/10.1080/07474938.2015.1114550
   Web of Science ®Google Scholar
• Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons.
   Google Scholar
• Mack, G. A., & Skillings, J. H. (1980). A friedman-type rank test for main effects in a two-factor anova. Journal of the American Statistical Association, 75(372), 947–951. https://doi.org/10.1080/01621459.1980.10477577
   Web of Science ®Google Scholar
• Poncela, P., Rodríguez, J., Sánchez-Mangas, R., & Senra, E. (2011). Forecast combination through dimension reduction techniques. International Journal of Forecasting, 27(2), 224–237. https://doi.org/10.1016/j.ijforecast.2010.01.012
   Web of Science ®Google Scholar
• Scotti, C. (2016). Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises. Journal of Monetary Economics, 82, 1–19. https://doi.org/10.1016/j.jmoneco.2016.06.002
   Web of Science ®Google Scholar
• Sheng, X., & Wallen, J. (2014). Information rigidity in macroeconomic forecasts: An international empirical investigation. [ Unpublished Manuscript]. American University Washington,
   Google Scholar
• Steira, Ø. (2012). How accurate are individual forecasters? an assessment of the survey of professional forecasters. Working Paper. SNF.
   Google Scholar
• Stekler, H. O. (1987). Who forecasts better? Journal of Business & Economic Statistics, 5(1), 155–158. https://doi.org/10.1080/07350015.1987.10509571
   Web of Science ®Google Scholar
• Zhao, Y. (2020). The robustness of forecast combination in unstable environments: A monte carlo study of advanced algorithms. Empirical Economics, 61(1), 1–27. https://doi.org/10.1007/s00181-020-01864-w
   Web of Science ®Google Scholar