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New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?

1
Monetary Policy Department, Bank of Latvia, K. Valdemara iela 2A, LV-1050 Riga, Latvia
2
KOF Swiss Economic Institute, ETH Zurich, Leonhardstrasse 21, 8092 Zürich, Switzerland
The paper was presented at the 2nd Vienna Workshop on Forecasting and at the 21st IWH-CIREQ-GW Macroeconometric Workshop. The author is grateful to two anonymous reviewers as well as workshop participants for their comments. The views are solely of the author and under no circumstances represent those of Latvijas Banka.
Academic Editors: Martin Wagner and Robert Kunst
Received: 14 December 2020 / Revised: 21 February 2021 / Accepted: 3 March 2021 / Published: 6 March 2021
(This article belongs to the Special Issue Special Issue on Economic Forecasting)
We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R2MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy. View Full-Text
Keywords: US GDP; nowcasts; real-time data; COVID-19 US GDP; nowcasts; real-time data; COVID-19
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MDPI and ACS Style

Siliverstovs, B. New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? . Econometrics 2021, 9, 11. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010011

AMA Style

Siliverstovs B. New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? . Econometrics. 2021; 9(1):11. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010011

Chicago/Turabian Style

Siliverstovs, Boriss. 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? " Econometrics 9, no. 1: 11. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010011

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