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NOWCASTING ‘TRUE’ MONTHLY U.S. GDP DURING THE PANDEMIC

Published online by Cambridge University Press:  23 June 2021

Gary Koop Open the ORCID record for Gary Koop [Opens in a new window] ,
Stuart McIntyre ,
James Mitchell  and
Aubrey Poon
Gary Koop*
Affiliation:
Department of Economics, University of Strathclyde, Glasgow, United Kingdom Economic Statistics Centre of Excellence, London, United Kingdom
Stuart McIntyre
Affiliation:
Department of Economics, University of Strathclyde, Glasgow, United Kingdom Economic Statistics Centre of Excellence, London, United Kingdom
James Mitchell
Affiliation:
Economic Statistics Centre of Excellence, London, United Kingdom Federal Reserve Bank of Cleveland, Cleveland, Ohio, United States
Aubrey Poon
Affiliation:
Department of Economics, University of Strathclyde, Glasgow, United Kingdom Economic Statistics Centre of Excellence, London, United Kingdom
*
*Corresponding author. Email: [email protected]
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Abstract

Expenditure-side and income-side gross domestic product (GDP) are measured at the quarterly frequency and contain measurement error. Econometric methods exist for producing reconciled estimates of underlying true GDP from these noisy estimates. Recently, the authors of this paper developed a mixed-frequency reconciliation model which produces monthly estimates of true GDP. In the present paper, we investigate whether this model continues to work well in the face of the extreme observations that occurred during the pandemic year and consider several extensions of it. These include stochastic volatility and error distributions that are fat-tailed or explicitly allow for outliers.

Keywords

pandemicnowcastingmixed-frequency vector autoregressionBayesianE01E32
Type
Research Article
Information
National Institute Economic Review , Volume 256: THE IMPACT OF COVID-19 ON MACROECONOMIC FORECASTING , Spring 2021 , pp. 44 - 70
Copyright
© National Institute Economic Review, 2021

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References

Antolin-Diaz, J., Drechsel, T. and Petrella, I. (2020), ‘Advances in nowcasting economic activity: Secular trends, large shocks and new data’, available online at https://ssrn.com/abstract=3669854, retrieved on 8 Aug. 2020.Google Scholar
Aruoba, S., Diebold, F., Nalewaik, J., Schorfheide, F. and Song, D. (2016), ‘Improving GDP measurement: A measurement-error perspective’, Journal of Econometrics, 191, pp. 384–97.CrossRefGoogle Scholar
Carriero, A., Clark, T. and Marcellino, M. (2020a), ‘Nowcasting tail risks to economic activity with many indicators’, Working paper 202013R3, Federal Reserve Bank of Cleveland, Cleveland, Ohio.Google Scholar
Carriero, A., Clark, T., Marcellino, M. and Mertens, E. (2020b), ‘Measuring uncertainty and its effects in the COVID-19 era’, Working paper 20-32, Federal Reserve Bank of Cleveland, Cleveland, Ohio, https://doi.org/10.26509/frbc-wp-202032.CrossRefGoogle Scholar
Chan, J. and Hsiao, C. (2014), ‘Estimation of stochastic volatility models with heavy tails and serial dependence’, in Jeliazkov, I. and Yang, X.-S. (eds), Bayesian Inference in the Social Sciences, Hoboken, NJ: John Wiley & Sons, pp. 159–80.Google Scholar
Huber, F., Koop, G., Onorante, L., Pfarrhofer, M. and Schreiner, J. (2020), ‘Nowcasting in a pandemic using non-parametric mixed frequency VARs’, Journal of Econometrics, forthcoming. https://doi.org/10.1016/j.jeconom.2020.11.006.CrossRefGoogle Scholar
Koop, G., McIntyre, S., Mitchell, J. and Poon, A. (2020), ‘Reconciled estimates of monthly GDP in the US’, Economic Statistics Centre of Excellence Discussion Paper DP-2020-16, Economic Statistics Centre of Excellence, London.Google Scholar
Lenza, M. and Primiceri, G. (2020), ‘How to estimate a VAR after March 2020’, Working paper 2461, European Central Bank, Frankfurt.CrossRefGoogle Scholar
Lerch, S., Thorarinsdottir, T., Ravazzolo, F. and Gneiting, T. (2017), ‘Forecaster’s dilemma: Extreme events and forecast evaluation’, Statistical Science, 32, pp. 106–27.10.1214/16-STS588CrossRefGoogle Scholar
Lewis, D., Mertens, K. and Stock, J. (2020), ‘Monitoring real activity in real time: The weekly economic index’, Liberty Street Economics 20200330b, Federal Reserve Bank of New York, available online at https://libertystreeteconomics.newyorkfed.org/2020/03/monitoring-real-activity-in-real-time-the-weekly-economic-index.html, retrieved on 30 Mar. 2020.Google Scholar
Nalewaik, J. (2010), ‘The income- and expenditure-side estimates of U.S. output growth’, Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(1), pp. 71–127 (with discussion).CrossRefGoogle Scholar
Nalewaik, J. (2012), ‘Estimating probabilities of recession in real time using GDP and GDI’, Journal of Money, Credit and Banking, 44, pp. 235–53.10.1111/j.1538-4616.2011.00475.xCrossRefGoogle Scholar
Rossi, B. (2020), ‘Forecasting in the presence of instabilities: How do we know whether economic models work and how to improve them’, CEPR Discussion Paper 14064, February 2020.Google Scholar
Schorfheide, F. and Song, D. (2015), ‘Real-time forecasting with a mixed-frequency VAR’, Journal of Business and Economic Statistics, 33, 3, pp. 366–80.CrossRefGoogle Scholar
Schorfheide, F. and Song, D. (2020), ‘Real-time forecasting with a (standard) mixed-frequency VAR during a pandemic’, Working paper 20-26, Federal Reserve Bank of Philadelphia, Philadelphia, Pennsylvania.CrossRefGoogle Scholar
Stock, J. and Watson, M. (2016), ‘Core inflation and trend inflation’, Review of Economics and Statistics, 98, pp. 770–84.10.1162/REST_a_00608CrossRefGoogle Scholar