Using payments data to nowcast macroeconomic variables during the onset of COVID-19

Arbeitspapier

The COVID-19 pandemic and the resulting public health mitigation have caused large-scale economic disruptions globally. During this time, there is an increased need to predict the macroeconomy's short-term dynamics to ensure the effective implementation of fiscal and monetary policy. However, economic prediction during a crisis is challenging because of the unprecedented economic impact, which increases the unreliability of traditionally used linear models that use lagged data. We help address these challenges by using timely retail payments system data in linear and nonlinear machine learning models. We find that compared to a benchmark, our model has a roughly 15 to 45% reduction in Root Mean Square Error when used for macroeconomic nowcasting during the global financial crisis. For nowcasting during the COVID-19 shock, our model predictions are much closer to the official estimates.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Bank of Canada Staff Working Paper ; No. 2021-2

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Forecasting Models; Simulation Methods ; Large Data Sets: Modeling and Analysis ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems ; Monetary Policy
• Thema: Econometric and statistical methods ; Payment clearing and settlement systems

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Chapman, James ; Desai, Ajit
• Ereignis: Veröffentlichung
• (wer): Bank of Canada
• (wo): Ottawa
• (wann): 2021

Weitere Informationen

• DOI: doi:10.34989/swp-2021-2
• Handle: http://hdl.handle.net/10419/241225
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ