Arbeitspapier

Forecasting growth of U.S. aggregate and household-sector M2 after 2000 using economic uncertainty measures

This paper evaluates the predictive out-of-sample forecasting properties of six different economic uncertainty variables for both growth in aggregate M2 and growth in household-sector M2 in the U.S. using data between 1971m1 and 2014m12. The core contention is that economic uncertainty improves both forecast accuracy as well as direction-of-change forecasts of real money stock growth. We estimate linear ARDL models using the iterated rolling-window forecasting scheme combined with two different indicator selection procedures. Forecast accuracy is evaluated by RMSE and the Diebold-Mariano test. Direction-of-change forecasts are assessed by means of the Kuipers Score and the Pesaran-Timmermann test. The results indicate an increased relevance of certain economic uncertainty measures for forecasting growth in both real aggregate as well as real household-sector M2 since 2000.

Sprache
Englisch

Erschienen in
Series: DEP (Socioeconomics) Discussion Papers - Macroeconomics and Finance Series ; No. 2/2017

Klassifikation
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Demand for Money
Money and Interest Rates: Forecasting and Simulation: Models and Applications
Thema
money demand
uncertainty
risk
multi-step forecasts
forecast comparison

Ereignis
Geistige Schöpfung
(wer)
Tarassow, Artur
Ereignis
Veröffentlichung
(wer)
Hamburg University, Department Socioeconomics
(wo)
Hamburg
(wann)
2017

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Tarassow, Artur
  • Hamburg University, Department Socioeconomics

Entstanden

  • 2017

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