EconPapers    
Economics at your fingertips  
 

Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation

Michael Clements and Ana Galvão ()

No 269743, Economic Research Papers from University of Warwick - Department of Economics

Abstract: Although many macroeconomic series such as US real output growth are sampled quarterly, many potentially useful predictors are observed at a higher frequency. We look at whether a recently developed mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth and inflation. We carry out a number of related real-time forecast comparisons using various indicators as explanatory variables. We find that MIDAS model forecasts of output growth are more accurate at horizons less than one quarter using coincident indicators; that MIDAS models are an effective way of combining information from multiple indicators; and that the forecast accuracy of the unemployment-rate Phillips curve for inflation is enhanced using the MIDAS approach.

Keywords: Agricultural and Food Policy; Research Methods/ Statistical Methods (search for similar items in EconPapers)
Date: 2006-07-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed

Downloads: (external link)
http://ageconsearch.umn.edu/record/269743/files/twerp_773.pdf (application/pdf)
http://ageconsearch.umn.edu/record/269743/files/twerp_773.pdf?subformat=pdfa (application/pdf)

Related works:
Working Paper: Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation (2006) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:uwarer:269743

Access Statistics for this paper

More papers in Economic Research Papers from University of Warwick - Department of Economics
Bibliographic data for series maintained by AgEcon Search ().

 
Page updated 2019-09-12
Handle: RePEc:ags:uwarer:269743