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

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.


Issue Date:
Jul 07 2006
Publication Type:
Working or Discussion Paper
Record Identifier:
http://ageconsearch.umn.edu/record/269743
Language:
English
Total Pages:
36
JEL Codes:
C51; C53




 Record created 2018-03-21, last modified 2018-03-21

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