Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth
AbstractMany macroeconomic series such as US real output growth are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS approach is compared to other ways of making use of monthly data to predict quarterly output growth. The MIDAS specification used in the comparison employs a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way of exploiting monthly data compared to alternative methods. We also exploit the best method to use the monthly vintages of the indicators for real-time forecasting.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 616.
Date of creation: Oct 2007
Date of revision:
Mixed data frequency; Coincident indicators; Real-time forecasting; US output growth;
Find related papers by JEL classification:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-10-27 (All new papers)
- NEP-CBA-2007-10-27 (Central Banking)
- NEP-ECM-2007-10-27 (Econometrics)
- NEP-FOR-2007-10-27 (Forecasting)
- NEP-MAC-2007-10-27 (Macroeconomics)
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