Forecasting with mixed frequencies
A dilemma faced by forecasters is that data are not all sampled at the same frequency. Most macroeconomic data are sampled monthly (e.g., employment) or quarterly (e.g., GDP). Most financial variables (e.g., interest rates and asset prices), on the other hand, are sampled daily or even more frequently. The challenge is how to best use available data. To that end, the authors survey some common methods for dealing with mixed-frequency data. They show that, in some cases, simply averaging the higher-frequency data produces no discernible disadvantage. In other cases, however, explicitly modeling the flow of data (e.g., using mixed data sampling as in Ghysels, Santa-Clara, and Valkanov, 2004) may be more beneficial to the forecaster, especially if the forecaster is interested in constructing intra-period forecasts.
Volume (Year): (2010)
Issue (Month): Nov ()
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- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
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99-4, Federal Reserve Bank of Philadelphia.
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