Forecasting with Mixed Frequencies

Michelle T. Armesto · Kristie M. Engemann · Michael T. Owyang

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Time Series Analysis and Forecasting

Abstract

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.

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References

2008A Simple Approximate Long-Memory Model of Realized Volatility 2013Handbook of Economic Forecasting 2008Nowcasting: The real-time informational content of macroeconomic data 2005There is a risk-return trade-off after all 1998Bayesian Methods for Dynamic Multivariate Models 2007MIDAS Regressions: Further Results and New Directions 2009Real-Time Measurement of Business Conditions 2005Predicting volatility: getting the most out of return data sampled at different frequencies

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2018Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models 2012Forecasting with Mixed-Frequency Data 2018Estimating MIDAS regressions via OLS with polynomial parameter profiling 2014Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences 2013Changes in predictive ability with mixed frequency data 2016

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