Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth and Inflation

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: Data frequency, multiple predictors, combination, real-time forecasting.JEL classification: C51, C53. * We are grateful to the Editor, an Associate Editor, and two anonymous referees for helpful comments and guidance. We also acknowledge helpful comments from participants at the International Symposium of Forecasting Macroeconomic Forecasting with Mixed Frequency Data:Forecasting US output growth and inflation AbstractAlthough 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.

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“…The output growth measure considered in this study is the quarterly growth rate of chain-linked Gross Domestic Product as released by the national instituts of statistics of the four countries, namely: ) series using the weight polynomial, given in equation (2).…”