Importance of daily data in long horizon inflation forecasting - a MIDAS approach

Abstract

We examine the accuracy of forecast models for the monthly Euro area inflation, focusing on the MIDAS approach. We compare two mixed frequency models with four low frequency models, using fourteen mixed frequency variables sampled at daily or monthly frequency. Our data set covers the period of February 1999 until August 2017, and we use a 10-year rolling window to construct the forecasts. We use MIDAS models with one- respectively five-month lags, as these specifications provide the lowest average MSEs. Our findings show that the MIDAS model with five month lags perform better in-sample compared to the MIDAS model with one-month lag. The opposite applies for our out-of-sample forecasts. Furthermore, our findings suggest, in line with previous findings, that the MIDAS models perform well for short forecast horizons. On the contrary to previous research, we find that the MIDAS models provide worse forecasts than an AR(1) for longer forecast horizons.