Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy

摘要

The aim of this study was to forecast the Singapore gross domestic product (GDP) growth rate by employing the mixed-data sampling (MIDAS) approach using mixed and high-frequency financial market data from Singapore, and to examine whether the high-frequency financial variables could better predict the macroeconomic variables. We adopt different time-aggregating methods to handle the high-frequency data in order to match the sampling rate of lower-frequency data in our regression models. Our results showed that MIDAS regression using high-frequency stock return data produced a better forecast of GDP growth rate than the other models, and the best forecasting performance was achieved by using weekly stock returns. The forecasting result was further improved by performing intra-period forecasting.