Real Time Forecasts of Inflation: The Role of Financial Variables

Abstract

and Christian Schumacher for useful discussions. We are also grateful to Fabio Busetti for helpful comments at various stages of the work. Usual disclaimers apply.

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

“…Our results show that the intra-period MIDAS forecasting model performs best with the lowest RMSE when we include weekly intra-period data up to the end of the 5th week. The findings are in some sense consistent with Monteforte and Moretti (2013) and Dogan and Mililic (2018) that the inclusion of daily variables helps improve forecasting performance and reduce forecast errors with respect to models that consider only monthly variables, and the mixed-frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Figure 7 presents the actual and forecasted GDP growth rates using the intra-period MIDAS forecasting model with monthly and up to the 5th week returns.…”

“…In this paper, we have comparatively investigated the forecasting performance of the three models-that is, the MIDAS regression model, the direct regression model on high-frequency data, and the time-averaging regression model-by using data from the Singapore economy. Consistent with the findings of Monteforte and Moretti (2013) and Dogan and Midilic, (2018), our results show that MIDAS regression using high-frequency stock returns data produces a better forecast of GDP growth rate than the other models, and the best forecasting performance is achieved using weekly stock returns. It is also found that the intra-period MIDAS model outperforms other forecasting models, as it can capture well all the important ups and downs of the economic performance in Singapore, especially during the economic crises in 2001-2002 and 2008-2009, with the lowest RMSE value.…”

“…1 Recently, Monteforte and Moretti (2013) proposed a mixed-frequency model for daily forecasts of euro area inflation by combining a monthly index of core inflation with daily data from financial markets, and found that the mixed-frequency model displayed superior predictive performance with respect to forecasts solely based on economic derivatives. Kuosmanen and Vataja (2014) tested the ability of financial variables to predict future economic growth in Finland, and reported that, during steady economic growth, the preferred choice for a financial indicator was the short-term interest rate combined with past values of output growth whereas, during economic turbulence, the traditional term spread and stock returns were more important in forecasting GDP growth.…”

Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach

“…Owing to increased accessibility of high-frequency data it is becoming increasingly attractive to employ variables observed at different sampling frequencies for economic forecasting (for a comprehensive review of the literature, see Ghysels et al, 2006;Monteforte and Moretti, 2013;Andreou et al, 2011;Modugno, 2013;Banbura et al, 2013). In such exercises various high-frequency observations (30 days, say) of the predictor are associated with a single low-frequency observation (1 month).…”

“…See alsoEdelstein (2007),Browne and Cronin (2010),Gospodinov and Ng (2013) andMonteforte and Moretti (2013) for more recent empirical studies. Copyright © 2015 John Wiley & Sons, Ltd J.…”