Forecasting Output Growth and Inflation with Stock Returns: A MIDAS Approach
Abstract
Improving forecasts of macroeconomic indicators such as output growth and inflation is of focal interest to academics and policy makers. Because stock returns can be observed at very high frequencies, there is the question of whether high frequency information is useful for forecasting output and inflation. Furthermore, stock data is timely, whereas macro data is available only at a lag, so there is a question of whether stock returns can help to indicate the current state of the economy, i.e., "nowcasting". In this thesis, we study the predictive power of daily stock returns on output growth and inflation with Mixed Data Sampling (henceforth, MIDAS) regression models both in forecasting and nowcasting contexts. We filter the daily stock returns with a newly proposed frequency domain filter, and aggregate the daily data with MIDAS weights using estimated parameter values. We find that predictors with MIDAS regressions perform quite well in inflation forecasting. For Singapore inflation, filtered stock returns forecast better than unfiltered stock returns; for US inflation, on the other hand, unfiltered stock returns forecast better than filtered stock returns. Predictors with MIDAS regressions perform fairly well in Singapore output growth forecasting in that contemporary stock returns have higher forecasting accuracy than the benchmark model, but for the US output growth, we don't see any improvements with our MIDAS regressions.