Changes in predictive ability with mixed frequency data

When assessing the predictive power of financial variables for economic activity, researchers usually aggregate higher-frequency data before estimating a forecasting model that assumes the relationship between the financial variable and the dependent variable to be linear. This paper proposes a model called smooth transition mixed data sampling (STMIDAS) regression, which relaxes both of these assumptions. Simulation exercises indicate that the improvements in forecasting accuracy from the use of mixed data sampling are larger in nonlinear than in linear specifications. When forecasting output growth with financial variables in real time, statistically significant improvements over a linear regression are more likely to arise from forecasting with STMIDAS than with MIDAS regressions.