Should Macroeconomic Forecasters Use Daily Financial Data and How?

There are hundreds of fi nancial times series available on a daily basis that contain information about the future states of the economy. Can we efficiently use all this daily nancial information for improving and/or updating macroeconomic forecasts? The literature on macroeconomic forecasting has not address this question. Instead, it has focused on how the use of a small set of financial series - usually aggregated at a monthly or quarterly frequency. In the paper we introduce two methods for predicting inflation and real activity: (1) methods which rely on combinations of regressions that involve regressors with different sampling frequency, such as quarterly macro series and daily financial series and (2) a small set of daily financial factors extracted from the large cross-section of daily series along with quarterly frequency factors dominated by macroeconomic variables. Both methods have the following important features: (1) they allow us to clearly show the incremental value of daily fi nancial series in terms of forecast improvements, (2) they provide a succinct summary of a huge amount of information in daily fi nancial data, (3) they rely on extremely simple parsimonious regression methods that are easy to implement in practice. The analysis of daily fi nancial factors are of independent interest and other potential applications too as they span equity premium, foreign exchange, fi xed income and commodity price information