Working Paper: NBER ID: w26872
Authors: Michael McCracken; Serena Ng
Abstract: In this paper we present and describe a large quarterly frequency, macroeconomic database. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD, our goal is simply to provide a publicly available source of macroeconomic “big data” that is updated in real time using the FRED database. We show that factors extracted from this data set exhibit similar behavior to those extracted from the original Stock and Watson data set. The dominant factors are shown to be insensitive to outliers, but outliers do affect the relative influence of the series as indicated by leverage scores. We then investigate the role unit root tests play in the choice of transformation codes with an emphasis on identifying instances in which the unit root-based codes differ from those already used in the literature. Finally, we show that factors extracted from our data set are useful for forecasting a range of macroeconomic series and that the choice of transformation codes can contribute substantially to the accuracy of these forecasts.
Keywords: macroeconomic database; big data; forecasting
JEL Codes: C30; C34; E01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| choice of transformation codes (C69) | accuracy of forecasts for various macroeconomic series (E17) |
| unit root-based transformation codes (C22) | predictive content for real and financial series (C58) |
| unit root-based transformation codes (C22) | accuracy of nominal price series forecasts (E37) |
| treating price inflation as integrated of order zero (i0) (E31) | better forecasts than treating it as integrated of order one (i1) (C53) |
| factors extracted from the fredqd database (C38) | factors extracted from the original Stock and Watson dataset (C38) |