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In this article, we present and describe FRED-QD, a large, quarterly frequency macroeconomic database that is currently available and regularly updated at https://research.stlouisfed.org/econ/ mccracken/fred-databases/. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD (McCracken and Ng, 2016), which is at a monthly frequency, our goal is simply to provide a publicly available source of macroeconomic "big data" that is updated in real time using the FRED® data service. We show that factors extracted from the FRED-QD dataset exhibit similar behavior to those extracted from the original Stock and Watson dataset. 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 dataset 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. (JEL C30, C33, C82)
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1INTRODUCTION
In our previous work, McCracken and Ng (2016), we describe and investigate a monthly frequency database of macroeconomic variables called FRED-MD. At some level, FRED-MD is not particularly innovative. It is, after all, just a collection of N = 128 standard U.S. macroeconomic time series that date back to January 1959 and have primarily been taken from FRED®, the data service maintained by the Federal Reserve Bank of St. Louis, and organized into a .csv file. That description, however, misses the point. Our main goal was to facilitate easy access to a standardized example of a data-rich environment that can be used for academic research. By automating this dataset, and maintaining a website that provides monthly frequency vintages, those who are interested in conducting research on big data can focus on the statistical problems associated with big data rather than having to put the dataset together themselves. This dataset frees the practitioner from dealing with issues related to, for example, updating the dataset when new data is released,...