Loading FRED-QD

17 Data Sets Used in This Book

This chapter is a reference appendix for the file-backed datasets shipped with the book repository. Earlier chapters link here instead of re-describing the source each time. Synthetic datasets generated inside code chunks are not listed here; they are documented where they appear.

17.1 FRED-QD

FRED-QD is the quarterly U.S. macroeconomic database developed by McCracken and Ng (2020). A local copy of the current release is stored at data/fred_qd_current.csv.

  • Unit of observation: calendar quarter
  • Coverage: U.S. macro-financial series, 1959Q1 onward
  • Frequency: quarterly
  • File layout: the CSV contains two metadata rows before the actual data. The first row stores factor-group identifiers and the second row stores the authors’ suggested transformation codes. The sasdate column holds the end-of-quarter date.

The chapters in the book use the subset of series listed below. Full variable documentation is available in the FRED-QD release notes.

Mnemonic Description
GDPC1 Real gross domestic product, chained dollars
CPIAUCSL Consumer price index, all urban consumers
UNRATE Civilian unemployment rate
FEDFUNDS Effective federal funds rate
GS10 10-year Treasury constant-maturity yield
TB3MS 3-month Treasury bill secondary-market rate
VIXCLSx CBOE volatility index (VIX), quarterly closing value

Figure 17.1 shows the series used in the book on a common standardized scale so that their timing can be compared across the sample. Magnitudes are not directly comparable on this scale.

Show the code 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

raw = pd.read_csv("data/fred_qd_current.csv")
fred_qd = raw.iloc[2:].copy()
fred_qd["sasdate"] = pd.to_datetime(fred_qd["sasdate"])

for column in fred_qd.columns:
    if column != "sasdate":
        fred_qd[column] = pd.to_numeric(fred_qd[column], errors="coerce")

gdp_growth = 400 * np.log(fred_qd["GDPC1"]).diff()
inflation = 400 * np.log(fred_qd["CPIAUCSL"]).diff()
term_spread = fred_qd["GS10"] - fred_qd["TB3MS"]
log_vix = np.log(fred_qd["VIXCLSx"])

plot_df = pd.DataFrame(
    {
        "date": fred_qd["sasdate"],
        "GDP growth": gdp_growth,
        "Inflation": inflation,
        "Unemployment": fred_qd["UNRATE"],
        "Fed funds": fred_qd["FEDFUNDS"],
        "Term spread": term_spread,
        "Log VIX": log_vix,
    }
).dropna()

standardized = plot_df.copy()
for column in standardized.columns[1:]:
    standardized[column] = (
        standardized[column] - standardized[column].mean()
    ) / standardized[column].std()

fig, ax = plt.subplots(figsize=(11.4, 4.8))
for column in standardized.columns[1:]:
    ax.plot(standardized["date"], standardized[column], linewidth=1.5, label=column)

ax.set_xlabel("Quarter")
ax.set_ylabel("Standardized value")
ax.grid(True, alpha=0.2)
ax.legend(frameon=False, ncol=3, loc="upper left")
plt.tight_layout()
plt.show()
Figure 17.1: Selected FRED-QD series used in the book, standardized to mean zero and variance one. Real GDP growth is the annualized log difference of GDPC1; inflation is the annualized log difference of CPIAUCSL; the remaining series are shown as levels, with the term spread constructed as GS10 - TB3MS and log VIX as log(VIXCLSx).

The snippet below is the canonical way to load FRED-QD in this book. It skips the two metadata rows, parses sasdate as a date, and coerces the remaining columns to numeric.

Show the code 
import pandas as pd

raw = pd.read_csv("data/fred_qd_current.csv")
fred_qd = raw.iloc[2:].copy()
fred_qd["sasdate"] = pd.to_datetime(fred_qd["sasdate"])

for column in fred_qd.columns:
    if column != "sasdate":
        fred_qd[column] = pd.to_numeric(fred_qd[column], errors="coerce")

fred_qd = fred_qd.sort_values("sasdate").reset_index(drop=True)

The authors’ transformation codes in the second metadata row are not applied here. Individual chapters construct the transformations they need directly from the raw levels so that the forecast origin and information set are explicit.

17.2 References

McCracken, Michael W., and Serena Ng. 2020. “FRED-QD: A Quarterly Database for Macroeconomic Research.” Working Paper 26872. National Bureau of Economic Research.