An overview of the FRED-MD database

FRED-MD is an open-source dataset of monthly U.S. macroeconomic indicators maintained by the Federal Reserve Bank of St. Louis. The FRED-MD dataset was introduced to provide a common benchmark for comparing model performance and to facilitate the reproducibility of research results [1]. The FRED-MD dataset includes eight different categories of macroeconomic indicators (see the Appendix for the full list):

1. Output and Income

2. Labor Market

3. Consumption and Orders

4. Orders and Inventories

5. Money and Credit

6. Interest Rates and Exchange Rates

7. Prices

8. Stock Market

The time series included in the FRED-MD dataset are sourced from the Federal Reserve Economic Data (FRED) database, which is St. Louis Fed’s main, publicly available, economic database. The FRED-MD dataset applies different adjustments to the raw data sourced from FRED, such as seasonal adjustments, inflation adjustments and backfilling of missing values.

The FRED-MD dataset also takes into account data changes and revisions. For instance, in the main FRED database the same indicator can be released with different names and, potentially, be reported in different units, over different time periods. In the FRED-MD dataset each indicator is instead always represented by a single time series with a unique name and is always reported in the same units.

The FRED-MD dataset was released for the first time in 01-2015. At the time of its first release, the FRED-MD dataset contained 134 time series. As of 12-2023, the FRED-MD dataset contains 127 time series. 118 time series are included in all monthly releases from 01-2015 to 12-2023. The first date included in the FRED-MD dataset is 01-1959, even though a few time series start several years later.

The FRED-MD dataset is updated on a monthly basis. Each monthly release is referred to as a vintage. A different CSV file is released for each month. The CSV files can be downloaded from the URL below, where {year} and {month} are the year and month of the release.

"https://files.stlouisfed.org/files/htdocs/fred-md/monthly/{year}-{month}.csv"

Each CSV file contains the data from 01-1959 up to the previous month end. For instance, the 01-2015.csv file contains the data from 01-1959 to 12-2014, the 02-2015.csv file contains the data from 01-1959 to 01-2015, and so on.

Note

The datasets released on a monthly basis since 01-2015 are referred to as real-time vintages. The authors have also made available the datasets from 08-1999 to 12-2014, which are referred to as historical vintages. The historical vintages can be downloaded from this link.

The first row of each CSV file includes the codes of the suggested transformations to be applied to the time series in order to make them stationary prior to using them in a statistical model. The transformation codes are defined as follows:

1. no transformation

2. first order difference

3. second order difference

4. logarithm

5. first order logarithmic difference

6. second order logarithmic difference

7. percentage change

The FRED-MD dataset has been used extensively for forecasting US inflation. In [2] it was shown that a random forest model trained on the FRED-MD dataset outperforms several standard inflation forecasting models at different forecasting horizons. [3] expanded the analysis in [2] to include an LSTM model and found that it did not significantly outperform the random forest model. [4] applied different dimension reduction techniques to the FRED-MD dataset in order to forecast US inflation and found that autoencoders provide the best performance. In [5] it was shown that machine learning models trained on the FRED-MD dataset outperform the standard linear regression model in all considered forecasting periods.

Code

In this section, we provide the Python code for downloading and processing the FRED-MD dataset. We start by importing the dependencies.

import os
import pandas as pd
import numpy as np

After that we define a function for transforming the time series based on their assigned transformation code.

def transform_series(x, tcode):
    '''
    Transform the time series.

    Parameters:
    ______________________________
    x: pandas.Series
        Time series.

    tcode: int.
        Transformation code.
    '''

    if tcode == 1:
        return x
    elif tcode == 2:
        return x.diff()
    elif tcode == 3:
        return x.diff().diff()
    elif tcode == 4:
        return np.log(x)
    elif tcode == 5:
        return np.log(x).diff()
    elif tcode == 6:
        return np.log(x).diff().diff()
    elif tcode == 7:
        return x.pct_change()
    else:
        raise ValueError(f"unknown `tcode` {tcode}")

We can now define a function for downloading and, optionally, transforming the time series.

def get_data(year, month, transform=True):
    '''
    Download and (optionally) transform the time series.

    Parameters:
    ______________________________
    year: int
        The year of the dataset vintage.

    month: int.
        The month of the dataset vintage.

    transform: bool.
        Whether the time series should be transformed or not.
    '''

    # get the dataset URL
    file = f"https://files.stlouisfed.org/files/htdocs/fred-md/monthly/{year}-{format(month, '02d')}.csv"

    # get the time series
    data = pd.read_csv(file, skiprows=[1], index_col=0)
    data.columns = [c.upper() for c in data.columns]

    # process the dates
    data = data.loc[pd.notna(data.index), :]
    data.index = pd.date_range(start="1959-01-01", freq="MS", periods=len(data))

    if transform:

        # get the transformation codes
        tcodes = pd.read_csv(file, nrows=1, index_col=0)
        tcodes.columns = [c.upper() for c in tcodes.columns]

        # transform the time series
        data = data.apply(lambda x: transform_series(x, tcodes[x.name].item()))

    return data

We can then use the above function for downloading the 12-2023 dataset vintage as follows:

dataset = get_data(year=2023, month=12, transform=False)

dataset.head(n=3)

dataset.tail(n=3)

Tip

A Python notebook with additional functions for working with the FRED-MD dataset is available in our GitHub repository.

References

[1] McCracken, M. W., & Ng, S. (2016). FRED-MD: A monthly database for macroeconomic research. Journal of Business & Economic Statistics, 34(4), 574-589. doi: 10.1080/07350015.2015.1086655.

[2] Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., & Zilberman, E. (2021). Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98-119. doi: 10.1080/07350015.2019.1637745.

[3] Paranhos, L. (2023). Predicting Inflation with Recurrent Neural Networks. Working Paper.

[4] Hauzenberger, N., Huber, F., & Klieber, K. (2023). Real-time inflation forecasting using non-linear dimension reduction techniques. International Journal of Forecasting, 39(2), 901-921. doi: 10.1016/j.ijforecast.2022.03.002.

[5] Malladi, R. K. (2023). Benchmark Analysis of Machine Learning Methods to Forecast the US Annual Inflation Rate During a High-Decile Inflation Period. Computational Economics, 1-41. doi: 10.1007/s10614-023-10436-w.

Appendix

1. Output and Income

Name	Description
CUMFNS	Capacity Utilization: Manufacturing
INDPRO	IP: Index
IPBUSEQ	IP: Business Equipment
IPCONGD	IP: Consumer Goods
IPDCONGD	IP: Durable Consumer Goods
IPDMAT	IP: Durable Materials
IPFINAL	IP: Final Products (Market Group)
IPFPNSS	IP: Final Products and Nonindustrial Supplies
IPFUELS	IP: Fuels
IPMANSICS	IP: Manufacturing (SIC)
IPMAT	IP: Materials
IPNCONGD	IP: Nondurable Consumer Goods
IPNMAT	IP: Nondurable Materials
IPB51222S	IP: Residential Utilities
RPI	Real Personal Income
W875RX1	Real personal income ex transfer receipts

Output and Income (group 1) FRED-MD time series as of 12-2023.

2. Labor Market

Name	Description
USCONS	All Employees: Construction
DMANEMP	All Employees: Durable goods
USFIRE	All Employees: Financial Activities
USGOOD	All Employees: Goods-Producing Industries
USGOVT	All Employees: Government
MANEMP	All Employees: Manufacturing
CES1021000001	All Employees: Mining and Logging: Mining
NDMANEMP	All Employees: Nondurable goods
USTRADE	All Employees: Retail Trade
SRVPRD	All Employees: Service-Providing Industries
PAYEMS	All Employees: Total nonfarm
USTPU	All Employees: Trade, Transportation & Utilities
USWTRADE	All Employees: Wholesale Trade
UEMPMEAN	Average Duration of Unemployment (Weeks)
CES2000000008	Avg Hourly Earnings: Construction
CES0600000008	Avg Hourly Earnings: Goods-Producing
CES3000000008	Avg Hourly Earnings: Manufacturing
CES0600000007	Avg Weekly Hours: Goods-Producing
AWHMAN	Avg Weekly Hours: Manufacturing
AWOTMAN	Avg Weekly Overtime Hours: Manufacturing
CE16OV	Civilian Employment
CLF16OV	Civilian Labor Force
UNRATE	Civilian Unemployment Rate
UEMP15OV	Civilians Unemployed - 15 Weeks & Over
UEMPLT5	Civilians Unemployed - Less Than 5 Weeks
UEMP15T26	Civilians Unemployed for 15-26 Weeks
UEMP27OV	Civilians Unemployed for 27 Weeks and Over
UEMP5TO14	Civilians Unemployed for 5-14 Weeks
HWI	Help-Wanted Index for United States
CLAIMSX	Initial Claims
HWIURATIO	Ratio of Help Wanted/No. Unemployed

Labor Market (group 2) FRED-MD time series as of 12-2023.

3. Consumption and Orders

Name	Description
HOUSTMW	Housing Starts, Midwest
HOUSTNE	Housing Starts, Northeast
HOUSTS	Housing Starts, South
HOUSTW	Housing Starts, West
HOUST	Housing Starts: Total New Privately Owned
PERMIT	New Private Housing Permits (SAAR)
PERMITMW	New Private Housing Permits, Midwest (SAAR)
PERMITNE	New Private Housing Permits, Northeast (SAAR)
PERMITS	New Private Housing Permits, South (SAAR)
PERMITW	New Private Housing Permits, West (SAAR)

Consumption and Orders (group 3) FRED-MD time series as of 12-2023.

4. Orders and Inventories

Name	Description
UMCSENTX	Consumer Sentiment Index
ACOGNO	New Orders for Consumer Goods
AMDMNOX	New Orders for Durable Goods
ANDENOX	New Orders for Nondefense Capital Goods
CMRMTSPLX	Real Manu. and Trade Industries Sales
DPCERA3M086SBEA	Real personal consumption expenditures
RETAILX	Retail and Food Services Sales
BUSINVX	Total Business Inventories
ISRATIOX	Total Business: Inventories to Sales Ratio
AMDMUOX	Unfilled Orders for Durable Goods

Orders and Inventories (group 4) FRED-MD time series as of 12-2023.

5. Money and Credit

Name	Description
BUSLOANS	Commercial and Industrial Loans
DTCOLNVHFNM	Consumer Motor Vehicle Loans Outstanding
M1SL	M1 Money Stock
M2SL	M2 Money Stock
BOGMBASE	Monetary Base
CONSPI	Nonrevolving consumer credit to Personal Income
REALLN	Real Estate Loans at All Commercial Banks
M2REAL	Real M2 Money Stock
NONBORRES	Reserves Of Depository Institutions
INVEST	Securities in Bank Credit at All Commercial Banks
DTCTHFNM	Total Consumer Loans and Leases Outstanding
NONREVSL	Total Nonrevolving Credit
TOTRESNS	Total Reserves of Depository Institutions

Money and Credit (group 5) FRED-MD time series as of 12-2023.

6. Interest Rates and Exchange Rates

Name	Description
T1YFFM	1-Year Treasury C Minus FEDFUNDS
GS1	1-Year Treasury Rate
T10YFFM	10-Year Treasury C Minus FEDFUNDS
GS10	10-Year Treasury Rate
CP3MX	3-Month AA Financial Commercial Paper Rate
COMPAPFFX	3-Month Commercial Paper Minus FEDFUNDS
TB3MS	3-Month Treasury Bill
TB3SMFFM	3-Month Treasury C Minus FEDFUNDS
T5YFFM	5-Year Treasury C Minus FEDFUNDS
GS5	5-Year Treasury Rate
TB6MS	6-Month Treasury Bill
TB6SMFFM	6-Month Treasury C Minus FEDFUNDS
EXCAUSX	Canada / U.S. Foreign Exchange Rate
FEDFUNDS	Effective Federal Funds Rate
EXJPUSX	Japan / U.S. Foreign Exchange Rate
BAAFFM	Moody’s Baa Corporate Bond Minus FEDFUNDS
AAAFFM	Moodys Aaa Corporate Bond Minus FEDFUNDS
AAA	Moodys Seasoned Aaa Corporate Bond Yield
BAA	Moodys Seasoned Baa Corporate Bond Yield
EXSZUSX	Switzerland / U.S. Foreign Exchange Rate
TWEXAFEGSMTHX	Trade Weighted U.S. Dollar Index
EXUSUKX	U.S. / U.K. Foreign Exchange Rate

Interest Rates and Exchange Rates (group 6) FRED-MD time series as of 12-2023.

7. Prices

Name	Description
CPIAUCSL	CPI: All Items
CPIULFSL	CPI: All Items Less Food
CUSR0000SA0L5	CPI: All items less medical care
CUSR0000SA0L2	CPI: All items less shelter
CPIAPPSL	CPI: Apparel
CUSR0000SAC	CPI: Commodities
CUSR0000SAD	CPI: Durables
CPIMEDSL	CPI: Medical Care
CUSR0000SAS	CPI: Services
CPITRNSL	CPI: Transportation
OILPRICEX	Crude Oil, spliced WTI and Cushing
WPSID62	PPI: Crude Materials
WPSFD49502	PPI: Finished Consumer Goods
WPSFD49207	PPI: Finished Goods
WPSID61	PPI: Intermediate Materials
PPICMM	PPI: Metals and metal products
DDURRG3M086SBEA	Personal Cons. Exp: Durable goods
DNDGRG3M086SBEA	Personal Cons. Exp: Nondurable goods
DSERRG3M086SBEA	Personal Cons. Exp: Services
PCEPI	Personal Cons. Expend.: Chain Index

Prices (group 7) FRED-MD time series as of 12-2023.

8. Stock Market

Name	Description
S&P 500	S&Ps Common Stock Price Index: Composite
S&P: INDUST	S&Ps Common Stock Price Index: Industrials
S&P DIV YIELD	S&Ps Composite Common Stock: Dividend Yield
S&P PE RATIO	S&Ps Composite Common Stock: Price-Earnings Ratio
VIXCLSX	VIX

Stock Market (group 8) FRED-MD time series as of 12-2023.