FRED-SD#

Back to FRED Datasets

FRED-SD is the package’s state-level macroeconomic data source. In macroforecast, it is loaded through mf.data.load_fred_sd() and returned as a canonical DataBundle.

The official source is the St. Louis Fed FRED-SD page: https://www.stlouisfed.org/research/economists/owyang/fred-sd.

What FRED-SD Contains#

FRED-SD is state-level and mixed-frequency. The official FRED-SD page describes the database as state-level monthly and quarterly observations. The package loads the official Data by Series workbook and infers each column’s native frequency from its observed dates.

This page is intentionally kept separate from the national FRED-MD/FRED-QD pages. FRED-SD needs its own audit because state-series frequencies and edge cases can differ by variable and state, including weekly or otherwise non-monthly/non-quarterly observed spacing in raw files.

Current Snapshot Checked For This Page#

This page was checked against the St. Louis Fed FRED-SD page and the latest official Data by Series workbook on 2026-06-01.

Item

Checked value

Latest workbook label

series-2026-04.xlsx

Latest workbook URL

https://www.stlouisfed.org/-/media/project/frbstl/stlouisfed/research/fred-sd/series/series-2026-04.xlsx

Panel shape after package load

1133 rows x 1428 state-series columns

State count

51

FRED-SD variable count

28

Last date present in panel

2026-04

Official page

https://www.stlouisfed.org/research/economists/owyang/fred-sd

The latest workbook has these native frequency counts:

Native frequency

Number of state-series columns

monthly

861

quarterly

546

unknown

21

These are aggregate counts across all state-series columns. Because FRED-SD is state-level, the state axis must also be checked. The latest workbook has 28 state-series columns for every state/DC, but the monthly/quarterly/unknown mix differs by state when some state-variable cells have too few observations for reliable frequency inference.

The latest workbook has these date-anchor counts:

Date anchor

Number of state-series columns

Meaning

month_start

810

Monthly observations dated on the first day of the month.

monthly_weekday_anchor

51

Monthly observations dated on a repeated weekday pattern rather than month start. In the latest workbook this is ICLAIMS.

quarter_start

546

Quarterly observations dated on the first day of the quarter.

none

21

Too few non-missing observations to infer an anchor.

The unknown frequency category appears when a state-series has too few observed points to infer a reliable date spacing. ICLAIMS is a special case: its dates look weekly because they fall on Saturdays, but each state has one observation per month in the latest workbook, so the package records native_frequency="monthly" and date_anchor="monthly_weekday_anchor".

Loader#

macroforecast.data.load_fred_sd(
    vintage: str | None = None,
    *,
    force: bool = False,
    cache_root: str | pathlib.Path | None = None,
    local_source: str | pathlib.Path | None = None,
    states: list[str] | None = None,
    variables: list[str] | None = None,
) -> DataBundle

Input#

Name

Type

Default

Meaning

vintage

str | None

None

Vintage label in YYYY-MM form. None loads the current official workbook.

force

bool

False

Re-download or re-copy the raw file even if it already exists in cache.

cache_root

path-like or None

None

Root directory for raw-file cache and manifest.

local_source

path-like or None

None

Local FRED-SD workbook or CSV source.

states

list[str] | None

None

State subset. Example: ["CA", "TX"].

variables

list[str] | None

None

FRED-SD variable subset. Example: ["UR", "ICLAIMS"].

For current data, the loader reads the official FRED-SD landing page and uses the latest by-series workbook link. For vintage data, the loader first tries the official series-YYYY-MM.xlsx workbook URL. If that direct workbook is not available, it downloads the official by-series zip for that year or two-year archive and extracts the workbook matching the requested vintage. This differs from FRED-MD/FRED-QD because FRED-SD distributes state data as Excel workbooks, not CSV files with t-code rows.

Output#

load_fred_sd() returns a DataBundle.

Field

Value

bundle.panel

Wide state-level panel with columns such as UR_CA or NQGSP_TX.

bundle.metadata["dataset"]

"fred_sd"

bundle.metadata["frequency"]

"monthly", "quarterly", "weekly", "annual", "mixed", or "unknown" depending on loaded columns.

bundle.metadata["native_frequency_by_column"]

Per-column native frequency map.

bundle.metadata["native_frequency_counts"]

Per-column native frequency counts.

bundle.metadata["date_anchor_by_column"]

Per-column date-anchor map.

bundle.metadata["date_anchor_counts"]

Per-column date-anchor counts.

bundle.metadata["state_summary"]

Per-state coverage, frequency counts, date-anchor counts, observed span, and unknown variables.

bundle.metadata["transform_codes"]

{} because FRED-SD has no official t-code map.

FRED-SD series metadata is stored in:

bundle.panel.attrs["macrocast_reports"]["fred_sd_series_metadata"]

That report contains:

Key

Meaning

series_count

Number of loaded state-series columns.

state_count

Number of states represented in the loaded panel.

sd_variable_count

Number of FRED-SD variable groups represented.

native_frequency_counts

Count of monthly, quarterly, weekly, annual, irregular, unknown, or other inferred frequencies.

date_anchor_counts

Count of date-anchor patterns such as month_start, quarter_start, or monthly_weekday_anchor.

state_summary

State-level coverage summary with frequency and date-anchor counts by state.

series

Row-level metadata for each state-series column.

Each series row contains column, sd_variable, state, source_sheet, native_frequency, date_anchor, observed_start, observed_end, and non_missing_observation_count.

Each state_summary row contains state, series_count, sd_variable_count, native_frequency_counts, date_anchor_counts, observed_start, observed_end, and unknown_variables.

State Coverage In Latest Workbook#

This table is computed from series-2026-04.xlsx. The key audit surface is not only the aggregate FRED-SD panel shape, but also whether each state/DC has the same number of columns and which state-variable pairs fall into unknown frequency.

State

State-series columns

Monthly

Quarterly

Unknown

Date-anchor pattern

Earliest obs.

Latest obs.

Unknown variables

AK

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1950-01-01

2026-04-04

NATURNQGSP

AL

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

AR

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

AZ

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

CA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

CO

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

CT

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

NATURNQGSP

DC

28

16

8

4

month_start: 15, monthly_weekday_anchor: 1, none: 4, quarter_start: 8

1948-01-01

2026-04-04

CONSTNQGSP, MANNQGSP, MINNG, NATURNQGSP

DE

28

16

10

2

month_start: 15, monthly_weekday_anchor: 1, none: 2, quarter_start: 10

1948-01-01

2026-04-04

MINNG, NATURNQGSP

FL

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

GA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

HI

28

16

10

2

month_start: 15, monthly_weekday_anchor: 1, none: 2, quarter_start: 10

1950-01-01

2026-04-04

MINNG, NATURNQGSP

IA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

ID

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

NATURNQGSP

IL

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

IN

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

NATURNQGSP

KS

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

KY

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

LA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

MA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

MD

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

ME

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

NATURNQGSP

MI

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

MN

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

MO

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

MS

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

MT

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

NC

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

ND

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

NE

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

NH

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

NJ

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

NM

28

16

11

1

month_start: 15, monthly_weekday_anchor: 1, none: 1, quarter_start: 11

1948-01-01

2026-04-04

RENTS

NV

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

NY

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

OH

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

OK

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

OR

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

PA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

RI

28

16

9

3

month_start: 15, monthly_weekday_anchor: 1, none: 3, quarter_start: 9

1948-01-01

2026-04-04

CONSTNQGSP, NATURNQGSP, RENTS

SC

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

SD

28

16

11

1

month_start: 15, monthly_weekday_anchor: 1, none: 1, quarter_start: 11

1948-01-01

2026-04-04

RENTS

TN

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

TX

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

UT

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

VA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

VT

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

NATURNQGSP

WA

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

WI

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

NATURNQGSP

WV

28

17

11

0

month_start: 16, monthly_weekday_anchor: 1, quarter_start: 11

1948-01-01

2026-04-04

-

WY

28

17

10

1

month_start: 16, monthly_weekday_anchor: 1, none: 1, quarter_start: 10

1948-01-01

2026-04-04

MANNQGSP

Variable Coverage In Latest Workbook#

This table is computed from series-2026-04.xlsx. Frequency and date-anchor patterns are column counts across states. Start/end dates are the widest observed range across states for that variable. Unknown states lists states whose state-series has too few observations for reliable frequency inference.

Variable

Native frequency pattern

Date-anchor pattern

States

Earliest obs.

Latest obs.

Non-missing obs.

Unknown states

BPPRIVSA

monthly: 51

month_start: 51

51

1988-01-01

2025-08-01

23052

-

CONS

monthly: 51

month_start: 51

51

1990-01-01

2026-02-01

22034

-

CONSTNQGSP

quarterly: 49, unknown: 2

none: 2, quarter_start: 49

51

2005-01-01

2025-10-01

4112

DC, RI

EXPORTS

monthly: 51

month_start: 51

51

1995-08-01

2026-02-01

18717

-

FIRE

monthly: 51

month_start: 51

51

1990-01-01

2025-12-01

22032

-

FIRENQGSP

quarterly: 51

quarter_start: 51

51

2005-01-01

2025-10-01

4284

-

GOVNQGSP

quarterly: 51

quarter_start: 51

51

2005-01-01

2025-10-01

4284

-

GOVT

monthly: 51

month_start: 51

51

1990-01-01

2025-12-01

22032

-

ICLAIMS

monthly: 51

monthly_weekday_anchor: 51

51

1985-09-28

2026-04-04

24662

-

IMPORTS

monthly: 51

month_start: 51

51

2008-01-01

2026-02-01

11118

-

INFO

monthly: 51

month_start: 51

51

1990-01-01

2025-12-01

22032

-

INFONQGSP

quarterly: 51

quarter_start: 51

51

2005-01-01

2025-10-01

4244

-

LF

monthly: 51

month_start: 51

51

1976-01-01

2025-08-01

30396

-

MANNQGSP

quarterly: 49, unknown: 2

none: 2, quarter_start: 49

51

2005-01-01

2025-10-01

4100

DC, WY

MFG

monthly: 51

month_start: 51

51

1990-01-01

2025-12-01

22032

-

MFGHRS

monthly: 51

month_start: 51

51

2007-01-01

2025-12-01

11628

-

MINNG

monthly: 48, unknown: 3

month_start: 48, none: 3

51

1990-01-01

2025-12-01

20364

DC, DE, HI

NA

monthly: 51

month_start: 51

51

1990-01-01

2026-02-01

22048

-

NATURNQGSP

quarterly: 40, unknown: 11

none: 11, quarter_start: 40

51

2005-01-01

2025-10-01

3340

AK, CT, DC, DE, HI, ID, IN, ME, RI, VT, WI

NQGSP

quarterly: 51

quarter_start: 51

51

2005-01-01

2025-10-01

4284

-

OTOT

quarterly: 51

quarter_start: 51

51

1948-01-01

2025-10-01

15896

-

PARTRATE

monthly: 51

month_start: 51

51

1976-01-01

2025-08-01

30396

-

PSERV

monthly: 51

month_start: 51

51

1990-01-01

2025-12-01

22032

-

PSERVNQGSP

quarterly: 51

quarter_start: 51

51

2005-01-01

2025-10-01

4284

-

RENTS

monthly: 48, unknown: 3

month_start: 48, none: 3

51

1990-01-01

2025-12-01

20736

NM, RI, SD

STHPI

quarterly: 51

quarter_start: 51

51

1975-01-01

2025-10-01

10404

-

UR

monthly: 51

month_start: 51

51

1976-01-01

2025-08-01

30396

-

UTILNQGSP

quarterly: 51

quarter_start: 51

51

2005-01-01

2025-10-01

4268

-

The official FRED-SD page includes a correction noting that OTOT and STHPI were previously described as monthly, but are quarterly. The latest workbook confirms both as quarterly in all 51 states.

No Official T-Codes#

FRED-SD does not provide official stationarity transformation codes. Therefore:

mf.preprocessing.reprocess(fred_sd_bundle)

raises unless the user explicitly chooses transform="none" or supplies custom codes.

Recommended explicit paths:

processed = mf.preprocessing.reprocess(fred_sd_bundle, transform="none")

or:

codes, provenance = mf.preprocessing.fred_sd_transform_codes(
    fred_sd_bundle,
    variable_codes={"UR": 2, "ICLAIMS": 5},
    return_table=True,
)

processed = mf.preprocessing.reprocess(
    fred_sd_bundle,
    transform="custom",
    transform_codes=codes,
)

fred_sd_transform_codes() can use user choices and package suggestions based on national FRED-MD/FRED-QD analogs. These are suggestions, not official metadata. The provenance table uses suggestion_confidence to distinguish:

Value

Meaning

user

The code was supplied by the user.

high

Built-in national-analog suggestion with close variable match.

medium

Built-in national-analog suggestion with weaker match.

none

No t-code was assigned.

Example#

import macroforecast as mf

sd = mf.data.load_fred_sd(states=["CA", "TX"], variables=["UR", "ICLAIMS"])
codes = mf.preprocessing.fred_sd_transform_codes(
    sd,
    variable_codes={"UR": 2, "ICLAIMS": 5},
)
processed = mf.preprocessing.reprocess(
    sd,
    transform="custom",
    transform_codes=codes,
)

Official URLs#

Source

URL

FRED-SD official page

https://www.stlouisfed.org/research/economists/owyang/fred-sd

Latest Data by Series workbook checked here

https://www.stlouisfed.org/-/media/project/frbstl/stlouisfed/research/fred-sd/series/series-2026-04.xlsx