Data

Download GMD Data

Enter your email to download the GMD in your preferred format.

Access GMD via Packages

You can access the Global Macro Database directly through our packages for Stata, Python, and R.

Stata Logo Access via Stata

Install Stata Package:
Our package is now officially available on the Stata SSC Archive. You can install it using:

Install package:
ssc install gmd
GMD Stata Syntax:
gmd variable_abbreviation, option
To load the full dataset:
gmd
Download raw data example: Download underlying raw rGDP data (only works if a variable is specified)
gmd rGDP, raw
Advanced filtering example: Download rGDP data and filter for a country (e.g., France)
gmd rGDP, country(FRA)

Additional resources:
For detailed syntax, type help gmd in the Stata terminal.

Python Logo Access via Python
Install via pip: pip install badge
pip install global_macro_data
Example usage:
from global_macro_data import gmd

      # Get preview data
      df = gmd()

      # Get latest version
      df = gmd(show_preview=False)

      # Specific version
      df = gmd(version="2025_01")

      # Specific country
      df = gmd(country="USA")

      # Multiple countries
      df = gmd(country=["USA", "CHN", "DEU"])

      # Specific variables
      df = gmd(variables=["rGDP", "infl", "unemp"])

      # Combine filters
      df = gmd(version="2025_01", country=["USA", "CHN"], variables=["rGDP", "unemp", "CPI"])
Parameter guide:
  • version (str): Dataset version in format 'YYYY_MM'. Defaults to latest.
  • country (str or list): ISO3 code(s), e.g., "SGP" or ["SGP", "MRT"].
  • variables (list): Variable codes like ["rGDP", "unemp"].
  • show_preview (bool): If True with no filters, shows sample data.
More info: Please see package code on our GMD Python Package GitHub Page.
R Logo Access via R
Install via devtools:
# Install devtools if not already installed
      install.packages("devtools")

      # Install the package from GitHub
      devtools::install_github("KMueller-Lab/Global-Macro-Database-R")
Example usage:
# Load the Package
      library(globalmacrodata)

      # Get preview data
      df <- gmd()

      # Get data from latest available version
      df <- gmd(show_preview = FALSE)

      # Get data from a specific version
      df <- gmd(version = "2025_01")

      # Get data for a specific country
      df <- gmd(country = "USA")

      # Get data for multiple countries
      df <- gmd(country = c("USA", "CHN", "DEU"))

      # Get specific variables
      df <- gmd(variables = c("rGDP", "infl", "unemp"))

      # Combine parameters
      df <- gmd(version = "2025_01", country = c("USA", "CHN"), variables = c("rGDP", "unemp", "CPI"))
Parameter guide:
  • version (character): Dataset version in format "YYYY_MM". Defaults to latest.
  • country (character or vector): ISO3 code(s), e.g., "SGP" or c("MRT", "SGP").
  • variables (character or vector): Variable codes like c("rGDP", "unemp").
  • show_preview (logical): If TRUE with no filters, shows preview data.
More info: Please see package code on our GMD R Package GitHub Page.

Release Schedule

Release Date Details
2025-01-31 Initial release: 2025_01
2025-03-31 Version: 2025_03
2025-06-30 Current Version: 2025_06
2025-09-30 Version: 2025_09
2025-12-31 Version: 2025_12
2026-03-31 Version: 2026_03

Release Notes

📦 Version 2025_06 – June 30, 2025New
📅 Released: June 30, 2025

Inflation Data Patch

  • Revised priority list to integrate new sources
  • Fixed issues in inflation data caused by breaks in the CPI

New Sources and Coverage

  • Expanded coverage with six new data sources: RR_infl, Clio, UN_trade, BEL_1, CAN_2, and KOR_2.
  • Updated source priority ordering for nGDP, cons, inv, finv, imports, and exports

Government Finance Methodology

  • Now construct government finance variables based on their ratio to GDP
  • Derived absolute-level series from ratios and applied chainlinking methodology
  • Applies to: govdebt, govdef, govexp, govrev, and govtax

Bug Fixes and Improvements

  • Improved robustness of the data cleaning pipeline
  • Resolved minor inconsistencies across historical sources
📦 Version 2025_03 – March 31, 2025
📅 Released: March 31, 2025

Overview

This release includes updated annual data, expanded historical coverage for key countries, resolved inconsistencies in past series, and introduces new ways to access the database through our Python, R, and Stata packages.

Data Updates

All datasets have been updated to include the most recently published annual values as of the release date.

Expanded Historical Coverage

New long-run series have been added for Argentina, Ireland, and Taiwan, enriching the database's historical scope.

IMF WEO Treatment Refined

We now treat the IMF World Economic Outlook (WEO) as two distinct sources: one for historical values and one for forecasts. This allows for clearer documentation and improved management of discontinuities between past data and forward-looking projections.

World Bank Data Prioritization

We have adjusted our source hierarchy to prioritize data from the World Development Indicators (WDI) over both editions of the IMF WEO. This change has resulted in some level adjustments, while maintaining stable trends. Correlation with previous versions remains high across variables—for example, nominal GDP retains a minimum correlation of 0.97 between the old and new series.

Exchange Rate Coverage

Monaco has been added to the EUR_fx irrevocable exchange rate list with a fixed rate of 6.55, aligned with the French Franc, which served as the country’s de facto currency prior to euro adoption.

Bug Fixes

Corrected inaccuracies in Australia’s long-run historical real GDP in USD figures.

New Access Tools

  • Python and R packages: We are excited to announce that our data can now also be easily accessed using our newly-released Python and R packages. See the Data page for setup instructions.
  • Official Stata package: Now available through the SSC Archive. New function updated for downloading underlying raw data via the "raw" option when using a specific variable.
📦 Version 2025_01 – January 31, 2025
📅 Released: January 31, 2025

Initial Release

  • Includes 46 core macro-financial variables
  • Covers 243 countries and territories
  • Annual time series spanning from the 1086 to 2024 with projections up to 2030

Data Sources

  • Integrated 32 major contemporary sources (e.g., IMF, World Bank, OECD)
  • Incorporated 78 historical datasets to extend long-run coverage

Structure and Design

  • Unified data model across all countries, variables, and time periods
  • Standardized indicator abbreviations and ISO3 country codes
  • Laid foundation for subsequent versioned updates and cross-platform access

Contribute

We welcome contributions! Submit issues, feedback, or pull requests on our GitHub repository, or email us at [email protected].