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Board of Governors of the Federal Reserve System

The FRB/US model Python package, or PyFRB/US, is a Python-based platform to run simulations with the FRB/US model. This package includes the FRB/US model equations, simulation code, example programs, and technical documentation on the usage of the FRB/US model in Python. It contains the following:

  • A README file with installation instructions and basic information;
  • The pyfrbus directory, which includes the platform code required to solve the model;
  • The models directory, containing the FRB/US equations;
  • The demos directory, which provides a suite of example programs for running FRB/US simulations in Python;
  • The docs directory, where users can find detailed technical information on PyFRB/US features, installation, model API, and simulation options.

Documentation is provided in HTML format and can be opened in your web browser. This package requires Python 3 and is not compatible with Python 2.

Data-Only Package and Disclaimer

Note: Because the FRB/US database is updated more frequently than the model and other material, the database is stored separately in the FRB/US data package. To run the model with the latest data, please download the FRB/US data package and copy the LONGBASE.TXT dataset into the data folder. When updates are available only for the dataset, it is not necessary to re-download the FRB/US Python package.

The following zip file contains the historical dataset (csv format) and FRB/US dataset (csv format and EViews edb format). The FRB/US dataset merges historical values with a mechanical extrapolation whose initial part follows the median path in the FOMC’s Summary of Economic Projections (SEP). Beyond the horizon of the SEP, variables for real GDP, PCE inflation, unemployment, and the federal funds rate automatically converge to the median long-run SEP targets. This dataset is provided as a convenience, so that users have data on which to run the FRB/US model, and is for illustrative purposes only. The trajectories in this dataset are not FRB/US model forecasts, nor should they be interpreted as forecasts of the FOMC, the Federal Reserve Board or its staff.

Board of Governors of the Federal Reserve System

FRB/US, a large-scale, nonlinear macroeconomic model of the U.S., has been in use at the Federal Reserve Board for 25 years. For nearly as long, the FRB/US “project” has included a linear version of the model known as LINVER. A key reason that LINVER exists is the vast reduction in the computational costs that linearity confers when running experiments requiring large numbers of simulations under the assumption that expectations are model-consistent (MC). The public has been able to download FRB/US simulation code, documentation, and data from the Federal Reserve Board’s website since 2014. To further expand access to and understanding of the FRB/US project, a package devoted to LINVER is now available on the website. In this paper, we provide both a general introduction to LINVER and an overview of the contents and capabilities of its package. We review the ways that LINVER has been used in past research to study key policy issues; describe the package’s comprehensive set of programs for running simulations with MC expectations, with or without imposing the effective lower bound (ELB) on the federal funds rate and other nonlinear constraints; and illustrate how LINVER deterministic and stochastic simulations can be used to gauge the implications of the ELB for macroeconomic performance and to assess different strategies for mitigating its adverse effects.

DOI: https://doi.org/10.17016/FEDS.2022.053

Disclaimer: The economic research that is linked from this page represents the views of the authors and does not indicate concurrence either by other members of the Board’s staff or by the Board of Governors. The economic research and their conclusions are often preliminary and are circulated to stimulate discussion and critical comment. The Board values having a staff that conducts research on a wide range of economic topics and that explores a diverse array of perspectives on those topics. The resulting conversations in academia, the economic policy community, and the broader public are important to sharpening our collective thinking.


PyFR 2.0.0 is an open-source flow solver that uses the high-order flux reconstruction method. For more information on the PyFR project visit our website, or to ask a question visit our forum.

  • Installation
    • Quick-start
      • macOS
      • Ubuntu
      • Dependencies
      • Running PyFR
      • Configuration File (.ini)
        • Backends
        • Systems
        • Boundary and Initial Conditions
        • Nodal Point Sets
        • Plugins
        • Additional Information
        • A Brief Overview of the PyFR Framework
          • Where to Start
          • Controller
          • Stepper
          • PseudoStepper
          • System
          • Elements
          • Interfaces
          • Backend
          • Pointwise Kernel Provider
          • Kernel Generator
          • PyFR-Mako Kernels
          • PyFR-Mako Macros
          • Syntax
          • OpenMP Backend
            • AVX-512
            • Cores vs. threads
            • Loop Scheduling
            • MPI processes vs. OpenMP threads
            • Asynchronous MPI progression
            • CUDA-aware MPI
            • HIP-aware MPI
            • METIS vs SCOTCH
            • Mixed grids
            • Detecting load imbalances
            • Euler Equations
              • 2D Euler Vortex
              • 2D Double Mach Reflection
              • 2D Couette Flow
              • 2D Incompressible Cylinder Flow
              • 2D Viscous Shock Tube
              • 3D Triangular Aerofoil
              • 3D Taylor-Green

              Indices and Tables

              © Copyright 2013–2024, Imperial College London. Revision 50fd3c45 .

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              pyFRF 1.1

              Frequency response function as used in structural dynamics.


              Verified details

              These details have been verified by PyPI


              Unverified details

              These details have not been verified by PyPI

              Ссылки проекта
              GitHub Statistics

              Метки FRF, MIMO, SIMO, ODS

              Описание проекта

              Frequency response function as used in structural dynamics.

              For more information check out the showcase examples and see documentation.

              Basic pyFRF usage:

              Make an instance of FRF class:

              Adding data:

              We can add the excitation and response data at the beginning through exc and resp arguments, otherwise, the excitation and response data can be added later via add_data() method:

              Computing FRF:

              Preferable way to get the frequency response functions is via get_FRF() method:

              We can also directly get the requested FRF via other methods: get_H1() , get_H2() , get_Hv() and, get_ods_frf() :

              to test the Showcase.ipynb.

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