Debian Science Economics packages

Dolo is a tool to assist researchers in solving several types of Dynamic Stochastic General Equilibrium (DSGE) models, using either local of global approximation methods.

Users are can separate the definition of their models from the solution algorithm. A simple syntax is provided in YAML files to define variables and equations of several types. This syntax integrates the specification of occasionally binding constraints (a.k.a. complementarity conditions)

The user can then implement his own preferred solution method using one of the provided tools (various types of interpolation, solvers, ...) or use one of the already implemented procedures.

Dolo is written in Python and so are his solution routines. If you prefer or need to use another language for the solution, you can always use dolo as a preprocessor. In that case, dolo will just translate the model file into a numerical file usable by your software. Currently, Octave/MATLAB and Julia are supported.

Minsky is one of a family of ``system dynamics'' computer programs. These programs allow a dynamic model to be constructed, not by writing mathematical equations or numerous lines of computer code, but by laying out a model of a system in a flowchart, which can then simulate the system. These programs are now the main tool used by engineers to design complex products, ranging from small electrical components right up to passenger jets.

What does Minsky provide that other system dynamics programs don't boils down to one feature: The Godley Table that enables a dynamic model of financial flows to be derived from a table that is very similar to the accountant's double-entry bookkeeping table. Hence Minsky is very well suited for simulating Stock-Flow Consistent (SFC) models.

NetLogo is a programmable modeling environment for simulating natural and social phenomena. It was authored by Uri Wilensky in 1999 and has been in continuous development ever since at the Center for Connected Learning and Computer-Based Modeling.

NetLogo is particularly well suited for modeling complex systems developing over time. Modelers can give instructions to hundreds or thousands of "agents" all operating independently. This makes it possible to explore the connection between the micro-level behavior of individuals and the macro-level patterns that emerge from their interaction.

This package allows to simulate Post-Keynesian Stock-Flow Consistent Models, following the approach of Godley, W. and M. Lavoie, 2007: Monetary Economics An Integrated Approach to Credit, Money, Income, Production and Wealth.

The package uses the Gauss-Seidel algorithm to solve linear systems of equations, following the approach found in Kinsella, Stephen and O’Shea, Terence, Solution and Simulation of Large Stock Flow Consistent Monetary Production Models Via the Gauss Seidel Algorithm.

pandaSDMX is SDMX software that facilitates the acquisition and analysis of SDMX-2.1 compliant data and metadata. These can be rendered in various formats:

• as a hierarchical data structure following closely the SDMX 2.1 information model. Technically, the model is a layer on top of the XML structure rendered by SDMX web services.
• as arbitrary output generated by dedicated writers. Currently, there is only one writer rendering data as pandas Series and DataFrames.

QuantEcon provides a high performance, open source Python code library for economics.

Contains data sets that can be used to replicate a large set of published papers.

Download economic and financial time series from public sources, including the St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics, the US Energy Information Administration, the World Bank, Eurostat, the European Central Bank, the Bank of England, the UK's Office of National Statistics, Deutsche Bundesbank, and INSEE.

This packages implements vector autoregressive-, structural vector autoregressive- and structural vector error correction models in R. In addition to the three cornerstone functions VAR(), SVAR() and SVEC() for estimating such models, functions for diagnostic testing, estimation of a restricted models, prediction, causality analysis, impulse response analysis and forecast error variance decomposition are provided too. It is further possible to convert vector error correction models into their level VAR representation.

BMR (Bayesian Macroeconometrics in R) is a collection of R and C++ routines for estimating Bayesian Vector Autoregressive (BVAR) and Dynamic Stochastic General Equilibrium (DSGE) models in the R statistical environment. Features

For BVARs, BMR supports: * normal-inverse-Wishart prior, * Minnesota prior, and * Mattias Villani's steady-state prior. The BMR package can also estimate BVARs with time-varying parameters, as well as classical (non-Bayesian) VARs.

For DSGE models, the package can: * solve models using either Harald Uhlig's method of undetermined coefficients

  or Chris Sims' canonical decomposition;
* estimate models using MCMC by means of a Kalman filter or the Chandrasekhar
  recursions;
* and estimate a hybrid DSGE-VAR model.

gEcon allows users to describe their models in terms of optimisation problems of agents. Given optimisation problems, constraints and identities, gEcon derives the first order conditions, steady state equations, and linearisation matrices automatically. Numerical solvers can be then employed to determine the steady state and approximate equilibrium laws of motion around it.

RECS is a MATLAB solver for dynamic, stochastic, rational expectations equilibrium models. RECS stands for "Rational Expectations Complementarity Solver". This name emphasizes that RECS has been developed specifically to solve models that include complementarity equations, also known as models with occasionally binding constraints.

RECS is designed to solve small-scale nonlinear and complementarity models, but not large-scale models. For solving large-scale problems, but without complementarity equations, see Dynare or similar toolboxes.

Repast for High Performance Computing (Repast HPC) is a next generation agent-based modeling system intended for large-scale distributed computing platforms. It implements the core Repast Simphony concepts (e.g. contexts and projections), modifying them to work in a parallel distributed environment.

Repast HPC is written in cross-platform C++. It can be used on workstations, clusters, and supercomputers running Apple macOS, Linux, or Unix. Portable models can be written in either standard or Logo-style C++.

Repast HPC has been successfully tested for scalability on Argonne National Laboratory's Blue Gene/P.

Repast Simphony is a tightly integrated, richly interactive, cross platform Java-based modeling system. It supports the development of extremely flexible models of interacting agents for use on workstations and computing clusters.

Repast Simphony models can be developed in several different forms including the ReLogo dialect of Logo, point-and-click statecharts, Groovy, or Java, all of which can be fluidly interleaved.

Repast Simphony has been successfully used in many application domains including social science, consumer products, supply chains, possible future hydrogen infrastructures, and ancient pedestrian traffic to name a few.