Debian Science Economics packages

Dynare is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models. The models solved by Dynare include those relying on the rational expectations hypothesis, wherein agents form their expectations about the future in a way consistent with the model. But Dynare is also able to handle models where expectations are formed differently: on one extreme, models where agents perfectly anticipate the future; on the other extreme, models where agents have limited rationality or imperfect knowledge of the state of the economy and, hence, form their expectations through a learning process. In terms of types of agents, models solved by Dynare can incorporate consumers, productive firms, governments, monetary authorities, investors and financial intermediaries. Some degree of heterogeneity can be achieved by including several distinct classes of agents in each of the aforementioned agent categories.

Dynare offers a user-friendly and intuitive way of describing these models. It is able to perform simulations of the model given a calibration of the model parameters and is also able to estimate these parameters given a dataset. In practice, the user will write a text file containing the list of model variables, the dynamic equations linking these variables together, the computing tasks to be performed and the desired graphical or numerical outputs.

This package provides a full installation of Dynare, to be run on top of GNU Octave.

The GNU Regression, Econometric and Time-Series Library (gretl) is a software package for econometric analysis. The package comprises a shared library, a command-line client program, and a graphical client built using GTK+.

This package provides the GTK+ client and the command-line client.

This package provides functions to work with econometrics in Octave, a numerical computation software. The functions include methods to do maximum likelihood (mle_estimate) and general method of moments (gmm_estimate) based estimations.

This Octave add-on package is part of the Octave-Forge project.

statsmodels Python3 module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for several distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem.

R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.

The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency, and many of R's core functions do so. The R distribution contains functionality for a large number of statistical procedures and underlying applied math computations. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.

Additionally, several thousand extension "packages" are available from CRAN, the Comprehensive R Archive Network, many also as Debian packages, named 'r-cran-'.

This package is a metapackage which eases the transition from the pre-1.5.0 package setup with its larger r-base package. Once installed, it can be safely removed and apt-get will automatically upgrade its components during future upgrades. Providing this package gives a way to users to then only install r-base-core if they so desire.

This GNU R package provides functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)

The bayesm package covers many important models used in marketing and micro-econometrics applications. The package includes:

• Bayes Regression (univariate or multivariate dep var)
• Multinomial Logit (MNL) and Multinomial Probit (MNP)
• Multivariate Probit,
• Multivariate Mixtures of Normals
• Hierarchical Linear Models with normal prior and covariates
• Hierarchical Multinomial Logits with mixture of normals prior and covariates
• Bayesian analysis of choice-based conjoint data
• Bayesian treatment of linear instrumental variables models
• Analyis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)).

For further reference, consult the authors' book, Bayesian Statistics and Marketing by Allenby, McCulloch and Rossi.

This R package provides a user-friendly interface for fitting dynamic linear models and time series regression relationships

The interface and internals of dynlm are very similar to lm, but currently dynlm offers three advantages over the direct use of lm:

• extended formula processing;
• preservation of time series attributes;
• instrumental variables regression (via two-stage least squares).

This package provides functions to import financial and economic data series import and is part of Rmetrics, a collection of packages for financial engineering and computational finance written and compiled by Diethelm Wuertz and others.

fImport provides import function to access (free) data from Economagic, the US Federal Reserve, Forecasts.Org, Yahoo and other web sources.

This package provides functions for modelling of nonlinear time series and is part of Rmetrics, a collection of packages for financial engineering and computational finance written and compiled by Diethelm Wuertz and others.

fNonlinear provides nonlinear time series modelling functions.

This package provides functions for reading and writing data stored by statistical packages such as Minitab, S, SAS, SPSS, Stata, ...

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

This package provides functions for unit root modelling of non-stationary time series and is part of Rmetrics, a collection of packages for financial engineering and computational finance written and compiled by Diethelm Wuertz and others.

fUnitRoots provides modelling functions for non-stationary time series.

This GNU R package is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; ), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; ) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; , Kitamura 1997; , Newey and Smith 2004; , and Anatolyev 2005 ).

The Hmisc library contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, translating SAS datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of S objects to LaTeX code, recoding variables, and bootstrap repeated measures analysis.

This R package provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes.

This CRAN package provides S4 classes and methods for fitting and examining linear mixed effects models (also called multilevel models, panel data models, and several other names) and generalized linear mixed effects models.

This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors. MCMC techniques are also preferable over direct estimation in the presence of missing data.

Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models.

The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.

The package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.

This R package intends to make the estimation of linear panel models straightforward. It provides functions to estimate a wide variety of models= and to make (robust) inference.

The main functions to estimate models are:

• plm: panel data estimators using lm on transformed data,
• pgmm: generalized method of moments (GMM) estimation for panel data,
• pvcm: variable coefficients models for panel data,
• pmg: mean groups (MG), demeaned MG and common correlated effects (CCEMG) estimators.

Next to the model estimation functions, the package offers several functions for statistical tests related to panel data/models.

Multiple functions for (robust) variance-covariance matrices are at hand as well. The package also provides data sets to demonstrate functions and to replicate some text book/paper results.

This package contains the Penn World Tables (PWT), which provide purchasing power parity (PPP) and national income accounts converted to international prices for 189 countries for some or all the years 1950-2010. The data are developed and maintained by scholars at the Center for International Comparisons of Production, Income and Prices (CIC) from the University of Pennsylvania.

The package contains all the releases of the PWT from version 5.6 to 7.1, which were created by the University of Pennsylvania. Note that more recent versions of the PWT have been created by the University of California, Davis and the University of Groningen, and are available in the r-cran-pwt8 and r-cran-pwt9 packages.

This package contains the Penn World Tables (PWT) version 8.x, which provide purchasing power parity (PPP) and national income accounts converted to international prices for 167 countries between 1950 and 2011.

This version of the PWT is produced by the University of California, Davis and the University of Groningen. It is the continuation of the work by the University of Pennsylvania. The older versions of the PWT (version 7 and below) are available in the package r-cran-pwt. A newer version of the PWT (version 9) is available in the package r-cran-pwt9.

This package contains the Penn World Tables (PWT) version 9.x, which provide purchasing power parity (PPP) and national income accounts converted to international prices for 182 countries between 1950 and 2017.

This version of the PWT is produced by the University of California, Davis and the University of Groningen. It is the continuation of the work by the University of Pennsylvania. Older versions of the PWT are available in the packages r-cran-pwt and r-cran-pwt8.

This package provides access to DBnomics data series (https://db.nomics.world/). DBnomics is an open-source project with the goal of aggregating the world’s economic data in one location, free of charge to the public. DBnomics covers hundreds of millions of series from international and national institutions (World Bank, International Monetary Fund, Eurostat, national statistical institutes and central banks…).

This package provides a set of classes and methods to read data and metadata documents exchanged through the Statistical Data and Metadata Exchange (SDMX) framework, currently focusing on the SDMX XML standard format (SDMX-ML).

SDMX is an initiative to foster standards for the exchange of statistical information. It is sponsored by several major providers of statistical information: the Bank for International Settlements, the European Central Bank, Eurostat (the statistical office of the European Union), the International Monetary Fund (IMF), the Organisation for Economic Co-operation and Development (OECD), the United Nations Statistics Division, the United Nations Educational, Scientific and Cultural Organization and the World Bank.

The package can therefore be used to download statistical information from the servers of those organizations, and from those of several other institutions.

This CRAN package provides additional time-series analysis functions, as well as several computational finance routines.

This package provides functions for unit root and cointegration analyses common in applied time series / econometrics.

Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators (WDI), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.

Note that the package does not contain the data. Instead, it provides a set of functions to download the data from the World Bank's website.

Emacs Speaks Statistics (ESS) is an add-on package for GNU Emacs. It is designed to support editing of scripts and interaction with various statistical analysis programs such as R, S, S-Plus, SAS, Stata, BUGS/JAGS and Julia.

It provides the following features:

• Editing source code (R, S, S-plus, SAS, BUGS/JAGS, Stata, Julia)
• Syntactic indentation and highlighting of source code
• Partial evaluation of code
• Loading and error-checking of code
• Source code revision maintenance
• Batch execution (SAS, BUGS/JAGS)
• Use of imenu to provide links to appropriate functions
• Interacting with the process (R, S, S-plus, SAS, Stata, Julia)
• Command-line editing
• Searchable Command history
• Command-line completion of R, S, S-plus object names and file names
• Quick access to object lists and search lists
• Transcript recording
• Interface to the help system
• Transcript manipulation (R, S, S-plus, Stata)
• Recording and saving transcript files
• Manipulating and editing saved transcripts
• Re-evaluating commands from transcript files
• Interaction with Help Pages and other Documentation (R)
• Fast Navigation
• Sending Examples to running ESS process
• Fast Transfer to Further Help Pages
• Help File Editing (R)
• Syntactic indentation and highlighting of source code
• Sending Examples to running ESS process
• Previewing

This metapackage will install Debian Science packages for financial engineering and computational finance.

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이 메타패키지는 수치 계산에 유용한 Debian Science 패키지를 설치할 것입니다. 패키지는 과학 계산 및 데이타 분석을 위한 배열 지향 계산 및 시각화 시스템을 제공합니다. 이 패키지들은 Matlab 및 IDL같은 상용 시스템과 매우 유사합니다.

이 메타패키지는 Debian Pure Blend "Debian Science"의 일부이며 통계와 관련된 패키지를 설치합니다. 이 태스크는 모든 과학 작업에 유용한 일반적인 태스크입니다. 통계 작성은 많은 R 패키지 뿐만 아니라 통계 작성에 유용한 다른 도구에서 결정됩니다. 게다가 과학 수학 작업은 모든 수학 관련 소프트웨어를 추가적으로 설치할 것을 제안합니다.

Dynare is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models. The models solved by Dynare include those relying on the rational expectations hypothesis, wherein agents form their expectations about the future in a way consistent with the model. But Dynare is also able to handle models where expectations are formed differently: on one extreme, models where agents perfectly anticipate the future; on the other extreme, models where agents have limited rationality or imperfect knowledge of the state of the economy and, hence, form their expectations through a learning process. In terms of types of agents, models solved by Dynare can incorporate consumers, productive firms, governments, monetary authorities, investors and financial intermediaries. Some degree of heterogeneity can be achieved by including several distinct classes of agents in each of the aforementioned agent categories.

Dynare offers a user-friendly and intuitive way of describing these models. It is able to perform simulations of the model given a calibration of the model parameters and is also able to estimate these parameters given a dataset. In practice, the user will write a text file containing the list of model variables, the dynamic equations linking these variables together, the computing tasks to be performed and the desired graphical or numerical outputs.

This package is only useful to users having MATLAB installed on their machine. It contains the source of the MEX files and will recompile them using the existing MATLAB installation.

X-13ARIMA-SEATS is a seasonal adjustment software produced, distributed, and maintained by the Census Bureau, U.S. Department of Commerce.

Features include:

• Extensive time series modeling and model selection capabilities for linear regression models with ARIMA errors (regARIMA models);
• The capability to generate ARIMA model-based seasonal adjustment using a version of the SEATS procedure originally developed by Victor Gómez and Agustín Maravall at the Bank of Spain as well as nonparametric adjustments from the X-11 procedure;
• Diagnostics of the quality and stability of the adjustments achieved under the options selected;
• The ability to efficiently process many series at once.

This package ships the version of the program that creates ASCII text file output.

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.