Debian Science Economics packages

Dynare er en programplatform til håndtering af en bred mængde af økonomiske modeller, specielt dynamisk stokastisk generel ligevægt (DSGE) og modeller for overlappende generationer (OLG). Modellerne i Dynare inkluderer dem der afhænger af den rationelle forventningshypotese, hvor agenter former deres forventninger om fremtiden på en måde der svarer til modellen. Men Dynare er også i stand til at håndtere modeller hvor forventninger formes på en anden måde: fra den ene yderlighed, modeller hvor agenter perfekt forudser fremtiden; til den anden yderlighed, modeller hvor agenter har begrænset rationalitet eller begrænset viden om økonomiens tilstand og derfor former deres forventninger via en læringsproces. I form af agenttyper, kan modeller løst af Dynare indarbejde kunder, produktive firmaer, regeringer, monetære autoriteter, investorer og finansielle institutioner. En grad af heterogenitet kan opnås ved at inkludere flere distinkte agentklasse i hver af de førnævnte agentkategorier.

Dynare tilbyder en brugervenlig og intuitiv måde at beskrive disse modeller på. Programmet kan udføre simulationer for en model givet en kalibrering af modellens parametre og kan også estimere disse parametre givet et datasæt. I praksis vil brugeren skrive en tekstfil der indeholder listen med modelvariabler, de dynamiske ligninger der lænker disse variabler sammen, beregningsopgaven der skal udføres og de ønskede grafiske eller numeriske resultater.

Denne pakke tilbyder en fuld installation af Dynare, som køres oven på GNU Octave.

Biblioteket for GNU Regression, økonometri (Econometric) og tidsserier (gretl) er en programpakke for økonometriske analyser. Pakken indeholder et delt bibliotek, et klientprogram for kommandolinjen og en grafisk klient bygget med brug af GTK+.

Denne pakke tilbyder GTK+-klienten og klienten for kommandolinjen.

Denne pakke tilbyder funktioner til at arbejde med økonometri i Octave, et numerisk beregningsprogram. Funktionerne inkluderer metoder til at udføre maksimal sandsynlighed (mle_estimate) og generelle metoder for momenters (gmm_estimate) baserede estimater.

Denne Octave-udvidelsespakke er en del af projektet Octave-Forge.

Statsmodels Python 3-modul tilbyder klasser og funktioner for estimering af flere kategorier af statistiske modeller. Disse inkluderer i øjeblikket lineære regressionsmodeller, OLS, GLS, WLS og GLS med AR(p)-fejl, generaliserede lineære modeller for flere distributionsfamilier og M-estimatorer for robuste lineære modeller. En omfattende liste over resultatstatistik er tilgængelig for hvert estimeringsproblem.

R er et system til statistisk beregning og grafik. Det består af et sprog plus et afviklingsmiljø med grafik, et aflusningsredskab, adgang til særlige systemfunktioner, samt evnen til at køre programmer som er gemt som skriptfiler.

Designet af R er tungt påvirket af to eksisterende sprog: Becker, Chambers & Wilks' S og Sussman's Scheme. Hvor sprogudformningen i sin fremtræden i høj grad ligner S, så er den underliggende implementering og semantik afledt fra Scheme.

Kernen i R er et fortolket programmeringssprog som tillader forgrening og løkker, så vel som modulær programmering med brug af funktioner. De fleste funktioner som er synlige for brugeren i R er skrevet i R. Det er muligt for brugeren at tilgå procedurer skrevet i C, C++ eller FORTRAN sprogene for effektivitet, og mange af R's kernefunktioner gør dette. Distributionen R indeholder funktionalitet til et stort antal statistiske procedurer og underliggende anvendte matematiske beregninger. Der er også et stort sæt af funktioner, der tilbyder et grafisk miljø til oprettelse af adskillige slags datarepræsentationer.

Derudover er flere tusinde udvidelsespakker tilgængelige fra CRAN (Comprehensive R Archive Network), mange også som Debianpakker, der er navngivet »r-cran-«.

Disse pakker er en metapakke, som letter overgangen fra pre-1.5.0-pakkestrukturen med dennes store r-base-pakke. Så snart den er installeret, kan den uden videre fjernes og apt-get vil automatisk opgradere dens komponenter under fremtidige opgraderinger. Denne pakke gør det muligt for brugerne kun at installere r-base-core, hvis de ønsker det.

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.)

Pakken bayesm dækker mange vigtige modeller, der anvendes i markedsføring og mikro-økonometri applikationer. Pakken indeholder:

• Bayes-regression (univariate eller multivariate dep var)
• Multinomial Logit (MNL) og Multinomial Probit (MNP)
• Multivariate Probit,
• Multivariate blandinger af Normals
• Hierarkisk lineære modeller med normale priors og kovariater
• Hierarkisk Multinomial Logits med blanding af normale priors og kovariater
• Bayesiansk analyse af valg-baserede conjoint data
• Bayesian behandling af lineære instrumentale variable modeller
• Analyis af Multivariate Ordinal-undersøgelsesdata med skalaforbrugs- heterogenitet (som i Rossi et al, JASA (01)).

For yderligere reference, se forfatternes bog, Bayesian Statistics And Marketing af Allenby, McCulloch og 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).

Denne pakke tilbyder funktioner til at importere finansielle og økonomiske dataserier og er en del af Rmetrics, en samling af pakker for finansiel videnskab og beregningsfinans skrevet og kompileret af Diethelm Wuertz og andre.

fImport tilbyder en importfunktion til at tilgå (frie) data fra Economagic, US Federal Reserve, Forecasts.Org, Yahoo og andre internetressourcer.

Denne pakke tilbyder funktioner for modellering af ikkelineære tidsserier og er en del af Rmetrics, en samling af pakker for finansiel videnskab og beregningsfinans skrevet og kompileret af Diethelm Wuertz samt andre.

fNonliear tilbyder ikkelineære funktioner for tidsseriemodellering.

Denne pakke tilbyder funktioner for læsning og skrivning af data lagret af statistiske pakker såsom Minitab, S, SAS, SPSS, Stata ...

Denne pakke er del af et sæt af pakker, som »anbefales« af R Core, og leveres med kildeudgivelser via opstrøm af R.

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 ).

Biblioteket Hmisc indeholder mange funktioner nyttige for dataanalyse, grafik på højt niveau, redskabshandlinger, funktioner for beregning af teststørrelse og kraft, oversættelse af SAS-datasæt, indsættelse af manglende værdier, avanceret tabeludarbejdelse, variable klyngeopbygning, manipulering af tegnstreng, konvertering af S-objekter til LaTeX-kode, ny kodning af variabler og bootstrap-gentaget måleanalyse.

Denne R-pakke tilbyder ISO 639-sprogkoder, ISO 3166-territoriekoder, ISO 4217-valutakoder, ISO 15924-skriptkoder og ISO 8859-tegnkoder samt UN M.49-områdekoder.

Denne CRAN-pakke tilbyder S4-klasser og metoder for tilpasning og undersøgelse af lineære blandede effektmodeller (også kaldt for modeller med flere niveauer, paneldatamodeller og flere andre navne) og generaliserede lineære blandede effektmodeller.

Dette er et sæt af rutiner for GNU R, som implementerer diverse statistiske og økonometriske modeller, der bruger Markov-kæde Monte Carlo-estimering (MCMC), som giver mulighed for at »løse« modeller, som ellers ville være umedgørlige med traditionelle teknikker, specielt problemer indenfor bayesiansk statistik (hvor en eller flere »priors« bruges som en del af estimeringsproceduren, i stedet for en antagelse om uvidenhed om »sande« punktskøn), skønt MCMC også kan anvendes til at løse statistiske problemer indenfor hyppighed med uinformative »priors«. MCMC-teknikker foretrækkes også over direkte estimering, når der er manglende data.

I øjeblikket er et antal økologiske inferensrutiner (EI) implementeret (for estimering af attributter eller opførsel på individniveau fra opsamlede data, såsom valgte afkast eller optællingsresultater) samt som modeller for traditionelle lineære panel- og tværsnitsdata, nogle visualiseringsrutiner for EI-diagnostik, to post-response (eller ideal-punkts estimering) teorimodeller, metrisk, ordenstal, og blandet respons faktoranalyse, og modeller for Gaussian- (lineær) og Poisson-regression, logistisk regression (eller logit), og binære og ordinal-svar probit-modeller.

De foreslåede pakker (r-cran-bayesm, -eco og -mnp) indeholder yderligere modeller, som kan være nyttige for dem interesseret i denne pakke.

Denne pakke implementerer flere tidsseriefiltre nyttige for udjævning og udtrækning af trend og cykliske komponenter for en tidsserie. Rutinerne bruges ofte indenfor økonomi og finans, de kan dog også være af interesse fro andre områder. I øjeblikket er Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth og trigonometriske regressionsfiltre inkluderet i pakken.

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…).

Denne pakke indeholder et sæt klasser og metoder til at læse data- og metadata-dokumenter udvekslet gennem Statistical Data and Metadata Exchange -rammen (SDMX), der i øjeblikket fokuserer på SDMX XML-standardformatet (SDMX-ML).

SDMX er et initiativ til at fremme standarder for udveksling af statistiske oplysninger. Det er sponsoreret af flere store udbydere af statistiske oplysninger: Bank for International Settlements, European Central Bank, Eurostat (Den Europæiske Unions statistiske kontor), den internationale valutafond (IMF), organisationen for økonomisk samarbejde og udvikling (OECD), De Forenede Nationers statistiske afdeling, De Forenede Nationers organisation for uddannelse, videnskab og kultur og Verdensbanken.

Pakken kan derfor bruges til at hente statistisk information fra serverne hos disse organisationer og fra flere andre institutioners servere.

Denne CRAN-pakke tilbyder yderligere analysefunktioner for tidsserier, samt flere beregningsrutiner for finans.

Denne pakke tilbyder funktioner for enhedsrod- og samintegrationsanalyser udbredte i anvendte tidsserier/økonometri.

Søg og hent data fra mere end 40 databaser hos Verdensbanken, inklusive World Development Indicators (WDI), International Debt Statistics, Doing Business, Human Capital Index og Sub-national Poverty indicators.

Bemærk at pakken ikke indeholder dataene. I stedet tilbydes et sæt af funktioner til at hente dataene fra Verdensbankens hjemmeside.

»Emacs Speaks Statistics« (ESS) er en udvidelsespakke for GNU Emacs. Pakken er designet til at understøtte redigering af skripter og interaktion med diverse statistiske analyseprogrammer såsom R, S, S-Plus, SAS, Stata, BUGS/JAGS og Julia.

Der tilbydes følgende funktioner:

• Redigering af kildekode (R, S, S-plus, SAS, BUGS/JAGS, Stata, Julia)
• Syntaksindrykning og fremhævelse af kildekode
• Delvis evaluering af kode
• Indlæsning og fejlkontrol af kode
• Revisionsvedligeholdelse af kildekode
• Jobafvikling (SAS, BUGS/JAGS)
• Brug af imenu til at tilbyder henvisninger til passende funktioner
• Interaktion med processen (R, S, S-plus, SAS, Stata, Julia)
• Redigering på kommandolinjen
• Kommandohistorik der kan søges i
• Fuldførelse af kommandolinjen for R-, S-, S-plus-objektnavne og filnavne
• Hurtig adgang til objektlister og søgelister
• Udskriftsoptagelse
• Grænseflade til hjælpesystemet
• Udskriftsmanipulation (R, S, S-plus, Stata)
• Optag og gem udskriftsfiler
• Manipuler og rediger gemte udskrifter
• Evaluer kommandoer på ny fra udskriftsfiler
• Interaktion med hjælpesider og anden dokumentation (R)
• Hurtig navigation
• Sende eksempler til kørende ESS-proces
• Hurtig overførsel til yderligere hjælpesider
• Redigering af hjælpefil (R)
• Syntaksindrykning og fremhævelse af kildekode
• Sende eksempler til kørende ESS-proces
• Forhåndsvisning

Denne metapakke vil installere Debian Science-pakker for finansiel ingeniørskab og beregningsfinans.

Denne metapakke vil installere Debians videnskabelige pakker relateret til matematik. Du vil måske også være interesseret i field::mathematics debtag og, afhængig af dit fokusområde, i education-mathematics-metapakken.

Denne metapakke vil installere Debian Science-pakker nyttige til numerisk beregning. Pakkerne tilbyder en tabelorienteret beregning og et visualiseringssystem til videnskabelig beregning og dataanalyse. Disse pakker svarer til kommercielle systemer såsom Matlab og IDL.

Denne metapakke vil installere Debian Science-pakker nyttige for humanistiske videnskaber.

Denne metapakke er en del af Debians Pure Blend »Debian Science« og installerer pakker relateret til statistik. Denne opgave er en generel opgave, som kan være nyttig for videnskabelig arbejde. Den afhænger af en masse R-pakker samt andre værktøjer, som er nyttige til at udføre statistik. Derudover foreslås Videnskabelig matematik-opgaven som valgfri installation af alle matematikrelaterede programmer.

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.