Package: MultiATSM 1.0.0

MultiATSM: Multicountry Term Structure of Interest Rates Models

Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (Forthcoming, JFEC) <doi:10.1093/jjfinec/nbae008> are also available.

Authors:Rubens Moura [aut, cre]

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MultiATSM.pdf |MultiATSM.html✨
MultiATSM/json (API)

# Install 'MultiATSM' in R:
install.packages('MultiATSM', repos = c('https://rubensmoura87.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • BR_jps_out - Replications of the JPS
  • DomMacro - Data: Risk Factors for the GVAR - Candelon and Moura
  • DomesticMacroVar - Data: Risk Factors - Candelon and Moura
  • FactorsGVAR - Data: Risk Factors for the GVAR - Candelon and Moura
  • GlobalMacro - Data: Risk Factors - Candelon and Moura
  • GlobalMacroVar - Data: Risk Factors - Candelon and Moura
  • ModelPara - Replications of the JPS (2014) outputs by the MultiATSM package
  • RiskFactors - Data: Risk Factors - Candelon and Moura
  • RiskFactors - Data: Risk Factors - Candelon and Moura
  • TradeFlows - Data: Trade Flows - Candelon and Moura
  • Trade_Flows - Data: Trade Flows - Candelon and Moura
  • Yields - Data: Yields - Candelon and Moura
  • Yields - Data: Yields - Candelon and Moura

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.78 score 8 scripts 521 downloads 18 exports 39 dependencies

Last updated 1 months agofrom:4c73b921d1. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:Bias_Correc_VARBootstrapDatabasePrepDataForEstimationForecastYieldsGVARInputsForOptInputsForOutputsJLLLabFacLoadDataNumOutputsOptimizationpca_weights_one_countrySpanned_FactorsStarFactorsTransition_MatrixVAR

Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtablehablarisobandlabelinglatticelifecyclelubridatemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpracmapurrrR6RColorBrewerrlangscalestibbletidyselecttimechangeutf8vctrsviridisLitewithrwraprzoo

MultiATSM package - General Guidelines

Rubens Moura

Rendered fromGuidelines.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2024-10-15
Started: 2024-10-15

Paper Replications

Rubens Moura

Rendered fromPaper-Replications.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2024-10-15
Started: 2023-08-16

Readme and manuals

Help Manual

Help pageTopics
Estimates an unbiased VAR(1) using stochastic approximation (Bauer, Rudebusch and Wu, 2012)Bias_Correc_VAR
Generates the bootstrap-related outputsBootstrap
Replications of the JPS (2014) outputs by Bauer and Rudebusch (2017)BR_jps_out
Gather data of several countries in a list. Particularly useful for GVAR-based setups (Compute "GVARFactors")DatabasePrep
Retrieves data from Excel and build the database used in the model estimationDataForEstimation
Data: Risk Factors - Candelon and Moura (forthcoming, JFEC)DomesticMacroVar
Data: Risk Factors for the GVAR - Candelon and Moura (2023)DomMacro
Data: Risk Factors for the GVAR - Candelon and Moura (forthcoming, JFEC)FactorsGVAR
Generates forecasts of bond yields for all model typesForecastYields
Data: Risk Factors - Candelon and Moura (2023)GlobalMacro
Data: Risk Factors - Candelon and Moura (forthcoming, JFEC)GlobalMacroVar
Estimates a GVAR(1) and a VARX(1,1,1) modelsGVAR
Generates several inputs that are necessary to build the likelihood functionInputsForOpt
Collects the inputs that are used to construct the numerical and the graphical outputsInputsForOutputs
Estimates the P-dynamics from JLL-based modelsJLL
Generates the labels factorsLabFac
Loads data sets from several papersLoadData
Replications of the JPS (2014) outputs by the MultiATSM packageModelPara
ATSM PackageMultiATSM-package MultiATSM
Constructs the model numerical outputs (model fit, IRFs, GIRFs, FEVDs, GFEVDs, and risk premia decomposition)NumOutputs
Perform the optimization of the log-likelihood function of the chosen ATSMOptimization
Weight matrix from principal componentspca_weights_one_country
Data: Risk Factors - Candelon and Moura (forthcoming, JFEC)RiskFactors
Computes the country-specific spanned factorsSpanned_Factors
Generates the star variables necessary for the GVAR estimationStarFactors
Data: Trade Flows - Candelon and Moura (2023)Trade_Flows
Data: Trade Flows - Candelon and Moura (forthcoming, JFEC)TradeFlows
Computes the transition matrix required in the estimation of the GVAR modelTransition_Matrix
Estimates a standard VAR(1)VAR
Data: Yields - Candelon and Moura (forthcoming, JFEC)Yields