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MultiATSM (version 1.3.0)

Multicountry Term Structure of Interest Rates Models

Description

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) . Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015, JFE) , Candelon and Moura (2023, EM) , and Candelon and Moura (Forthcoming, JFEC) are also available.

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install.packages('MultiATSM')

Monthly Downloads

365

Version

1.3.0

License

GPL-2 | GPL-3

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Repository

https://github.com/rubensmoura87/MultiATSM

Maintainer

Rubens Moura

Last Published

March 24th, 2025

Functions in MultiATSM (1.3.0)

BUnspannedAdapSep

Transform B_spanned into B_unspanned for sepQ models

Boot_DataGraphFact_perShock

Generates the desired bootstrap graphs

BUnspannedAdapJoint

Transform B_spanned into B_unspanned for jointQ models

Replications of the JPS (2014) outputs by Bauer and Rudebusch (2017)

Build Confidence intervals for yield-related outputs

Boot_DataGraphYield_perShock

Generates the desired bootstrap graphs

Obtain the auxiliary values corresponding to each parameter, its size and its name

Build the time-series of bond yields for each bootstrap draw

BootstrapBoundsSet

Builds the confidence bounds and graphs (Bootstrap set)

BuildATSM_RiskFactors

Builds the time series of the risk factors that are used in the estimation of the ATSM

Check consistency of the inputs provided in JLL-based models

Build the list of IRF and GIRF for both factors and bond yields

Check_comparison__OLS

check whether mean/median of OLS is close to actual OLS estimates

DataForEstimation

Retrieves data from Excel and build the database used in the model estimation

Compute the expected component for all models

ComputeBounds_IRFandGIRF

Compute the confidence bounds from the model's numerical outputs

Build country-specific link matrices

BuildRiskFactors_BS

Build the time-series of the risk factors in each bootstrap draw

CleanOrthoJLL_Boot

Clean unnecessary outputs of JLL models in the bootstrap setup

CholRestrictionsJLL

Impose the zero-restrictions on the Cholesky-factorization from JLL-based models.

ComputeBounds_FEVDandGFEVD

Compute the confidence bounds around the P-dynamics and bond yields for FEVD and GFEVD

Convert2JordanForm

Convert a generic matrix to its Jordan form

Boot_Yields_Graphs

Build P-dynamic graphs after the bootstrap implementation

Build the GVAR(1) from the country-specific VARX(1,1,1)

Compute_BnX_AnX

Compute the latent loading AnX and BnX

Generates the bootstrap-related outputs

CheckInputsForMLE

Check consistence of inputs

Boot_graph_template

Builds template from bootstrap-related graphs

Prepare the factor set for GVAR models (Bootstrap version)

ExpectedComponent

Get the expected component of all models

Mean of the llk function used in the estimation of the selected ATSM

EstimationSigma_Ye

Estimate numerically the Cholesky-factorization from the JLL-based models

mean of the llk function used in the estimation of the selected ATSM

FolderCreation_Boot

Creates folder to store graphs generated from the bootstrap analysis

EstimationSigma_GVARrest

Estimate numerically the variance-covariance matrix from the GVAR-based models

Compute the Feedback matrix of each bootstrap draw

FEVDandGFEVDs_Graphs

Generates graphs for FEVDs and GFEVDs

FEVDandGFEVDgraphs

FEVD and GFEVD graphs for all models

CheckInputsGVAR

Check consistency of the inputs provided in GVARinputs

Compute IRFs of all models

Compute GIRFs for all models

Compute the forward premia for all models

FEVDs and GFEVDs for all models

FeedbackMatrixRestrictionsJLL

Set the zero-restrictions on the feedback matrix of JLL's P-dynamics

Data: Risk Factors for the GVAR - Candelon and Moura (2023)

Preliminary checks for inputs provided for the performing out-of-sample forecasting

Gather_Forecasts

Gather several forecast dates

Compute GFEVDs for all models

Compute FEVDs for all models

FEVDandGFEVD_BS

FEVDs and GFEVDs after bootstrap for all models

Check_label_consistency

Check consistency of labels (economies, domestic and global variables)

Build the list of IRF and GIRF for both factors and bond yields

Compute Sigmas/Cholesky factorizations

Collect both the domestic and global unspanned factors of all countries in single matrices

Set up the vector-valued objective function (Point estimate)

Compute quantiles for model P-dynamics

Map auxiliary (unconstrained) parameters a to constrained parameters b

Performs state rotations

GVAR_PrepFactors

Prepare risk factors for the estimation of the GVAR model

Creates the confidence bounds and the graphs of FEVDs and GFEVDs after bootstrap (all models)

FolderPrep_IRFs

Create folders for storing IRFs and GIRFs

GetYields_AllCountries

Gather all country-specific yields in a single matrix of dimension CJ x T

Compute the variance-covariance matrix after the bias correction procedure

Obtain the country-specific a0

Gather data of several countries in a list. Particularly useful for GVAR-based setups (Compute "GVARFactors")

FactorBounds_FEVDandGFEVD

Compute the confidence bounds for the model bond P-dynamics-related outputs

DomesticMacroVar

Data: Risk Factors - Candelon and Moura (2024, JFEC)

Estimates a GVAR(1) and a VARX(1,1,1) models

Generates the labels factors

Constructs the model numerical outputs (model fit, IRFs, GIRFs, FEVDs, GFEVDs, and risk premia decomposition)

Generates forecasts of bond yields for all model types

Extract the indexes related to the spanned factors in the variance-covariance matrix

Obtain the indexes of both the domestic and global unspanned factors

Impose stationarity under the Q-measure

Functionf_vectorized

Use function f to generate the outputs from a ATSM

Data: Risk Factors for the GVAR - Candelon and Moura (2024, JFEC)

Data: Risk Factors - Candelon and Moura (2023)

Compute the A loadings

Compute the B matrix of loadings

NumOutputs_Bootstrap

Numerical outputs (IRFs, GIRFs, FEVD, and GFEVD) for bootstrap

FolderPrep_FEVDs

Create folders for storing IRFs and GIRFs

Generate the variable labels of the JLL models

IDXZeroRestrictionsJLLVarCovOrtho

Find the indexes of zero-restrictions from the orthogonalized variance-covariance matrix from the JLL-based models

Compute the feedback matrix from a GVAR model with global factors

Map constrained parameters b to unconstrained auxiliary parameters a.

Data: Risk Factors - Candelon and Moura (2024, JFEC)

Get_SigmaYields

Compute the variance-covariance matrix of the bond yields

Exclude series that contain NAs

Compute the term premia

Build subplot for fitted yields

Model fit graphs for all models

Reg__OLSconstrained

Restricted OLS regression

Factors_NonOrtho

Makes the pre-allocation of the factors set for JLL-based models

IRFs and GIRFs for all models

Gen_Forecast_Yields

Compute the bond yield forecast for any model type

Impose stationary constraint under the risk-neutral measure

IRFandGIRFgraphs

IRF and GIRF graphs for all models

Loads data sets from several papers

ImposeStat_True

Makes sure that the stationary constraint under the risk-neutral measure is preserved

FactorBounds_IRFandGIRF

Compute the confidence bounds for the model P-dynamics

GraphicalOutputs

Generate the graphical outputs for the selected models (Point estimate)

FolderCreationBoot

Creates the folders and the path in which the graphical outputs are stored (Bootstrap version)

Generate the factor labels for models estimated on a country-by-country basis

FolderCreationPoint

Creates the folders and the path in which the graphical outputs are stored (point estimate version)

GaussianDensity

computes the density function of a gaussian process

Gen_Artificial_Series

Generate artificial time-series in the bootstrap setup

Perform out-of-sample forecast of bond yields

Build the B loadings

Get the intercept, feedback matrix and the variance-covariance matrix from GVAR without global factors

IRFandGIRFs_Format_Fac

Gather data for IRFs and GIRFs grahs (version "Factors")

Compute the parameters used in the P-dynamics of the model

Compute the maximum likelihood function of all models

GeneralMLEInputs

Gathers the general inputs for model estimation

Compute P-dynamics parameters using the bias correction method from BRW (2012)

GetPdynPara_NoBC

Compute P-dynamics parameters without using the bias correction method from BRW (2012)

Generates inputs necessary to build the likelihood function for the ATSM model

Estimate a VARX(1,1,1)

TermPremiaDecomp

Decomposition of yields into the average of expected future short-term interest rate and risk premia for all models

Estimate the risk-neutral feedbak matrix K1Q using linear regressions

Generates the star variables necessary for the GVAR estimation

Compute the log-likelihood function

Compute the root mean square error for all models

Prepare outputs to export after the model optimization

Optimization_PE

Peform the minimization of mean(f)

VarianceExplained

Percentage explained by the spanned factors of the variations in the set of observed yields for all models

Fit yields for all maturities of interest

IRFandGIRFs_Format_Yields

Gather data for IRFs and GIRFs grahs (version "Yields")

Term Premia decomposition graphs for all models

Transformation of the Jordan-related parameters (True form)

Find the indexes of the spanned factors

Transform a number bounded between a lower bound and upper bound to x by:

Transformation of the JLL-related parameters (true form)

Data: Yields - Candelon and Moura (2024, JFEC)

Computes numerical first order derivative of f(x)

Computes two measures of model fit for bond yields (all models)

summary.ATSMModelInputs

Summary method for ATSMModelInputs objects

Compute r0 for the various models

Compute the square root of a matrix

InputsForOutputs

Collects the inputs that are used to construct the numerical and the graphical outputs

summary.ATSMModelOutputs

Summary method for ATSMModelOutputs objects

Adjust delta for numerical differentiation

plot.ATSMModelForecast

Plot method for ATSMModelForecast objects

Generate the labels of the star variables

Optimization_Time

Compute the time elapsed in the numerical optimization

Generate the labels of the spanned factors

Obtain the non-orthogonalized model parameters

Transform a positive number y to back to x by:

Transform x to a number bounded btw lb and ub by:

Create a vector of numerical maturities in years

Creates the confidence bounds and the graphs of IRFs and GIRFs after bootstrap

IRFs and GIRFs after bootstrap for all models

Spanned_Factors

Computes the country-specific spanned factors

Perform the optimization of the log-likelihood function of the chosen ATSM

VAR(1) with orthogonalized factors (JLL models)

OptimizationSetup_ATSM

Optimization routine for the entire selected ATSM

Create the variable labels used in the estimation

Get coefficients from the orthogonalized regressions

Replications of the JPS (2014) outputs by the MultiATSM package

ParaATSM_opt_ALL

Update the list of parameters

Outputs2exportMLE

Prepares inputs to export

Data: Trade Flows - Candelon and Moura (2024, JFEC)

TimeVarWeights_GVAR

Compute the star variables with time-varying weights

Wished_Graphs_FEVDandGFEVD

Extract list of desired graph features (IRFs anc GIRFs)

Transformation of a PSD matrix (true form)

Transformation of the bounded parameters (True form)

SpecificMLEInputs

Concatenate the model-specific inputs in a list

ResampleResiduals_BS

Compute the residuals from the original model

Compute some key parameters from the P-dynamics (Bootstrap set)

Transition_Matrix

Computes the transition matrix required in the estimation of the GVAR model

Update_ParaList

converts the vectorized auxiliary parameter vector x to the parameters that go directly into the likelihood function.

Transformation of the block diagonal parameters (true form)

Check for JLL models for Jordan restrictions (auxiliary form)

Data: Risk Factors - Candelon and Moura (2024, JFEC)

Update the variance-covariance matrix from the "JLL joint Sigma" model. Necessary for optimization

Estimates the P-dynamics from JLL-based models

Estimates a standard VAR(1)

Data: Trade Flows - Candelon and Moura (2023)

YieldBounds_FEVDandGFEVD

Compute the confidence bounds for the model bond yield-related outputs

Model-implied yields (cross-section)

Wished_Graphs_IRFandGIRF

Extract list of desired graph features (IRFs anc GIRFs)

Estimate a VAR(1) - suited to Bauer, Rudebusch and Wu (2012) methodology

print.ATSMModelInputs

Print method for ATSMModelInputs objects

YieldBounds_IRFandGIRF

Compute the confidence bounds for the model bond yield-related outputs

Find mean or median of OLS when DGP is VAR(1)

Generate M data sets from VAR(1) model

Model-implied yields (P-dynamics)

Transform x to a positive number by: y = log(e^x + 1)

Build the log-likelihood function of the P-dynamics from the JLL-based models

Compute risk-neutral intercept and slope

Killan's VAR stationarity adjustment

residY_original

Compute the residuals from the observational equation

MarginalModelPara

Estimate the marginal model for the global factors

pca_weights_one_country

Computes the PCA weights for a single country

YieldQuantile_bs

Compute quantiles for model bond yield-related outputs

RiskFactorsPrep

Builds the complete set of time series of the risk factors (spanned and unspanned)

Compile the bond yield forecast for any model type

Efficient computation of matrix product for arrays

OutputConstruction

Numerical outputs (variance explained, model fit, IRFs, GIRFs, FEVDs, GFEVDs, and risk premia decomposition) for all models

Complete list of several outputs from an ATSM

Adjust vector of maturities

RiskFactorsGraphs

Spanned and unspanned factors plot

WishGraphs_IRFandGIRF_Boot

Extract graphs of interest (bootstrap version)

WishGraphs_FEVDandGFEVD_Boot

Extract graphs of interest (bootstrap version)

AdjustPathFEVDs

Generate paths to save IRFs/GIRFs graphs

Gathers the estimate of the bootstrap draws

Compute the cross-section loadings of yields of a canonical A0_N model

Boot_Fac_Graphs

Build P-dynamic graphs after the bootstrap implementation

Transformation of a PSD matrix (auxiliary form)

Generate paths to save IRFs/GIRFs graphs

Transformation of the bounded parameters (auxiliary form)

AdjustYieldsDates

Makes sure that the time series of yields and risk factors have coincident sample spans

Transformation of the JLL-related parameters (auxiliary form)

Transformation of the Jordan-related parameters (auxiliary form)

Adjust_Const_Type

Adjust the constant label

Transformation of the block diagonal parameters (auxiliary form)

BUnspannedAdapSep_BS

Obtain the full form of B unspanned for "sep Q" models within the bootstrap setting

Bias_Correc_VAR

Estimates an unbiased VAR(1) using stochastic approximation (Bauer, Rudebusch and Wu, 2012)