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MultiATSM (version 1.1.0)

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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Version

Install

install.packages('MultiATSM')

Monthly Downloads

324

Version

1.1.0

License

GPL-2 | GPL-3

Maintainer

Rubens Moura

Last Published

December 16th, 2024

Functions in MultiATSM (1.1.0)

Bias_Correc_VAR

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

BUnspannedAdapJoint

Transform B_spanned into B_unspanned for jointQ models

BUnspannedAdapSep

Transform B_spanned into B_unspanned for sepQ models

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

Generates the bootstrap-related outputs

Check consistency of the inputs provided in JLL-based models

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

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

Prepare the factor set for GVAR models (Bootstrap version)

CheckInputsGVAR

Check consistency of the inputs provided in GVARinputs

BuildRiskFactors_BS

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

BUnspannedAdapSep_BS

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

CheckInputsForMLE

Check consistence of inputs

Build country-specific link matrices

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

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

Compute_BnX_AnX

Compute the latent loading AnX and BnX

FEVDandGFEVDbs_jointQ_Ortho

Creates the confidence bounds and the graphs of FEVDs and GFEVDs after bootstrap (JLL-based models)

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

Convert2JordanForm

Convert a generic matrix to its Jordan form

FEVDandGFEVDbs_sepQ

Creates the confidence bounds and the graphs of FEVDs and GFEVDs after bootstrap ("sep Q" models)

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

Compute IRFs of all models

FEVDgraphsJLLOrtho

FEVDs graphs for orthogonalized risk factors of JLL-based models

Check_label_consistency

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

FEVDs graphs for ("sep Q" models)

Compute GIRFs for all models

FEVDs for "joint Q" models

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

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

GFEVDs after bootstrap for "sep Q" models

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

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

EstimationSigma_GVARrest

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

FEVDgraphsJoint

FEVDs graphs for ("joint Q" models)

Performs state rotations

DomesticMacroVar

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

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

Generates forecasts of bond yields for all model types

FolderPrep_IRFs

Create folders for storing IRFs and GIRFs

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

FEVDs after bootstrap for "sep Q" models

CholRestrictionsJLL

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

Factors_NonOrtho

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

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

FEVDs after bootstrap for "joint Q" models

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

Functionf_vectorized

Use function f to generate the outputs from a ATSM

GFEVDgraphsJoint

GFEVDs graphs for "joint Q" models

GFEVDjointOrthoJLL_BS

GFEVDs after bootstrap for JLL-based models

FeedbackMatrixRestrictionsJLL

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

GFEVDgraphsJLLOrtho

GFEVDs graphs for orthogonalized risk factors of JLL-based models

EstimationSigma_Ye

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

CleanOrthoJLL_Boot

Clean unnecessary outputs of JLL models in the bootstrap setup

FEVDs for "sep Q" models

Compute the Feedback matrix of each bootstrap draw

GFEVDjointOrthoJLL

Orthogonalized GFEVDs for JLL models

Gen_Forecast_Yields

compute the bond yield forecast for any model type

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

FolderCreationBoot

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

GetPdynPara_NoBC

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

GFEVDs graphs for ("sep Q" models)

Map constrained parameters b to unconstrained auxiliary parameters a.

FolderCreationPoint

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

GFEVDs for "joint Q" models

Map auxiliary (unconstrained) parameters a to constrained parameters b

GetYields_AllCountries

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

Gather_Forecasts

Gather several forecast dates

GVAR_PrepFactors

Prepare risk factors for the estimation of the GVAR model

Get_SigmaYields

Compute the variance-covariance matrix of the bond yields

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

Generate the variable labels of the JLL models

FEVDandGFEVDbs_jointQ

Creates the confidence bounds and the graphs of FEVDs and GFEVDs after bootstrap ("joint Q" models)

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

BUild the B loadings

IRFandGIRFgraphs

IRFs graphs for all models

FEVDjointOrthogoJLL

Orthogonalized FEVDs for JLL models

Compute the A loadings

IRFandGIRFs_Format_Fac

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

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

FEVDjointOrthogoJLL_BS

FEVDs after bootstrap for JLL-based models

GeneralMLEInputs

Gathers the general inputs for model estimation

Compute the B matrix of loadings

Model fit graphs for ("sep Q" models)

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

Build subplot for fitted yields

Obtain the indexes of both the domestic and global unspanned factors

GFEVDs for "sep Q" models

GFEVDs after bootstrap for "joint Q" models

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

Compute the variance-covariance matrix after the bias correction procedure

Compute the log-likelihood function

Compute r0 for the various models

GaussianDensity

computes the density function of a gaussian process

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

Estimates the P-dynamics from JLL-based models

Gen_Artificial_Series

Generate artificial time-series in the bootstrap setup

Compute the feedback matrix from a GVAR model with global factors

GraphicalOutputs

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

IRFandGIRFs_Format_Yields

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

Obtain the country-specific a0

Compute Sigmas/Cholesky factorizations

IRFs and GIRFs for all models

IDXZeroRestrictionsJLLVarCovOrtho

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

Generates several inputs that are necessary to build the likelihood function

IRFs and GIRFs after bootstrap for all models

IRFandGIRFbs_jointQ

Creates the confidence bounds and the graphs of IRFs and GIRFs after bootstrap ("joint Q" models)

Find the indexes of the spanned factors

Check for JLL models for Jordan restrictions (auxiliary form)

Data: Risk Factors - Candelon and Moura (2023)

InputsForOutputs

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

Generate the labels of the spanned factors

Compute the root mean square error ("joint Q" models)

Generate the labels of the star variables

Get coefficients from the orthogonalized regressions

OutputConstructionJoint

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

OutputConstructionSep_BS

Gathers all the model numerical ouputs after bootstrap for "sep Q" models

OutputConstructionJoint_BS

Gathers all the model numerical ouputs after bootstrap for "joint Q" models

OutputConstructionSep

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

Compute the root mean square error ("sep Q" models)

VAR(1) with orthogonalized factors (JLL models)

SpannedFactorsSepQ

Gather all spanned factors ("sep Q" models)

Obtain the non-orthogonalized model parameters

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

NumOutputs_Bootstrap

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

SpannedFactorsjointQ

Gather all spanned factors ("joint Q" models)

Transformation of the block diagonal parameters (true form)

ResampleResiduals_BS

Compute the residuals from the original model

IRFandGIRFbs_jointQ_Ortho

Creates the confidence bounds and the graphs of IRFs and GIRFs after bootstrap (JLL-based models)

Generates the labels factors

Create a vector of numerical maturities in years

Impose stationarity under the Q-measure

Perform out-of-sample forecast of bond yields

Transformation of the bounded parameters (True form)

MarginalModelPara

Estimate the marginal model for the global factors

TPDecompGraphSep

Term Premia decomposition graphs for "joint Q" models

Spanned_Factors

Computes the country-specific spanned factors

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

SpecificMLEInputs

Concatenate the model-specific inputs in a list

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

Check whether one element is a subset of another element

IRFandGIRFbs_sepQ

Creates the confidence bounds and the graphs of IRFs and GIRFs after bootstrap ("sep Q" models)

Prepare outputs to export after the model optimization

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

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

computes the logarithm of determinant of a matrix A

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

TermPremiaDecompJoint

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

Create the variable labels used in the estimation

Estimate the risk-neutral feedbak matrix K1Q using linear regressions

Impose stationary constraint under the risk-neutral measure

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

Reg__OLSconstrained

Restricted OLS regression

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

Generates the star variables necessary for the GVAR estimation

Wished_Graphs_IRFandGIRF

Extract list of desired graph features (IRFs anc GIRFs)

TPDecompGraphJoint

Term Premia decomposition graphs for "joint Q" models

RiskFactorsGraphs

Spanned and unspanned factors plot

Transformation of the JLL-related parameters (true form)

ImposeStat_True

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

TermPremiaDecompSep

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

Estimate a VARX(1,1,1)

RiskFactorsPrep

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

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

Exclude series that contain NAs

Model-implied yields (cross-section)

Transformation of the Jordan-related parameters (True form)

Eliminates the @

VarianceExplained

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

Compute the maximum likelihood function of all models

Loads data sets from several papers

Compile the bond yield forecast for any model type

Model-implied yields (P-dynamics)

Update_ParaList

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

Efficient computation of matrix product for arrays

OptimizationSetup_ATSM

Optimization routine for the entire selected ATSM

Transformation of a PSD matrix (true form)

Optimization_PE

Peform the minimization of mean(f)

Outputs2exportMLE

Prepares inputs to export

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

ParaATSM_opt_ALL

Update the list of parameters

Transform a positive number y to back to x by:

Inverse each 2D slice of an array (M) with arbitrary dimensions support

YieldsFitAllSep

Fit yields for all maturities of interest

Inverts an array of matrices so that: inva[,,i] = inv(a[,,i])

YieldsFitAllJoint

Fit yields for all maturities of interest

pca_weights_one_country

Weight matrix from principal components

multiprod_2terms

computes matrix product for arrays a and b: c[,,i] = a[,,i] b[,,i]

residY_original

Compute the residuals from the observational equation

Transition_Matrix

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

Data: Trade Flows - Candelon and Moura (2023)

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

Estimates a standard VAR(1)

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

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

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

Computes numerical first order derivative of f(x)

Compute the square root of a matrix

Extract the parameter values from the parameter list set

Killan's VAR stationarity adjustment

Generate M data sets from VAR(1) model

Inverse the (m,m,T) array of matrices for m<=4

Inverse each 2D slice of an array (M) with arbitrary dimensions support

Transformation of the bounded parameters (auxiliary form)

Transformation of the JLL-related parameters (auxiliary form)

Adjust_Const_Type

Adjust the constant label

Transformation of the block diagonal parameters (auxiliary form)

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

BootstrapBoundsSet

Builds the confidence bounds and graphs (Bootstrap set)

Gathers the estimate of the bootstrap draws

Generate paths to save IRFs/GIRFs graphs

AdjustYieldsDates

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

BuildATSM_RiskFactors

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

Transformation of the Jordan-related parameters (auxiliary form)

Transformation of a PSD matrix (auxiliary form)