MultiATSM (version 1.5.0)

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

Description

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

Usage

Optimization(
  MLEinputs,
  StatQ,
  DataFreq,
  FactorLabels,
  Economies,
  ModelType,
  tol = 1e-04,
  EstType = c("BFGS", "Nelder-Mead"),
  TimeCount = TRUE,
  BS_outputs = FALSE,
  verbose = TRUE
)

Value

An object of class 'ATSMModelOutputs' containing model outputs after the optimization of the chosen ATSM specification.

Arguments

MLEinputs

list. Contains the inputs for building the log-likelihood function (see InputsForOpt).

StatQ

A logical value indicating whether to impose that the largest eigenvalue under Q is strictly smaller than 1. Set TRUE to impose this restriction.

DataFreq

character vector specifying the data frequency. Available options: "Daily All Days", "Daily Business Days", "Weekly", "Monthly", "Quarterly", "Annually".

FactorLabels

list. Labels for all variables present in the model, as returned by LabFac.

Economies

character vector. Names of the C economies included in the system.

ModelType

character. Model type to be estimated. Permissible choices: "JPS original", "JPS global", "GVAR single", "JPS multi", "GVAR multi", "JLL original", "JLL No DomUnit", "JLL joint Sigma".

tol

numeric. Convergence tolerance. The default is 1e-4.

EstType

Available options are"BFGS" and/or "Nelder-Mead".

TimeCount

Logical. If TRUE, computes the time required for model estimation. Default is TRUE.

BS_outputs

Logical. If TRUE, generates a simplified output list in the bootstrap setting. Default is FALSE.

verbose

Logical flag controlling function messaging. Default is TRUE.

Available Methods

- `summary(object)`

References

• Candelon, C. and Moura, R. (2024). “A Multicountry Model of the Term Structures of Interest Rates with a GVAR.” Journal of Financial Econometrics 22 (5): 1558–87.

• Jotikasthira, C; Le, A. and Lundblad, C (2015). “Why Do Term Structures in Different Currencies Co-Move?” Journal of Financial Economics 115: 58–83.

• Joslin, S,; Priebsch, M. and Singleton, K. (2014). “Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks.” Journal of Finance 69 (3): 1197–1233.

• Joslin, S., Singleton, K. and Zhu, H. (2011). "A new perspective on Gaussian dynamic term structure models". The Review of Financial Studies.

• Le, A. and Singleton, K. (2018). "A Small Package of Matlab Routines for the Estimation of Some Term Structure Models." Euro Area Business Cycle Network Training School - Term Structure Modelling.

Examples

LoadData("CM_2024")
ModelType <- "JPS original"
Economy <- "Brazil"
t0 <- "01-05-2007" # Initial Sample Date (Format: "dd-mm-yyyy")
tF <- "01-12-2018" # Final Sample Date (Format: "dd-mm-yyyy")
N <- 1
GlobalVar <- "Gl_Eco_Act" # Global Variables
DomVar <- "Eco_Act" # Domestic Variables
DataFreq <- "Monthly"
StatQ <- FALSE

FacLab <- LabFac(N, DomVar, GlobalVar, Economy, ModelType)
ATSMInputs <- InputsForOpt(t0, tF, ModelType, Yields, GlobalMacro, DomMacro,
  FacLab, Economy, DataFreq,
  CheckInputs = FALSE, verbose = FALSE
)

OptPara <- Optimization(ATSMInputs, StatQ, DataFreq, FacLab, Economy, ModelType, verbose = FALSE)