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

Source: R/Optimization.R
Optimization.Rd

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
)

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.

Value

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

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)