Bias_Correc_VAR: Estimates an unbiased VAR(1) using stochastic approximation...

In MultiATSM: Multicountry Term Structure of Interest Rates Models

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

Description

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

Usage

Bias_Correc_VAR(
  ModelType,
  BRWinputs,
  RiskFactors,
  N,
  Economies,
  FactorLabels,
  GVARinputs = NULL,
  JLLinputs = NULL,
  ev_restr = 1,
  nargout = 4
)

Arguments

ModelType	A character vector indicating the model type to be estimated.
BRWinputs	A list containing the necessary inputs for the BRW model estimation: flag_mean: Logical. Determines whether mean- (TRUE) or median- (FALSE) unbiased estimation is desired. Default is TRUE. gamma: Numeric. Adjustment parameter between 0 and 1. Default is 0.5. N_iter: Integer. Number of iterations for the stochastic approximation algorithm after burn-in. Default is 5,000. N_burn: Integer. Number of burn-in iterations. Default is 15 B: Integer. Number of bootstrap samples per iteration for calculating the noisy measure of the OLS estimator's mean or median. Default is 50. check: Logical. Indicates whether to perform a closeness check. Default is TRUE. B_check: Integer. Number of bootstrap samples for the closeness check. Default is 100,000.
RiskFactors	A numeric matrix (T x F) representing the time series of risk factors.
N	Integer. Number of country-specific spanned factors.
Economies	A character vector containing the names of the economies included in the system.
FactorLabels	A list of character vectors with labels for all variables in the model.
GVARinputs	List. Inputs for GVAR model estimation (see GVAR function). Default is NULL.
JLLinputs	List. Inputs for JLL model estimation (see JLL function). Default is NULL.
ev_restr	Numeric. Restriction on the largest eigenvalue under the P-measure. Default is 1.
nargout	Integer. Number of elements in the output list. Default is 4.

Value

Bias-corrected VAR parameters based on the framework of Bauer, Rudebusch and Wu (2012). The list contains:

1. Phi_tilde: estimated coefficient matrix (F x F);

2. mu_tilde: estimated intercept (F x 1);

3. V_tilde: estimated variance-covariance matrix (F x F);

4. dist: root mean square distance (scalar);

5. Phi_sample: sample estimated variance-covariance matrix used in the checks (F x F x B_check) - this output is reported if nargout is 5.

References

Bauer, Rudebusch and, Wu (2012). "Correcting Estimation Bias in Dynamic Term Structure Models"
This function is based on the est_unb_var Matlab function available at Cynthia Wu's website (https://sites.google.com/view/jingcynthiawu/).

Examples

data(CM_Factors)
Factors <- t(RiskFactors[1:7,])

BRWinputs <- list(flag_mean = TRUE, gamma = 0.4, N_iter = 1000, N_burn = 100,
                  B = 10, check = 1, B_check = 5000)

Economies <- "China"
N <- 3
ModelType <- "JPS original"
FactorLabels <- NULL

BRWpara <- Bias_Correc_VAR(ModelType, BRWinputs, Factors, N, Economies, FactorLabels)

MultiATSM documentation built on April 4, 2025, 1:40 a.m.