MultiATSM (version 0.3.6)

Bias_Correc_VAR: Estimate an unbiased VAR(1) using stochastic approximation (Bauer, Rudebusch and Wu, 2012)

Description

Estimate 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
)

Value

Bias-corrected VAR paramaters 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 set to 5.

Arguments

ModelType

string-vector containing the label of the model to be estimated

BRWinputs

List containing the following necessary inputs for the estimation of the BRW model:

1. flag_mean: flag whether mean- (TRUE) or median- (FALSE) unbiased estimation is desired. Default is set to TRUE;

2. gamma: adjustment parameter. Value parameters should vary between 0 and 1. Default is set to 0.5;

3. N_iter: number of iterations used in the stochatic approximation algorithm after burn-in. Default is set to 5,000;

4. N_burn: number of burn-in iterations used in the stochatic approximation algorithm. Default is set to 0.15*N_iter;

5. B: number of bootstrap samples per iteration to calculate noisy measure of mean/median of the OLS estimator. Default is set to 50;

6. check: flag whether the user wishes to perform the closeness check. Default is set to TRUE;

7. B_check: number of bootstrap samples used in the closeness check. Default is set to 100,000.

RiskFactors

time series of the risk factors (T x F)

N

number of country-specific spanned factors (scalar)

Economies

string-vector containing the names of the economies which are part of the economic system

FactorLabels

string-list based which contains the labels of all variables present in the model

GVARinputs

inputs used in the estimation of the GVAR-based models (see "GVAR" function). Default is set to NULL

JLLinputs

inputs used in the estimation of the JLL-based models (see "JLL" function). Default is set to NULL

ev_restr

largest eigenvalue restriction under the P-measure. Default is set to 1

nargout

number of elements present in the list of outputs. Default is set to 4

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

# \donttest{
data(CM_Factors)
Factors <- t(RiskFactors[1:7,])

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

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


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