Estimates a GVAR(1) and VARX(1,1,1) models
Arguments
- GVARinputs
list. Inputs for GVAR model estimation:
Economies: character vector. Contains theCnames of the economies included in the system.GVARFactors: list. All variables used in the estimation of the VARX model
(see e.g.GVARFactorsfile for details);VARXtype: Permissible:'unconstrained': model is estimated without constraints (each equation is estimated individually by ordinary least square);'constrained: Spanned Factors': The model is estimated with the restriction that foreign pricing factors do NOT affect (i) domestic economic variables and (ii) domestic pricing factors (estimation via restricted least squares).'constrained : [factor_name]': The model is estimated with the restriction that the specified risk factor is influenced only by its own lagged values and the lagged values of its corresponding star variables. (estimation via restricted least squares.)
Wgvar: The GVAR transition matrix (C x C) used in the model solution.
(See the output from theTransition_Matrixfunction.).
- N
positive integer. Number of country-specific spanned factors.
- CheckInputs
logical. Whether to perform a prior consistency check on the inputs provided in
GVARinputs. Default is FALSE.
Value
list. Contains:
parameters of the country-specific VARX(1,1,1):
intercept (M + N x 1)
phi_1 (M + N x M + N)
phi_1* (M + N x M + N)
phi_g (M + N x M + N)
Sigma (M + N x G)
parameters of the GVAR:
F0 (K x K)
F1 (K x K)
Sigma_y (K x K)
General Notation
C: number of countries in the systemG: number of global unspanned factorsM: number of country-specific unspanned factorsN: number of country-specific spanned factorsK: total number of risk factors (K = C x (N + M) + G)
References
Chudik, A. and Pesaran, M. H. (2016). "Theory and Practice of GVAR modelling" (Journal of Economic Surveys)
Examples
data(GVARFactors)
GVARinputs <- list(
Economies = c("China", "Brazil", "Mexico", "Uruguay"),
GVARFactors = GVARFactors, VARXtype = "unconstrained"
)
GVARinputs$Wgvar <- matrix(c(
0, 0.83, 0.86, 0.38,
0.65, 0, 0.13, 0.55,
0.32, 0.12, 0, 0.07,
0.03, 0.05, 0.01, 0
), nrow = 4, ncol = 4)
N <- 3
GVARPara <- GVAR(GVARinputs, N)