GVARX (version 1.2)

GVAR_Xt: Compute the G0, G1, G2, and F1, F2 matrices for filtering Xt

Description

Compute the G0, G1, G2, and F1, F2 matrices for filtering Xt

Usage

GVAR_Xt(data,p, type="const",ic="AIC",weight.matrix)

Arguments

data

Dataframe is a strictly balanced panel data format,the first column is cross-section ID,and the second column is Time. For the sake of identification, both columns must be named by, respectively, id and Time.

p

The number of lag for Xt matrix. The number of lag for foreign variables in country-specific VAR FLag is set to be p+1.

type

Model specificaiton for VAR. As in package vars, we have four selection: "none","const","trend", "both".

ic

Information criteria for optimal lag.As in package vars, we have four selection: "AIC", "HQ", "SC", "FPE".

weight.matrix

Bilateral trade weight matrix for computing foreign variables. If the computation of foreign variables are weighted by one weighting matrix, weight.matrix must be a "data.frame". If the computation of foreign variables are weighted on a year-to-year basis, then weight.matrix must be a "list", with the same length as the weighting frequency.

Value

G0

Matrix G0 of Eq.(2.6) in Filippo and Pesaran(2013, P.17)

G1

Matrix G1 of Eq.(2.6) in Filippo and Pesaran(2013, P.17)

G2

Matrix G2 of Eq.(2.6) in Filippo and Pesaran(2013, P.17)

F1

Matrix F1 of Eq.(2.6) in Filippo and Pesaran(2013, P.17)

F2

Matrix F2 of Eq.(2.6) in Filippo and Pesaran(2013, P.17)

lagmatrix

Country-secific optimal lag number, which must be the same.

RESID

original residuals=u in Filippo and Pesaran (2013, P.17)

newRESID

New residuals=epsilon in Filippo and Pesaran (2013, P.17)

Details

This function generates several matrices of Eq.(2.6) in Filippo and Pesaran(2013, P.17), which is resuired to recursively filter Xt; besides, it also re-calculates the transformed residuals. In this version, we do not include the impulse responses function(IRF), because the IRF can be computed by these matrices and residuals easily. We will not update it until the next version.

References

Mauro Filippo di and Pesaran H. M. (2013) The GVAR Handbook-- Structure and Applications of a Macro Model of the Global Economy for Policy. Oxford University Press.

Examples

# NOT RUN {
data("PriceVol")
data("tradeweightx")
data("tradeweight1")
p=2
type="const"
ic="SC"

Result=GVAR_Xt(data=PriceVol,p,type,ic, weight.matrix=tradeweight1)
Result$G0
Result$G1
Result$G2
Result$F1
Result$F2
Result$lagmatrix
Result$RESID
Result$newRESID

# }