GVARest {GVARX}

Estimate country-specific VAR in a GVAR setting

Description

Estimate country-specific VAR in a GVAR setting

Usage

GVARest(data,p,lag.max, type="const", ic,weight.matrix=NULL)

Arguments

data

Dataframe for bivariate VAR is allowed so far, which is also 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. Restriction of bivariate VAR will be relaxed soon.

p

The number of lag for Xt matrix, foreign variables are set by FLag=p+1. Current version restricts p <= 2 with a view to avoiding too many paramaters in low-frequency data of many variables and many countries. It will be relaxed soon.

lag.max

The maximal number of lag for estimating country-specific VAR

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", and "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

gvar

Country-specific GVAR output list

White

Coefficient estimates with White robust covariance

NWHAC

Coefficient estimates withNewy-West robust covariance

p

Number of lags for endogeneous variables in VAR

K

Number of lags for Ft variables in VAR

type

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

datamat

input data=data

lagmatrix

GVAR's Country-secific optimal lag number.

lagmatrix1

VAR's Country-secific optimal lag number.

exoLag

Ft lags

Ft

Foreign variables

NAMES

Names of countries

gvarRSD

Country-specific GVAR residuals

varRSD

VAR residuals

weight

weight.matrix

Author(s)

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

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

data("PriceVol")
data("tradeweight1")
data("tradeweightx")

p=2
lag.max=15
type="const"
ic="SC"
weight.matrix=tradeweightx
mainOUTPUT = GVARest(data=PriceVol,p,lag.max,type,ic,weight.matrix)

mainOUTPUT$lagmatrix    # Country-specific GVAR lags
mainOUTPUT$gvar
mainOUTPUT$gvar[[1]]
coef(mainOUTPUT$gvar[[17]])
mainOUTPUT$White[[17]]
mainOUTPUT$NWHAC[[17]][1]