GVARest: 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)

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

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.

Author

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, 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

Run this code

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]

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