GVECMest: Estimate country-specific Engle-Granger VECM in a Global VECM setting

Description

Estimate country-specific Engle-Granger VECM in a Global VECM setting

Usage

GVECMest(data,p=2,lag.max=NULL, type="const", ic,weight.matrix=NULL)

Value

gvecm: Country-specific GVECM output list
White: Coefficient estimates with White robust coVECMiance
NWHAC: Coefficient estimates withNewy-West robust coVECMiance
p: Number of lags for endogeneous VECMiables in VECM
K: Number of lags for Ft VECMiables in VECM
type: Model specificaiton. As in package VECMs, we have four selection: "none","const","trend", and "both".
datamat: input data=data
lagmatrix: GVECM's Country-secific optimal lag number.
lagmatrix1: VECM's Country-secific optimal lag number.
exoLag: Ft lags
Ft: Foreign VECMiables
NAMES: Names of countries
gvecmRSD: Country-specific Global VECM residuals
vecmRSD: VECM residuals

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. Foreign variables are set by FLag=p+1. Current version restricts p <= 2 for simplicity, which aims at 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 VECM
type: Model specificaiton for VECM. As in package VECMs, we have four selection: "none","const","trend", "both".
ic: Information criteria for optimal lag.As in package VECMs, we have four selection: "AIC", "HQ", "SC", and "FPE".
weight.matrix: Bilateral trade weight matrix for computing foreign VECMiables. If the computation of foreign VECMiables are weighted by one weighting matrix, weight.matrix must be a "data.frame". If the computation of foreign VECMiables 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 GVECM 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
FLag=2
lag.max=15
type="const"
ic="SC"
weight.matrix=tradeweightx
mainOUTPUT = GVECMest(data=PriceVol,p,lag.max,type,ic,weight.matrix)

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

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