Estimate country-specific VAR in a GVAR setting
Estimate country-specific VAR in a GVAR setting
GVARest(data,p,FLag, lag.max, type="const", ic,weight.matrix=NULL)
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. |
FLag |
The number of lag for foreign variables in country-specific VAR |
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. |
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 |
Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.
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.
data("PriceVol")
data("tradeweight1")
data("tradeweightx")
p=2
FLag=2
lag.max=15
type="const"
ic="SC"
weight.matrix=tradeweightx
mainOUTPUT = GVARest(data=PriceVol,p,FLag,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]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.