Type: Package
Title: Perform Global Vector Autoregression Estimation and Inference
Version: 1.4
Date: 2023-1-30
Author: Ho Tsung-wu
Maintainer: Ho Tsung-wu <tsungwu@ntnu.edu.tw>
Description: Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <doi:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <doi:10.1002/jae.932>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyData: TRUE
LazyLoad: yes
Depends: R (≥ 3.5.0),vars,xts
Imports: lmtest, lubridate, sandwich, strucchange, tsDyn, urca
NeedsCompilation: no
Packaged: 2023-01-30 00:31:03 UTC; badal
Repository: CRAN
Date/Publication: 2023-01-30 00:50:02 UTC

Function to generate foreign variables

Description

Function to generate foreign variables

Usage

GVAR_Ft(data, weight.matrix=NULL)

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.

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. If NULL, then it computes the foreign vriables by average.

Value

Ft

Weighted foerign variables as described in GVAR

Author(s)

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

#=== Loading Data ===#
data("PriceVol")
data("tradeweight1")
data("tradeweightx")

#Generate country-specific foreign variables
Ft=GVAR_Ft(data=PriceVol,weight.matrix=tradeweight1)
k=17
head(Ft[[k]])
tail(Ft[[k]])

Compute the structural coefficients matrices G0, G1, G2, and F1, F2

Description

Compute the structural coefficients matrices G0, G1, G2, and F1, F2

Usage

GVAR_GF(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. 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.

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.

Details

This function generates several structural coefficient matrices of Eq.(2.6) in Filippo and Pesaran(2013, P.17), which are required to compute IRF and multistep forecasts. 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.

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)

fitted

In-sample fitted values, or conditional mean

data

data used

Author(s)

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

data("PriceVol")
data("tradeweightx")
data("tradeweight1")
p=2
type="const"
ic="SC"

Result=GVAR_GF(data=PriceVol,p,type,ic, weight.matrix=tradeweight1)
Result$G0
Result$G1
Result$G2
Result$F1
Result$F2
Result$lagmatrix
Result$RESID
Result$newRESID
Result$fitted
Result$data
#May use forecast::accuracy(Result$fitted[,1], Result$data[,1]) for performance.

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 <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

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]

Estimate country-specific Johansen test results in a Global VECM setting

Description

Estimate country-specific Johansen test results in a Global VECM setting

Usage

GVECM.jo(data,p=2,ecdet = "const", type = "eigen",spec = "longrun",
season = NULL,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. 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.

ecdet

Character, 'none' for no intercept in cointegration, 'const' for constant term in cointegration and 'trend' for trend variable in cointegration.

type

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

spec

Determines the specification of the VECM, see details in pakcage urca.

season

If seasonal dummies should be included, the data frequency must be set accordingly,i.e '4' for quarterly data.

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.

Value

JO.test

List object of country-specific Johansen test results

VECMoutputs

List object of country-specific VECM results

RESID

List object of country-specific VECM residuals, obtained by using vars::vec2var

Author(s)

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

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

p=2
FLag=2
type="const"
ic="SC"
weight.matrix=tradeweight1
mainOUT.JO=GVECM.jo(data=PriceVol,p=2,weight.matrix=weight.matrix)
mainOUT.JO$JO.test

Compute the structural coefficients matrices G0, G1, G2, and F1, F2

Description

Compute the structural coefficients matrices G0, G1, G2, and F1, F2

Usage

GVECM_GF(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.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.

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.

Details

This function generates several structural coefficient matrices of Eq.(2.6) in Filippo and Pesaran(2013, P.17), which are required to compute IRF and multistep forecasts. 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.

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.

newRESID

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

fitted

In-sample fitted values, or conditional mean

data

data used

Author(s)

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

data("PriceVol")
data("tradeweightx")
data("tradeweight1")
p=2
type="const"
ic="SC"

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

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)

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.

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

Author(s)

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

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]

Dataset price-volumn of 17 mareket indices

Description

A nine-year balanced panel price-volumn data of 17 mareket indices, 2006/8/30-2014/11/19

Usage

data("PriceVol")

Format

A data frame with 0 observations on the following 2 variables.

ID

Names of country, cross-section ID

Time

Time index

Ret

Daily returns computed by close-to-close

Vol

Daily transaction volumn, by log

Source

Yahoo finance

Examples

data(PriceVol)

Comparing average residual correlations.

Description

Average pairwise cross-section residual correlations.

Usage

averageCORgvar(out)

Arguments

out

Estimation results object generated by GVARest

Details

This function compares the dependency of residuals in VAR and GVAR.

Value

varRSDcor

A list object of average residual correlations of country-specific VAR

gvarRSDcor

A list object of average residual correlations of country-specific VAR augmented by foreign variables(GVAR)

Author(s)

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

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,lag.max,type,ic,weight.matrix)

cor2_avg=averageCORgvar(out=mainOUTPUT)
as.matrix((cor2_avg$varRSDcor)[[1]])
as.matrix((cor2_avg$varRSDcor)[[2]])

as.matrix(cor2_avg$gvarRSDcor[[1]])
as.matrix(cor2_avg$gvarRSDcor[[2]])

Comparing average residual correlations of GVECM and VECM.

Description

Average pairwise cross-section residual correlations of GVECM and VECM.

Usage

averageCORgvecm(out)

Arguments

out

Estimation results object generated by GVECMest

Details

This function compares the dependency of residuals in VAR and GVAR.

Value

vecmRSDcor

A list object of average residual correlations of country-specific VECM

gvecmRSDcor

A list object of average residual correlations of country-specific VECM augmented by foreign variables(GVECM)

Author(s)

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

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)

cor2_avg=averageCORgvecm(out=mainOUTPUT)
as.matrix((cor2_avg$vecmRSDcor)[[1]])
as.matrix((cor2_avg$vecmRSDcor)[[2]])

as.matrix(cor2_avg$gvecmRSDcor[[1]])
as.matrix(cor2_avg$gvecmRSDcor[[2]])

Return country-specific standard LS coefficient estimates.

Description

Extract country-specific standard LS coefficient estimates.

Usage

getCOEF(out,sheet)

Arguments

out

A list object of estimation results generated by GVARest()

sheet

The number of country in out file

Details

Extract country-specific standard LS coefficient estimates.

Value

coef

Country-specific coefficient estimates

Author(s)

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

Examples

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,lag.max,type,ic,weight.matrix)
COEF=getCOEF(out=mainOUTPUT,sheet=1)

All-country LS coefficient estimates.

Description

Extract all-country LS coefficient estimates.

Usage

getCOEFexo(out)

Arguments

out

A list object of estimation results generated by GVARest().

Details

Extract all-country LS coefficient estimates.

Value

coef

Country-specific coefficient estimates.

Author(s)

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

Examples

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,lag.max,type,ic,weight.matrix)
#COEF=getCOEFexo(out=mainOUTPUT)

Extract country-specific LS coefficient estimates with Newy-West robust covariance.

Description

Extract country-specific LS coefficient estimates with Newy-West robust covariance.

Usage

getNWCOEF(out,sheet)

Arguments

out

A list object of estimation results generated by GVARest.

sheet

The number of country in out that is to be saved.

Value

coef

Country-specific coefficient estimates.

Author(s)

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

References

Newey WK and West KD (1994) Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies,61,631-653.

Examples

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,lag.max,type,ic,weight.matrix)
COEF=getNWCOEF(out=mainOUTPUT,sheet=1)

Extract all-country coefficient estimates with Newy-West robust covariance.

Description

Extract all-country coefficient estimates with Newy-West robust covariance.

Usage

getNWCOEFexo(out)

Arguments

out

A list object of estimation results generated by GVARest.

Value

coef

Country-specific coefficient estimates.

Author(s)

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

References

Newey WK and West KD (1994) Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61, 631-653.

Examples

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,lag.max,type,ic,weight.matrix)
COEF=getNWCOEFexo(out=mainOUTPUT)

Extract country-specific LS coefficient estimates with White robust covariance.

Description

Extract country-specific LS coefficient estimates with White robust covariance.

Usage

getWhiteCOEF(out,sheet)

Arguments

out

A list object of estimation results generated by GVARest.

sheet

The number of country in out that is to be saved.

Value

coef

Country-specific coefficient estimates.

Author(s)

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

Examples

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,lag.max,type,ic,weight.matrix)

COEF=getWhiteCOEF(out=mainOUTPUT,sheet=1)

Extract all-country coefficient estimates with White robust covariance.

Description

Extract all-country coefficient estimates with Newy-West robust covariance, and save them in a .csv file.

Usage

getWhiteCOEFexo(out)

Arguments

out

A list object of estimation results generated by GVARest.

Value

coef

Country-specific coefficient estimates.

Author(s)

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

Examples

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,lag.max,type,ic,weight.matrix)
COEF=getWhiteCOEFexo(out=mainOUTPUT)

A single year cross-section bilateral trade weight matrix, 2014.

Description

A single year cross-section bilateral trade weight matrix, 2014

Usage

data("tradeweight1")

Format

A matrix of 17 by 17 bilateral trade weight matrix,2014

Australia

Bilateral trade weight matrix of Australia, 2014

Austria

Bilateral trade weight matrix of Austria, 2014

Belgium

Bilateral trade weight matrix of Belgium, 2014

Brazil

Bilateral trade weight matrix of Brazil, 2014

France

Bilateral trade weight matrix of France, 2014

UK

Bilateral trade weight matrix of UK, 2014

US

Bilateral trade weight matrix of US, 2014

Canada

Bilateral trade weight matrix of Canada, 2014

HongKong

Bilateral trade weight matrix of Hong Kong, 2014

Indonesia

Bilateral trade weight matrix of Indonesia, 2014

Malaysia

Bilateral trade weight matrix of Malaysia, 2014

Korea

Bilateral trade weight matrix of Korea, 2014

Mexico

Bilateral trade weight matrix of Mexico, 2014

Japan

Bilateral trade weight matrix of Japan, 2014

Swiss

Bilateral trade weight matrix of Swiss, 2014

China

Bilateral trade weight matrix of China, 2014

Taiwan

Bilateral trade weight matrix of Taiwan, 2014

Details

This matrix is a 17 by 17 trade weight matrix, the column names are 17 countries. Given column j, the row-wise elements are bilateral trade weights of country j. Please make sure that the order of countries exactly matches the dataset's ID column.

Examples

data(tradeweight1)
is.data.frame(tradeweight1)

A nine-year bilateral trade weight matrix, 2006-2014

Description

A nine-year bilateral trade weight matrix, 2006-2014

Usage

data("tradeweightx")

Format

A list with 17 by 17 matrix on the following variable.

Australia

Bilateral trade weight matrix of Australia, 2014

Austria

Bilateral trade weight matrix of Austria, 2014

Belgium

Bilateral trade weight matrix of Belgium, 2014

Brazil

Bilateral trade weight matrix of Brazil, 2014

France

Bilateral trade weight matrix of France, 2014

UK

Bilateral trade weight matrix of UK, 2014

US

Bilateral trade weight matrix of US, 2014

Canada

Bilateral trade weight matrix of Canada, 2014

HongKong

Bilateral trade weight matrix of Hong Kong, 2014

Indonesia

Bilateral trade weight matrix of Indonesia, 2014

Malaysia

Bilateral trade weight matrix of Malaysia, 2014

Korea

Bilateral trade weight matrix of Korea, 2014

Mexico

Bilateral trade weight matrix of Mexico, 2014

Japan

Bilateral trade weight matrix of Japan, 2014

Swiss

Bilateral trade weight matrix of Swiss, 2014

China

Bilateral trade weight matrix of China, 2014

Taiwan

Bilateral trade weight matrix of Taiwan, 2014

Details

This example data is annual trade weight matrix, it is a list with length 9 (2006-2014).Each list is a year specific 17 by 17 trade weight matrix, the column names are 17 countries. Given column j, the row-wise elements are bilateral trade weights of country j. Make sure that the length of list must exactly match with the number of years. Because once you use this as tradewieght input matrix, R function will automatically compute foreign variables weighted year-by-year. Please make sure that the order of countries exactly matches the dataset's ID column.

Examples

data(tradeweightx)
is.data.frame(tradeweightx)