| Title: | Pooled Mean Group Estimation of Dynamic Heterogenous Panels |
|---|---|
| Description: | Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>. |
| Authors: | Piotr Zientara [aut], Lech Kujawski [aut, cre] |
| Maintainer: | Lech Kujawski <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0 |
| Built: | 2024-10-31 20:46:13 UTC |
| Source: | CRAN |
Calculates the pool mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>. This estimator enables the intercepts, short-run coefficient and error variances to differ freely across groups, but restricts the long-run coefficients to being equal. Additionally, it allows the numbers of time series observations to differ freely across groups. This software also performs diagnostic tests of error terms, such as autocorrelation, heteroscedasticity and normality. Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
The DESCRIPTION file:
| Package: | PooledMeanGroup |
| Version: | 1.0 |
| Date: | 2017-12-13 |
| Title: | Pooled Mean Group Estimation of Dynamic Heterogenous Panels |
| Authors@R: | c(person("Piotr", "Zientara", role="aut", email="[email protected]"), person("Lech", "Kujawski", role=c("aut", "cre"), email = "[email protected]")) |
| Author: | Piotr Zientara [aut], Lech Kujawski [aut, cre] |
| Maintainer: | Lech Kujawski <[email protected]> |
| Depends: | R (>= 3.2.3) |
| Description: | Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>. |
| License: | GPL (>= 2) |
| URL: | https://www.r-project.org |
| NeedsCompilation: | no |
| Packaged: | 2017-12-14 08:45:00 UTC; Marcin |
| Repository: | CRAN |
| Date/Publication: | 2017-12-14 13:08:28 UTC |
Index of help topics:
BGtest BGtest
ConoverMulti ConoverMulti
DataExp DataExp
DiffPanel DiffPanel
GQtest GQtest
JBtest JBtest
LagPanel LagPanel
PMG PMG
PanelNaOmit PanelNaOmit
PooledMeanGroup-package
Pooled Mean Group Estimation of Dynamic
Heterogenous Panels
optimPMG optimPMG
Lech Kujawski, Piotr Zientara Piotr Zientara [aut], Lech Kujawski [aut, cre]
Maintainer: Lech Kujawski <[email protected]>
Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>
Tests autocorrelation between the current and lagged residuals. The test is a joint test of the first P autocorrelations
BGtest(residuals, explvariab, acor.ord)BGtest(residuals, explvariab, acor.ord)
residuals |
residuals for group i |
explvariab |
explanatory variables (regressors) for group i |
acor.ord |
order of tested autocorrelations |
Calculates statistics and probs of the Breusch-Godfrey autocorrelation test (with two variants: chi-squared and F
Chi-squared and F statistics with probs
Lech Kujawski, Piotr Zientara
# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) # BGtest ExpBGtest=BGtest(residuals=resid(model), explvariab=cbind(x1,x2), acor.ord=4) ExpBGtest# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) # BGtest ExpBGtest=BGtest(residuals=resid(model), explvariab=cbind(x1,x2), acor.ord=4) ExpBGtest
Tests for homoscedasticity among subsamples k within a particular group i (note that the Conover test is a non-parametric test)
ConoverMulti(residuals, subsample)ConoverMulti(residuals, subsample)
residuals |
residuals for group i |
subsample |
the vector of c(s1i, s2i,..., ski), where s1i+s2i+...+ski=Ti (i.e., the vector divides a particular group i into subsamples) |
Calculates chi-squared statistic with a prob
Chi-squared statistic with a prob
Lech Kujawski, Piotr Zientara
# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) # ConoverMulti ExpConoverMulti=ConoverMulti(residuals=resid(model), subsample=c(10,10,10)) ExpConoverMulti# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) # ConoverMulti ExpConoverMulti=ConoverMulti(residuals=resid(model), subsample=c(10,10,10)) ExpConoverMulti
A dataset in the form of stacked time-series. Quarterly data cover nine countries (Poland, Bulgaria, the Czech Republic, Hungary, Latvia, Lithuania, Romania, Slovakia, Slovenia; the numbers denoting particular countries form a series i=1,2,...,9) from 2005q2 to 2013q4. The dataset contains the following (below) variables
data("DataExp")data("DataExp")
A data frame with 315 observations on the following 16 variables.
y10a numeric vector
y10spreada numeric vector
riskaversa numeric vector
debta numeric vector
deficita numeric vector
openessa numeric vector
cpia numeric vector
growtha numeric vector
criska numeric vector
uratea numeric vector
iipa numeric vector
iipnettoa numeric vector
cagdpa numeric vector
caresvsa numeric vector
cdsa numeric vector
bidaska numeric vector
data(DataExp)data(DataExp)
Calculates first differences of a particular variable from a panel data set
DiffPanel(variable, quantity)DiffPanel(variable, quantity)
variable |
a particular variable from a panel data set in the form of stacked time series; in practice; a selected singular column from a panel data set |
quantity |
a vector of the number of time series observations in each group; in practice, it takes the form c(T1,...Tn) since the PMG allows the numbers of time series observations to differ freely across groups (if the number of time series observations in each group is the same, then c(T,...,T) and T=T1=T2=...=Tn |
Calculates first differences of a particular variable from a panel data set in order to bring it to stationarity. Preserves the original dimension of time series observations in each group, completing data lost due to differentiating by inserting "NA"
First differences of a particular variable from a panel data set
Lech Kujawski, Piotr Zientara
# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then execute DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) cbind(y10,dy10,diip)[1:5,]# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then execute DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) cbind(y10,dy10,diip)[1:5,]
Tests for homoscedasticity
GQtest(residuals, subsample, nep)GQtest(residuals, subsample, nep)
residuals |
residuals for group i |
subsample |
the vector of c(s1i, s2i), where s1i=Ti/2 and s2i=s1i+1 (if Ti is EVEN) or where s1i=Ti/2-0.5 and s2i=s1i+1 (if Ti is UNEVEN) |
nep |
the number of estimated parameters for group i |
Calculates F statistic with a prob
F statistic with a prob
Lech Kujawski, Piotr Zientara
# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) #BGtest ExpGQtest=GQtest(residuals=resid(model), subsample=c(15,16), nep=3) ExpGQtest# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) #BGtest ExpGQtest=GQtest(residuals=resid(model), subsample=c(15,16), nep=3) ExpGQtest
Tests for normality
JBtest(residuals)JBtest(residuals)
residuals |
residuals for group i |
Calculates chi-squared statistic with a prob
Chi-squared statistic with a prob
Lech Kujawski, Piotr Zientara
# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) #JBtest ExpJBtest=JBtest(residuals=resid(model)) ExpJBtest# creating artificial variables x1=rnorm(30,0,1) x2=rnorm(30,0,1) e=rnorm(30,0,0.2) y=1+2*x1+3*x2+e # any model model=lm(y~x1+x2) #JBtest ExpJBtest=JBtest(residuals=resid(model)) ExpJBtest
Provides the first lag of a particular variable from a panel data set
LagPanel(variable, quantity)LagPanel(variable, quantity)
variable |
a particular variable from a panel data set in the form of stacked time series; in practice; a selected singular column from a panel data set |
quantity |
a vector of the number of time series observations in each group; in practice, it takes the form c(T1,...Tn) since the PMG allows the numbers of time series observations to differ freely across groups (if the number of time series observations in each group is the same, then c(T,...,T) and T=T1=T2=...=Tn |
Provides the first lag of a particular variable from a panel data set. Preserves the original dimension of time series observations in each group, completing data lost due to lagging by inserting "NA"
A lagged particular variable from a panel data set
Lech Kujawski, Piotr Zientara
# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then execute LagPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) cbind(y10,ly10,ldebt)[1:5,]# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then execute LagPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) cbind(y10,ly10,ldebt)[1:5,]
Estimates parameters of long-run and short-run relationships. Makes use of a "back-substitution" algorithm, as described by Pesaran, Shin and Smith (1999). Also estimates the information matrix as well as standard errors of estimations, as indicated in Equation 13 (Pesaran, Shin and Smith, 1999). Calculates Student's t-distribution type statistics, probs and confidence intervals. Also performs diagnostic tests of error terms, such as the Breusch-Godfrey autocorrelation test, the Goldfeld-Quandt heteroscedasticity test, the Conover nonparametric test of homogeneity of variance and the Jarque-Bera normality test
optimPMG(dLL, maxIter, TetaStart, vecSR, vecLR, dataset, quantity, const)optimPMG(dLL, maxIter, TetaStart, vecSR, vecLR, dataset, quantity, const)
dLL |
a parameter indicating the convergence criterion; an optimization algorithm is stopped when an increase in concentrated log-likelihood function (Equation 8 in Pesaran, Shin and Smith (1999)) is less than dLL; the default value is dLL=10^-10 |
maxIter |
a maximum number of iterations; the default value is 200 |
TetaStart |
a vector of first (initial) Teta values, from which the algorithm starts searching for parameters ensuring the maximization of log-likelihood function |
vecSR |
a list of vectors containing the column numbers of variables in short-run relationships for each group (alternatively a list of vectors containing the variables names instead of column numbers). In each vector of the list the first number must indicate dy (i.e., the dependant variable) |
vecLR |
a vector containing the column numbers of variables in long-run relationships (alternatively a vector containing the variables names instead of column numbers). The first number must indicate ly (i.e., the lagged dependant variable) |
dataset |
a panel data set in the form of stacked time series, containing variables of long-run and short-run relationships (i.e., including differentiated and lagged variables) |
quantity |
a vector of the number of time series observations in each group; in practice, it takes the form c(T1,...,Tn) since the PMG allows the numbers of time series observations to differ freely across groups (if the number of time series observations in each group is the same, then c(T,...,T) and T=T1=T2=...=Tn |
const |
logical. If TRUE (the default value), the intercept term is added to the model (i.e., to the short-run relationship) |
Estimates parameters of long-run and short-run relationships. Also estimates the information matrix as well as standard errors of estimations, as indicated in Equation 13 (Pesaran, Shin and Smith, 1999). Calculates Student's t-distribution type statistics, probs and confidence intervals. Also performs diagnostic tests of error terms, such as the Breusch-Godfrey autocorrelation test, the Goldfeld-Quandt heteroscedasticity test and the Conover nonparametric test of homogeneity of variance and the Jarque-Bera normality test
$LogL |
the concentrated log-likelihood function |
$dLogL |
the incresase of concentrated log-likelihood function in last iteration |
$i |
the number of iterations performed to achieve convergence |
$LR |
the estimated parameters of long-run relationships |
$SR |
the estimated parameters of short-run relationships |
$DiagTests |
results of diagnostic tests |
$residuals |
residuals |
Lech Kujawski, Piotr Zientara
Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>
# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then prepare lags and diffs using LagPanel and DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel) dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip, DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7]) dataPanel=data.frame(dataPanel) names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip", "iip", "ldcrisk", "crisk", "ddcpi", "cpi") dataPanel[1:5,] # prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9)) dataPanel$dataset[1:5,] dataPanel$quantity # optimPMG OptimPmgExp=optimPMG( dLL=10^-10, maxIter=200, TetaStart=rep(x=1, times=4), # note that length(TetaStart)=length(vecLR)-1 vecSR=list(SR1=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR2=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR3=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR4=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR5=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR6=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR7=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR8=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR9=c("dy10","dopeness","diip","ldcrisk","ddcpi")), vecLR=c("ly10","openess","iip","crisk","cpi"), dataset=dataPanel$dataset, quantity=dataPanel$quantity, const=TRUE) OptimPmgExp# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then prepare lags and diffs using LagPanel and DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel) dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip, DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7]) dataPanel=data.frame(dataPanel) names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip", "iip", "ldcrisk", "crisk", "ddcpi", "cpi") dataPanel[1:5,] # prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9)) dataPanel$dataset[1:5,] dataPanel$quantity # optimPMG OptimPmgExp=optimPMG( dLL=10^-10, maxIter=200, TetaStart=rep(x=1, times=4), # note that length(TetaStart)=length(vecLR)-1 vecSR=list(SR1=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR2=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR3=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR4=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR5=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR6=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR7=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR8=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR9=c("dy10","dopeness","diip","ldcrisk","ddcpi")), vecLR=c("ly10","openess","iip","crisk","cpi"), dataset=dataPanel$dataset, quantity=dataPanel$quantity, const=TRUE) OptimPmgExp
Prepares a panel data set for further calculations by eliminating "NA" and modifying quantity or a vector of the number of time series observations in each group
PanelNaOmit(dataset, quantity)PanelNaOmit(dataset, quantity)
dataset |
a panel data set in the form of stacked time series, containing variables of long-run and short-run relationships (i.e., including differentiated and lagged variables from DiffPanel or LagPanel) |
quantity |
a vector of the number of time series observations in each group; in practice, it takes the form c(T1,...Tn) since the PMG allows the numbers of time series observations to differ freely across groups (if the number of time series observations in each group is the same, then c(T,...,T) and T=T1=T2=...=Tn |
Eliminates "NA" and modifies quantity or a vector of the number of time series observations in each group
$dataset |
panel data set for further calculations modified by eliminating "NA" |
$quantity |
modified vector of the number of time series observations in each group |
Lech Kujawski, Piotr Zientara
# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then prepare lags and diffs using LagPanel and DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA") after DiffPanel, LagPanel dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip, DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7]) dataPanel=data.frame(dataPanel) names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip", "iip", "ldcrisk", "crisk", "ddcpi", "cpi") dataPanel[1:5,] # prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9)) dataPanel$dataset[1:5,] dataPanel$quantity# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then prepare lags and diffs using LagPanel and DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA") after DiffPanel, LagPanel dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip, DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7]) dataPanel=data.frame(dataPanel) names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip", "iip", "ldcrisk", "crisk", "ddcpi", "cpi") dataPanel[1:5,] # prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9)) dataPanel$dataset[1:5,] dataPanel$quantity
Having particular long-run parameters (exp. start values) estimates parameters of short-run relationships as well as standard errors of estimations, Student's t-distribution type statistics, probs, confidence intervals. Also performs diagnostic tests of error terms, such as autocorrelation, heteroscedasticity and normality
PMG(paramTeta, vecSR, vecLR, dataset, quantity, const)PMG(paramTeta, vecSR, vecLR, dataset, quantity, const)
paramTeta |
the vector of parameters of long-run relationships, as outlined in Equation 7 (Pesaran, Shin and Smith, 1999) |
vecSR |
a list of vectors containing the column numbers of variables in short-run relationships for each group (alternatively a list of vectors containing the variables names instead of column numbers). In each vector of the list the first number must indicate dy (i.e., the dependant variable) |
vecLR |
a vector containing the column numbers of variables in long-run relationships (alternatively a vector containing the variables names instead of column numbers). The first number must indicate ly (i.e., the lagged dependant variable) |
dataset |
a panel data set in the form of stacked time series, containing variables of long-run and short-run relationships (i.e., including differentiated and lagged variables) |
quantity |
a vector of the number of time series observations in each group; in practice, it takes the form c(T1,...,Tn) since the PMG allows the numbers of time series observations to differ freely across groups (if the number of time series observations in each group is the same, then c(T,...,T) and T=T1=T2=...=Tn |
const |
logical. If TRUE (the default value), the intercept term is added to the model (i.e., to the short-run relationship) |
Having particular long-run parameters estimates parameters of short-run relationships. Also estimates the information matrix as well as standard errors of estimations, as indicated in Equation 13 (Pesaran, Shin and Smith, 1999). Calculates Student's t-distribution type statistics, probs and confidence intervals. Also performs diagnostic tests of error terms, such as the Breusch-Godfrey autocorrelation test, the Goldfeld-Quandt heteroscedasticity test and the Conover nonparametric test of homogeneity of variance and the Jarque-Bera normality test
$LogL |
the concentrated log-likelihood function |
$LR |
parameters of long-run relationships |
$SR |
the estimated parameters of short-run relationships |
$DiagTests |
results of diagnostic tests |
$residuals |
residuals |
Lech Kujawski, Piotr Zientara
Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>
# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then prepare lags and diffs using LagPanel and DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel) dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip, DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7]) dataPanel=data.frame(dataPanel) names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip", "iip", "ldcrisk", "crisk", "ddcpi", "cpi") dataPanel[1:5,] # prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9)) dataPanel$dataset[1:5,] dataPanel$quantity # PMG PmgExp=PMG( paramTeta=c(-14.22768, -23.84427, -0.75717, 27.57753), vecSR=list(SR1=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR2=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR3=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR4=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR5=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR6=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR7=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR8=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR9=c("dy10","dopeness","diip","ldcrisk","ddcpi")), vecLR=c("ly10","openess","iip","crisk","cpi"), dataset=dataPanel$dataset, quantity=dataPanel$quantity, const=TRUE) PmgExp# first import DataExp, i=1...9, T1=T2=...T9=35 data(DataExp) DataExp[1:5,] # then prepare lags and diffs using LagPanel and DiffPanel y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel) dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip, DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7]) dataPanel=data.frame(dataPanel) names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip", "iip", "ldcrisk", "crisk", "ddcpi", "cpi") dataPanel[1:5,] # prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9)) dataPanel$dataset[1:5,] dataPanel$quantity # PMG PmgExp=PMG( paramTeta=c(-14.22768, -23.84427, -0.75717, 27.57753), vecSR=list(SR1=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR2=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR3=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR4=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR5=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR6=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR7=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR8=c("dy10","dopeness","diip","ldcrisk","ddcpi"), SR9=c("dy10","dopeness","diip","ldcrisk","ddcpi")), vecLR=c("ly10","openess","iip","crisk","cpi"), dataset=dataPanel$dataset, quantity=dataPanel$quantity, const=TRUE) PmgExp