optimPMG: optimPMG
In PooledMeanGroup: Pooled Mean Group Estimation of Dynamic Heterogenous Panels
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
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
Usage
1
optimPMG(dLL, maxIter, TetaStart, vecSR, vecLR, dataset, quantity, const)
Arguments
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)
Details
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
Value
$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
$residuals
residuals
Author(s)
Lech Kujawski, Piotr Zientara
References
Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>
Examples
1
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13
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20
21
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# first import DataExp, i=1...9, T1=T2=...T9=35
data(DataExp)
DataExp[1:5,]
# then prepare lags and diffs using LagPanel and DiffPanel
y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp))
cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp))
dy10=DiffPanel(variable=y10, quantity=rep(35,9))
dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9))
ly10=LagPanel(variable=y10, quantity=rep(35,9))
diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9))
dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9))
ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9))
dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9))
ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9))
ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9))
# create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel)
dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip,
DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7])
dataPanel=data.frame(dataPanel)
names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip",
"iip", "ldcrisk", "crisk", "ddcpi", "cpi")
dataPanel[1:5,]
# prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit
dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9))
dataPanel$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
Example output
y10 y10spread riskavers debt deficit openess cpi growth
i=1,t=1,2005q2 4.91 1.78 0.4907953 0.478 -0.0446 0.749 1.001360 0.006
i=1,t=2,2005q3 4.57 1.50 0.5888259 0.468 -0.0436 0.754 0.999384 0.016
i=1,t=3,2005q4 5.16 1.82 0.4938176 0.471 -0.0487 0.776 1.003080 0.012
i=1,t=4,2006q1 4.79 1.15 0.7014965 0.490 -0.0215 0.804 1.002830 0.016
i=1,t=5,2006q2 5.55 1.59 0.6218348 0.494 -0.0430 0.819 1.011460 0.017
crisk urate iip iipnetto cagdp caresvs cds
i=1,t=1,2005q2 74.76667 0.1830 1.079462 0.4271757 -0.018 -0.04051902 16.21965
i=1,t=2,2005q3 74.80000 0.1773 1.093086 0.4364869 -0.028 -0.04921230 15.82511
i=1,t=3,2005q4 76.00000 0.1740 1.117122 0.4430011 -0.027 -0.03684523 16.41765
i=1,t=4,2006q1 76.86667 0.1793 1.113280 0.4436238 -0.036 -0.07054284 17.54188
i=1,t=5,2006q2 76.33333 0.1653 1.085001 0.4320550 -0.035 -0.06876323 23.02218
bidask
i=1,t=1,2005q2 0.000245231
i=1,t=2,2005q3 0.000252121
i=1,t=3,2005q4 0.000385385
i=1,t=4,2006q1 0.000323231
i=1,t=5,2006q2 0.000337833
y10 dy10 ly10 openess dopeness diip iip
i=1,t=1,2005q2 4.91 NA NA 0.749 NA NA 1.079462
i=1,t=2,2005q3 4.57 -0.34 4.91 0.754 0.005 0.013623126 1.093086
i=1,t=3,2005q4 5.16 0.59 4.57 0.776 0.022 0.024036799 1.117122
i=1,t=4,2006q1 4.79 -0.37 5.16 0.804 0.028 -0.003842008 1.113280
i=1,t=5,2006q2 5.55 0.76 4.79 0.819 0.015 -0.028278985 1.085001
ldcrisk crisk ddcpi cpi
i=1,t=1,2005q2 NA 74.76667 NA 1.001360
i=1,t=2,2005q3 NA 74.80000 NA 0.999384
i=1,t=3,2005q4 0.03333333 76.00000 0.005672 1.003080
i=1,t=4,2006q1 1.20000000 76.86667 -0.003946 1.002830
i=1,t=5,2006q2 0.86666667 76.33333 0.008880 1.011460
y10 dy10 ly10 openess dopeness diip iip
i=1,t=3,2005q4 5.16 0.59 4.57 0.776 0.022 0.024036799 1.117122
i=1,t=4,2006q1 4.79 -0.37 5.16 0.804 0.028 -0.003842008 1.113280
i=1,t=5,2006q2 5.55 0.76 4.79 0.819 0.015 -0.028278985 1.085001
i=1,t=6,2006q3 5.48 -0.07 5.55 0.836 0.017 0.020901595 1.105903
i=1,t=7,2006q4 5.14 -0.34 5.48 0.845 0.009 0.057480412 1.163383
ldcrisk crisk ddcpi cpi
i=1,t=3,2005q4 0.03333333 76.00000 0.005672 1.00308
i=1,t=4,2006q1 1.20000000 76.86667 -0.003946 1.00283
i=1,t=5,2006q2 0.86666667 76.33333 0.008880 1.01146
i=1,t=6,2006q3 -0.53333334 77.66667 -0.006170 1.01392
i=1,t=7,2006q4 1.33333334 76.83333 0.000010 1.01639
[1] 33 33 33 33 33 33 33 33 33
$LogL
[,1]
[1,] -246.3219
$dLogL
[,1]
[1,] 2.310685e-11
$i
[1] 44
$LR
Coef StdErr z P>|z| Low95% High95%
openess -14.2276819 1.8946444 -7.509421 5.928591e-14 -17.941117 -10.5142471
iip -23.8442534 3.7437226 -6.369129 1.901042e-10 -31.181815 -16.5066920
crisk -0.7571656 0.1489203 -5.084369 3.688511e-07 -1.049044 -0.4652872
cpi 27.5775310 3.6967195 7.460001 8.659740e-14 20.332094 34.8229682
$SR
$SR[[1]]
Coef(i=1) StdErr z P>|z| Low95% High95%
ec1 -0.03252643 0.02202632 -1.4767075 0.13975407 -0.07569723 0.010644364
dopeness 5.78138705 4.10533843 1.4082608 0.15905387 -2.26492841 13.827702513
diip -0.71549091 0.96819504 -0.7389946 0.45991027 -2.61311832 1.182136493
ldcrisk -0.11288716 0.05931871 -1.9030618 0.05703248 -0.22914969 0.003375363
ddcpi 10.25211574 5.61342341 1.8263571 0.06779649 -0.74999198 21.254223466
const 2.42855562 1.61978155 1.4993106 0.13379308 -0.74615787 5.603269120
$SR[[2]]
Coef(i=2) StdErr z P>|z| Low95%
ec2 0.005437134 0.03615882 0.15036814 0.88047417 -0.06543285
dopeness -0.105145562 1.06568476 -0.09866479 0.92140443 -2.19384932
diip 0.524903505 1.84165894 0.28501667 0.77563137 -3.08468168
ldcrisk -0.015035884 0.08619423 -0.17444188 0.86151820 -0.18397348
ddcpi -10.090130520 5.01905907 -2.01036298 0.04439279 -19.92730553
const -0.507038729 3.26265592 -0.15540674 0.87650067 -6.90172682
High95%
ec2 0.07630711
dopeness 1.98355819
diip 4.13448869
ldcrisk 0.15390171
ddcpi -0.25295551
const 5.88764936
$SR[[3]]
Coef(i=3) StdErr z P>|z| Low95% High95%
ec3 -0.02970175 0.03181964 -0.9334409 0.3505924 -0.0920671 0.03266359
dopeness -3.23702006 2.99810162 -1.0796899 0.2802803 -9.1131912 2.63915114
diip 1.60888923 1.98672122 0.8098213 0.4180429 -2.2850128 5.50279127
ldcrisk 0.04882033 0.08410627 0.5804601 0.5616044 -0.1160249 0.21366559
ddcpi 3.76680529 5.69811003 0.6610622 0.5085724 -7.4012852 14.93489573
const 2.63391417 2.84243026 0.9266416 0.3541126 -2.9371468 8.20497512
$SR[[4]]
Coef(i=4) StdErr z P>|z| Low95%
ec4 -0.01226321 0.007701996 -1.59221142 0.11133721 -0.02735884
dopeness -0.77268159 3.387631973 -0.22808900 0.81957705 -7.41231825
diip -0.01801169 0.475599099 -0.03787158 0.96979007 -0.95016879
ldcrisk 0.24045768 0.130799546 1.83836788 0.06600822 -0.01590472
ddcpi 11.39009171 10.456336572 1.08930041 0.27602143 -9.10395138
const 2.20722341 1.335799806 1.65236093 0.09846100 -0.41089610
High95%
ec4 0.002832429
dopeness 5.866955073
diip 0.914145417
ldcrisk 0.496820084
ddcpi 31.884134800
const 4.825342920
$SR[[5]]
Coef(i=5) StdErr z P>|z| Low95%
ec5 -0.108259329 0.02843895 -3.80672715 0.0001408180 -0.1639986
dopeness 1.062649919 4.92344303 0.21583471 0.8291165881 -8.5871211
diip 0.094635822 1.46369103 0.06465560 0.9484482145 -2.7741459
ldcrisk 0.002816201 0.07754723 0.03631595 0.9710304343 -0.1491736
ddcpi -5.590857580 8.67144028 -0.64474383 0.5190932105 -22.5865682
const 10.766531935 2.63696208 4.08293014 0.0000444714 5.5981812
High95%
ec5 -0.05252001
dopeness 10.71242095
diip 2.96341752
ldcrisk 0.15480598
ddcpi 11.40485307
const 15.93488263
$SR[[6]]
Coef(i=6) StdErr z P>|z| Low95% High95%
ec6 -0.6613952 0.1085913 -6.090681 1.124311e-09 -0.8742303 -0.4485601
dopeness 3.5862064 2.8373976 1.263907 2.062634e-01 -1.9749908 9.1474036
diip 9.5282203 4.2552125 2.239188 2.514369e-02 1.1881571 17.8682835
ldcrisk 0.3823801 0.1656203 2.308776 2.095603e-02 0.0577703 0.7069899
ddcpi -7.9951253 11.4489864 -0.698326 4.849733e-01 -30.4347263 14.4444756
const 53.7559053 9.3244673 5.765038 8.163961e-09 35.4802852 72.0315254
$SR[[7]]
Coef(i=7) StdErr z P>|z| Low95% High95%
ec7 -0.1194344 0.03979726 -3.0010701 0.002690327 -0.1974356 -0.04143317
dopeness 5.3567786 3.61597832 1.4814189 0.138494981 -1.7304086 12.44396592
diip 2.3327065 2.37340615 0.9828518 0.325680381 -2.3190841 6.98449710
ldcrisk -0.2740193 0.11969588 -2.2892963 0.022062142 -0.5086189 -0.03941972
ddcpi -9.1485216 7.02294527 -1.3026617 0.192690293 -22.9132414 4.61619818
const 7.4783925 2.74365245 2.7257069 0.006416394 2.1009325 12.85585249
$SR[[8]]
Coef(i=8) StdErr z P>|z| Low95% High95%
ec8 -0.02511285 0.02059801 -1.21918808 0.2227728 -0.06548422 0.01525851
dopeness -1.29529758 1.46579880 -0.88368034 0.3768688 -4.16821044 1.57761528
diip 0.01370812 1.18972876 0.01152205 0.9908069 -2.31811740 2.34553363
ldcrisk -0.04158100 0.09265287 -0.44878262 0.6535885 -0.22317727 0.14001528
ddcpi 3.57092127 9.91379909 0.36019706 0.7186998 -15.85976791 23.00161045
const 2.63783434 2.14297910 1.23091930 0.2183530 -1.56232751 6.83799619
$SR[[9]]
Coef(i=9) StdErr z P>|z| Low95% High95%
ec9 0.004225679 0.04286397 0.09858347 0.92146899 -0.07978616 0.08823752
dopeness -1.467380039 2.90674421 -0.50481911 0.61368589 -7.16449401 4.22973393
diip -1.286021868 2.04436890 -0.62905568 0.52931260 -5.29291128 2.72086754
ldcrisk 0.128452009 0.07472078 1.71909355 0.08559734 -0.01799803 0.27490204
ddcpi 10.996486420 5.83318469 1.88516000 0.05940824 -0.43634548 22.42931832
const -0.261817297 3.88473341 -0.06739646 0.94626608 -7.87575487 7.35212027
$DiagTests
R^2 Rbar^2 statJB probJB statGQ probGQ
i=1 0.30080890 0.17132907 0.71304772 7.001058e-01 1.171858 0.4050681554
i=2 0.11834714 -0.04492191 173.18220471 0.000000e+00 3.298566 0.0313337851
i=3 0.09058024 -0.07783083 1.24067570 5.377627e-01 2.437157 0.0858215193
i=4 0.20357789 0.05609231 51.82313445 5.581424e-12 3.450052 0.0269277283
i=5 0.55013643 0.46682836 0.03904985 9.806645e-01 1.583561 0.2385894048
i=6 0.78296297 0.74277092 0.82869197 6.607723e-01 1.210697 0.3853192999
i=7 0.45998113 0.35997763 1.59647161 4.501224e-01 1.424955 0.2922699445
i=8 0.08191103 -0.08810545 2.81556452 2.446853e-01 2.072518 0.1306343786
i=9 0.21505445 0.06969416 2.14641446 3.419102e-01 9.256368 0.0007294311
statCON probCON statBG(F) probBG(F) statBG(Chi^2) probBG(Chi^2)
i=1 0.1003408 0.951067335 0.04856346 0.9950357 0.1942539 0.99557776
i=2 3.1754160 0.204393548 0.10580439 0.9785707 0.4232176 0.98053226
i=3 7.7181030 0.021087992 0.50897006 0.7301476 2.0358802 0.72915943
i=4 7.1914359 0.027440974 0.31071878 0.8660435 1.2428751 0.87099031
i=5 4.4001322 0.110795835 0.35108547 0.8389765 1.4043419 0.84344003
i=6 5.8912389 0.052569485 1.67433552 0.2115386 6.6973421 0.15277321
i=7 3.9016396 0.142157481 0.17147565 0.9493820 0.6859026 0.95305597
i=8 1.3961242 0.497548578 0.37641769 0.8216791 1.5056708 0.82563649
i=9 11.9132322 0.002588657 3.48988983 0.0354738 13.9595593 0.00742525
$residuals
$residuals[[1]]
[,1]
i=1,t=3,2005q4 0.34318765
i=1,t=4,2006q1 -0.39040416
i=1,t=5,2006q2 0.58024534
i=1,t=6,2006q3 -0.15044840
i=1,t=7,2006q4 -0.17693157
i=1,t=8,2007q1 -0.11127411
i=1,t=9,2007q2 0.30474005
i=1,t=10,2007q3 0.30858050
i=1,t=11,2007q4 0.01052414
i=1,t=12,2008q1 0.14234326
i=1,t=13,2008q2 0.54540505
i=1,t=14,2008q3 -0.20241249
i=1,t=15,2008q4 -0.32335071
i=1,t=16,2009q1 0.14298986
i=1,t=17,2009q2 -0.36639824
i=1,t=18,2009q3 0.02550564
i=1,t=19,2009q4 0.21071933
i=1,t=20,2010q1 -0.31833937
i=1,t=21,2010q2 0.03916362
i=1,t=22,2010q3 -0.26555037
i=1,t=23,2010q4 0.57088649
i=1,t=24,2011q1 0.34986787
i=1,t=25,2011q2 -0.29012151
i=1,t=26,2011q3 -0.16939701
i=1,t=27,2011q4 -0.32368506
i=1,t=28,2012q1 -0.43015691
i=1,t=29,2012q2 -0.03391308
i=1,t=30,2012q3 0.11646648
i=1,t=31,2012q4 -0.69875724
i=1,t=32,2013q1 0.16561443
i=1,t=33,2013q2 -0.11225239
i=1,t=34,2013q3 0.46980457
i=1,t=35,2013q4 0.03734836
$residuals[[2]]
[,1]
i=2,t=3,2005q4 0.26138815
i=2,t=4,2006q1 0.53256047
i=2,t=5,2006q2 0.13915654
i=2,t=6,2006q3 -0.27397754
i=2,t=7,2006q4 0.19196413
i=2,t=8,2007q1 0.10095382
i=2,t=9,2007q2 0.05159331
i=2,t=10,2007q3 0.23508937
i=2,t=11,2007q4 0.53021788
i=2,t=12,2008q1 -0.22562174
i=2,t=13,2008q2 0.01823027
i=2,t=14,2008q3 -0.04268963
i=2,t=15,2008q4 2.51773433
i=2,t=16,2009q1 -0.10327622
i=2,t=17,2009q2 -0.48045656
i=2,t=18,2009q3 -0.04024994
i=2,t=19,2009q4 -0.65808308
i=2,t=20,2010q1 -0.61946243
i=2,t=21,2010q2 0.34593957
i=2,t=22,2010q3 -0.36476066
i=2,t=23,2010q4 0.11771764
i=2,t=24,2011q1 -0.26445692
i=2,t=25,2011q2 -0.15142643
i=2,t=26,2011q3 -0.27630004
i=2,t=27,2011q4 0.21920105
i=2,t=28,2012q1 -0.14452806
i=2,t=29,2012q2 -0.11795151
i=2,t=30,2012q3 -1.09601071
i=2,t=31,2012q4 -0.36964872
i=2,t=32,2013q1 0.03445872
i=2,t=33,2013q2 -0.36273371
i=2,t=34,2013q3 0.20371578
i=2,t=35,2013q4 0.09171287
$residuals[[3]]
[,1]
i=3,t=3,2005q4 0.276955304
i=3,t=4,2006q1 0.003447832
i=3,t=5,2006q2 0.458121999
i=3,t=6,2006q3 -0.066496275
i=3,t=7,2006q4 -0.030286296
i=3,t=8,2007q1 0.064869613
i=3,t=9,2007q2 0.586400798
i=3,t=10,2007q3 0.117059879
i=3,t=11,2007q4 0.127007934
i=3,t=12,2008q1 -0.108117305
i=3,t=13,2008q2 0.433679162
i=3,t=14,2008q3 -0.749972511
i=3,t=15,2008q4 -0.112924381
i=3,t=16,2009q1 0.344957567
i=3,t=17,2009q2 0.157436890
i=3,t=18,2009q3 -0.473204998
i=3,t=19,2009q4 -1.109710528
i=3,t=20,2010q1 0.016619063
i=3,t=21,2010q2 0.461716980
i=3,t=22,2010q3 -0.733644524
i=3,t=23,2010q4 0.731807310
i=3,t=24,2011q1 0.397476460
i=3,t=25,2011q2 -0.231405818
i=3,t=26,2011q3 -0.555445925
i=3,t=27,2011q4 0.761239990
i=3,t=28,2012q1 -0.016923075
i=3,t=29,2012q2 -0.264595797
i=3,t=30,2012q3 -0.631508797
i=3,t=31,2012q4 -0.458511525
i=3,t=32,2013q1 0.002688231
i=3,t=33,2013q2 0.301876874
i=3,t=34,2013q3 0.424372925
i=3,t=35,2013q4 -0.124987056
$residuals[[4]]
[,1]
i=4,t=3,2005q4 0.43935255
i=4,t=4,2006q1 -0.12766498
i=4,t=5,2006q2 -0.34599212
i=4,t=6,2006q3 -0.25197512
i=4,t=7,2006q4 -1.10731705
i=4,t=8,2007q1 -0.08081356
i=4,t=9,2007q2 -0.21543982
i=4,t=10,2007q3 0.20625854
i=4,t=11,2007q4 -0.08834092
i=4,t=12,2008q1 1.21362870
i=4,t=13,2008q2 -0.11845459
i=4,t=14,2008q3 -0.15021419
i=4,t=15,2008q4 0.45466205
i=4,t=16,2009q1 3.34018217
i=4,t=17,2009q2 -1.21198064
i=4,t=18,2009q3 -1.40543733
i=4,t=19,2009q4 0.07392237
i=4,t=20,2010q1 -0.68005379
i=4,t=21,2010q2 0.98032993
i=4,t=22,2010q3 -0.63419601
i=4,t=23,2010q4 0.77739712
i=4,t=24,2011q1 -0.84405258
i=4,t=25,2011q2 -0.14103946
i=4,t=26,2011q3 0.81084414
i=4,t=27,2011q4 0.38926667
i=4,t=28,2012q1 0.18548942
i=4,t=29,2012q2 0.50767521
i=4,t=30,2012q3 -0.31297382
i=4,t=31,2012q4 -0.79634881
i=4,t=32,2013q1 -0.22473140
i=4,t=33,2013q2 -0.09128778
i=4,t=34,2013q3 0.14646992
i=4,t=35,2013q4 -0.69716481
$residuals[[5]]
[,1]
i=5,t=3,2005q4 -0.64994342
i=5,t=4,2006q1 -0.44099373
i=5,t=5,2006q2 0.19730503
i=5,t=6,2006q3 0.17460290
i=5,t=7,2006q4 0.55312193
i=5,t=8,2007q1 0.04272684
i=5,t=9,2007q2 -0.01656373
i=5,t=10,2007q3 -0.82797683
i=5,t=11,2007q4 -0.54883372
i=5,t=12,2008q1 -0.63591484
i=5,t=13,2008q2 0.03651034
i=5,t=14,2008q3 -0.50075451
i=5,t=15,2008q4 1.46557503
i=5,t=16,2009q1 1.01797149
i=5,t=17,2009q2 0.12552509
i=5,t=18,2009q3 0.54728318
i=5,t=19,2009q4 1.30248670
i=5,t=20,2010q1 -1.71752724
i=5,t=21,2010q2 0.82110063
i=5,t=22,2010q3 0.91352476
i=5,t=23,2010q4 -0.66249605
i=5,t=24,2011q1 0.21056586
i=5,t=25,2011q2 0.30030135
i=5,t=26,2011q3 0.39885031
i=5,t=27,2011q4 0.88444033
i=5,t=28,2012q1 -0.31322920
i=5,t=29,2012q2 0.11167879
i=5,t=30,2012q3 -1.32507970
i=5,t=31,2012q4 -0.80688255
i=5,t=32,2013q1 -0.22970842
i=5,t=33,2013q2 -0.18465714
i=5,t=34,2013q3 -0.05597270
i=5,t=35,2013q4 -0.18703677
$residuals[[6]]
[,1]
i=6,t=3,2005q4 -0.55090067
i=6,t=4,2006q1 -0.35656781
i=6,t=5,2006q2 -0.05285696
i=6,t=6,2006q3 -0.72218756
i=6,t=7,2006q4 0.33861180
i=6,t=8,2007q1 -0.05694310
i=6,t=9,2007q2 0.70684262
i=6,t=10,2007q3 0.45337348
i=6,t=11,2007q4 0.14106840
i=6,t=12,2008q1 -0.72139055
i=6,t=13,2008q2 0.24619043
i=6,t=14,2008q3 -1.71421789
i=6,t=15,2008q4 0.80542725
i=6,t=16,2009q1 0.83114371
i=6,t=17,2009q2 -0.17448990
i=6,t=18,2009q3 1.40852703
i=6,t=19,2009q4 -0.78530293
i=6,t=20,2010q1 -1.62770176
i=6,t=21,2010q2 0.24957428
i=6,t=22,2010q3 0.79799896
i=6,t=23,2010q4 1.19530368
i=6,t=24,2011q1 0.68737060
i=6,t=25,2011q2 -0.02225626
i=6,t=26,2011q3 -0.01514204
i=6,t=27,2011q4 -0.07358283
i=6,t=28,2012q1 -1.14960561
i=6,t=29,2012q2 -1.41739764
i=6,t=30,2012q3 0.34972199
i=6,t=31,2012q4 0.03926383
i=6,t=32,2013q1 1.33751084
i=6,t=33,2013q2 -0.05408932
i=6,t=34,2013q3 0.06307056
i=6,t=35,2013q4 -0.15636664
$residuals[[7]]
[,1]
i=7,t=3,2005q4 0.28947885
i=7,t=4,2006q1 -0.07063205
i=7,t=5,2006q2 0.21848913
i=7,t=6,2006q3 0.70875811
i=7,t=7,2006q4 0.49910657
i=7,t=8,2007q1 0.43561372
i=7,t=9,2007q2 -0.10936579
i=7,t=10,2007q3 -0.09084952
i=7,t=11,2007q4 0.06139217
i=7,t=12,2008q1 -0.38210782
i=7,t=13,2008q2 -0.90448204
i=7,t=14,2008q3 0.42375343
i=7,t=15,2008q4 -0.22534739
i=7,t=16,2009q1 -0.04947686
i=7,t=17,2009q2 1.10786934
i=7,t=18,2009q3 0.13591977
i=7,t=19,2009q4 -1.43535870
i=7,t=20,2010q1 -0.79422416
i=7,t=21,2010q2 0.02357752
i=7,t=22,2010q3 0.57059078
i=7,t=23,2010q4 -0.72463897
i=7,t=24,2011q1 0.49099496
i=7,t=25,2011q2 0.64489241
i=7,t=26,2011q3 -0.05914003
i=7,t=27,2011q4 0.52630472
i=7,t=28,2012q1 -0.48127378
i=7,t=29,2012q2 0.23008485
i=7,t=30,2012q3 -0.10152833
i=7,t=31,2012q4 0.11177367
i=7,t=32,2013q1 -0.33967876
i=7,t=33,2013q2 -0.18346727
i=7,t=34,2013q3 -0.52917278
i=7,t=35,2013q4 0.00214424
$residuals[[8]]
[,1]
i=8,t=3,2005q4 0.30641436
i=8,t=4,2006q1 0.10477160
i=8,t=5,2006q2 0.52184968
i=8,t=6,2006q3 0.18014651
i=8,t=7,2006q4 -0.70181818
i=8,t=8,2007q1 0.07450220
i=8,t=9,2007q2 0.49279876
i=8,t=10,2007q3 0.07315398
i=8,t=11,2007q4 0.14591297
i=8,t=12,2008q1 -0.09541717
i=8,t=13,2008q2 0.71888478
i=8,t=14,2008q3 0.09620153
i=8,t=15,2008q4 -0.43180113
i=8,t=16,2009q1 -0.31424605
i=8,t=17,2009q2 0.09003509
i=8,t=18,2009q3 -0.35520970
i=8,t=19,2009q4 -0.51105586
i=8,t=20,2010q1 -0.23428649
i=8,t=21,2010q2 -0.22197307
i=8,t=22,2010q3 -0.07536075
i=8,t=23,2010q4 0.53919542
i=8,t=24,2011q1 0.40750981
i=8,t=25,2011q2 0.30621073
i=8,t=26,2011q3 -0.04912902
i=8,t=27,2011q4 0.89660880
i=8,t=28,2012q1 -0.21186299
i=8,t=29,2012q2 -0.05253682
i=8,t=30,2012q3 -0.48319926
i=8,t=31,2012q4 -0.29007320
i=8,t=32,2013q1 0.10533683
i=8,t=33,2013q2 -1.38755324
i=8,t=34,2013q3 0.69609341
i=8,t=35,2013q4 -0.34010352
$residuals[[9]]
[,1]
i=9,t=3,2005q4 -0.03163499
i=9,t=4,2006q1 0.13217580
i=9,t=5,2006q2 -0.27896294
i=9,t=6,2006q3 -0.05130029
i=9,t=7,2006q4 -0.04915133
i=9,t=8,2007q1 0.43231692
i=9,t=9,2007q2 0.04626241
i=9,t=10,2007q3 -0.03751935
i=9,t=11,2007q4 -0.33001563
i=9,t=12,2008q1 -0.13550630
i=9,t=13,2008q2 0.56160708
i=9,t=14,2008q3 -0.22490167
i=9,t=15,2008q4 -0.21373149
i=9,t=16,2009q1 -0.17147750
i=9,t=17,2009q2 -0.12929533
i=9,t=18,2009q3 -0.47632230
i=9,t=19,2009q4 -0.34548243
i=9,t=20,2010q1 0.66412166
i=9,t=21,2010q2 -0.35914338
i=9,t=22,2010q3 0.19937297
i=9,t=23,2010q4 0.31753333
i=9,t=24,2011q1 0.03606469
i=9,t=25,2011q2 -0.49196462
i=9,t=26,2011q3 0.59580898
i=9,t=27,2011q4 1.66144100
i=9,t=28,2012q1 -0.94941829
i=9,t=29,2012q2 0.09683310
i=9,t=30,2012q3 0.80518549
i=9,t=31,2012q4 -1.19511193
i=9,t=32,2013q1 -0.19562901
i=9,t=33,2013q2 1.12549412
i=9,t=34,2013q3 0.31979755
i=9,t=35,2013q4 -1.32744631
PooledMeanGroup documentation built on May 2, 2019, 12:40 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) References Examples
Description
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
Usage
1 | optimPMG(dLL, maxIter, TetaStart, vecSR, vecLR, dataset, quantity, const)
|
Arguments
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) |
Details
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
Value
$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 |
$residuals |
residuals |
Author(s)
Lech Kujawski, Piotr Zientara
References
Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # first import DataExp, i=1...9, T1=T2=...T9=35
data(DataExp)
DataExp[1:5,]
# then prepare lags and diffs using LagPanel and DiffPanel
y10=data.frame(y10=DataExp[,1], row.names=row.names(DataExp))
cpi=data.frame(cpi=DataExp[,7], row.names=row.names(DataExp))
dy10=DiffPanel(variable=y10, quantity=rep(35,9))
dopeness=DiffPanel(variable=DataExp[,6], quantity=rep(35,9))
ly10=LagPanel(variable=y10, quantity=rep(35,9))
diip=DiffPanel(variable=DataExp[,11], quantity=rep(35,9))
dcrisk=DiffPanel(variable=DataExp[,9], quantity=rep(35,9))
ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9))
dcpi=DiffPanel(variable=DataExp[,7], quantity=rep(35,9))
ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9))
ldebt=LagPanel(variable=DataExp[,4], quantity=rep(35,9))
# create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel)
dataPanel=cbind(y10, dy10, ly10, DataExp[,6], dopeness, diip,
DataExp[,11], ldcrisk, DataExp[,9], ddcpi, DataExp[,7])
dataPanel=data.frame(dataPanel)
names(dataPanel)=c("y10", "dy10", "ly10", "openess", "dopeness", "diip",
"iip", "ldcrisk", "crisk", "ddcpi", "cpi")
dataPanel[1:5,]
# prepare dataset and quantity for PMG or optimPMG functions using PanelNaOmit
dataPanel=PanelNaOmit(dataset=dataPanel, quantity=rep(35,9))
dataPanel$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
|
Example output
y10 y10spread riskavers debt deficit openess cpi growth
i=1,t=1,2005q2 4.91 1.78 0.4907953 0.478 -0.0446 0.749 1.001360 0.006
i=1,t=2,2005q3 4.57 1.50 0.5888259 0.468 -0.0436 0.754 0.999384 0.016
i=1,t=3,2005q4 5.16 1.82 0.4938176 0.471 -0.0487 0.776 1.003080 0.012
i=1,t=4,2006q1 4.79 1.15 0.7014965 0.490 -0.0215 0.804 1.002830 0.016
i=1,t=5,2006q2 5.55 1.59 0.6218348 0.494 -0.0430 0.819 1.011460 0.017
crisk urate iip iipnetto cagdp caresvs cds
i=1,t=1,2005q2 74.76667 0.1830 1.079462 0.4271757 -0.018 -0.04051902 16.21965
i=1,t=2,2005q3 74.80000 0.1773 1.093086 0.4364869 -0.028 -0.04921230 15.82511
i=1,t=3,2005q4 76.00000 0.1740 1.117122 0.4430011 -0.027 -0.03684523 16.41765
i=1,t=4,2006q1 76.86667 0.1793 1.113280 0.4436238 -0.036 -0.07054284 17.54188
i=1,t=5,2006q2 76.33333 0.1653 1.085001 0.4320550 -0.035 -0.06876323 23.02218
bidask
i=1,t=1,2005q2 0.000245231
i=1,t=2,2005q3 0.000252121
i=1,t=3,2005q4 0.000385385
i=1,t=4,2006q1 0.000323231
i=1,t=5,2006q2 0.000337833
y10 dy10 ly10 openess dopeness diip iip
i=1,t=1,2005q2 4.91 NA NA 0.749 NA NA 1.079462
i=1,t=2,2005q3 4.57 -0.34 4.91 0.754 0.005 0.013623126 1.093086
i=1,t=3,2005q4 5.16 0.59 4.57 0.776 0.022 0.024036799 1.117122
i=1,t=4,2006q1 4.79 -0.37 5.16 0.804 0.028 -0.003842008 1.113280
i=1,t=5,2006q2 5.55 0.76 4.79 0.819 0.015 -0.028278985 1.085001
ldcrisk crisk ddcpi cpi
i=1,t=1,2005q2 NA 74.76667 NA 1.001360
i=1,t=2,2005q3 NA 74.80000 NA 0.999384
i=1,t=3,2005q4 0.03333333 76.00000 0.005672 1.003080
i=1,t=4,2006q1 1.20000000 76.86667 -0.003946 1.002830
i=1,t=5,2006q2 0.86666667 76.33333 0.008880 1.011460
y10 dy10 ly10 openess dopeness diip iip
i=1,t=3,2005q4 5.16 0.59 4.57 0.776 0.022 0.024036799 1.117122
i=1,t=4,2006q1 4.79 -0.37 5.16 0.804 0.028 -0.003842008 1.113280
i=1,t=5,2006q2 5.55 0.76 4.79 0.819 0.015 -0.028278985 1.085001
i=1,t=6,2006q3 5.48 -0.07 5.55 0.836 0.017 0.020901595 1.105903
i=1,t=7,2006q4 5.14 -0.34 5.48 0.845 0.009 0.057480412 1.163383
ldcrisk crisk ddcpi cpi
i=1,t=3,2005q4 0.03333333 76.00000 0.005672 1.00308
i=1,t=4,2006q1 1.20000000 76.86667 -0.003946 1.00283
i=1,t=5,2006q2 0.86666667 76.33333 0.008880 1.01146
i=1,t=6,2006q3 -0.53333334 77.66667 -0.006170 1.01392
i=1,t=7,2006q4 1.33333334 76.83333 0.000010 1.01639
[1] 33 33 33 33 33 33 33 33 33
$LogL
[,1]
[1,] -246.3219
$dLogL
[,1]
[1,] 2.310685e-11
$i
[1] 44
$LR
Coef StdErr z P>|z| Low95% High95%
openess -14.2276819 1.8946444 -7.509421 5.928591e-14 -17.941117 -10.5142471
iip -23.8442534 3.7437226 -6.369129 1.901042e-10 -31.181815 -16.5066920
crisk -0.7571656 0.1489203 -5.084369 3.688511e-07 -1.049044 -0.4652872
cpi 27.5775310 3.6967195 7.460001 8.659740e-14 20.332094 34.8229682
$SR
$SR[[1]]
Coef(i=1) StdErr z P>|z| Low95% High95%
ec1 -0.03252643 0.02202632 -1.4767075 0.13975407 -0.07569723 0.010644364
dopeness 5.78138705 4.10533843 1.4082608 0.15905387 -2.26492841 13.827702513
diip -0.71549091 0.96819504 -0.7389946 0.45991027 -2.61311832 1.182136493
ldcrisk -0.11288716 0.05931871 -1.9030618 0.05703248 -0.22914969 0.003375363
ddcpi 10.25211574 5.61342341 1.8263571 0.06779649 -0.74999198 21.254223466
const 2.42855562 1.61978155 1.4993106 0.13379308 -0.74615787 5.603269120
$SR[[2]]
Coef(i=2) StdErr z P>|z| Low95%
ec2 0.005437134 0.03615882 0.15036814 0.88047417 -0.06543285
dopeness -0.105145562 1.06568476 -0.09866479 0.92140443 -2.19384932
diip 0.524903505 1.84165894 0.28501667 0.77563137 -3.08468168
ldcrisk -0.015035884 0.08619423 -0.17444188 0.86151820 -0.18397348
ddcpi -10.090130520 5.01905907 -2.01036298 0.04439279 -19.92730553
const -0.507038729 3.26265592 -0.15540674 0.87650067 -6.90172682
High95%
ec2 0.07630711
dopeness 1.98355819
diip 4.13448869
ldcrisk 0.15390171
ddcpi -0.25295551
const 5.88764936
$SR[[3]]
Coef(i=3) StdErr z P>|z| Low95% High95%
ec3 -0.02970175 0.03181964 -0.9334409 0.3505924 -0.0920671 0.03266359
dopeness -3.23702006 2.99810162 -1.0796899 0.2802803 -9.1131912 2.63915114
diip 1.60888923 1.98672122 0.8098213 0.4180429 -2.2850128 5.50279127
ldcrisk 0.04882033 0.08410627 0.5804601 0.5616044 -0.1160249 0.21366559
ddcpi 3.76680529 5.69811003 0.6610622 0.5085724 -7.4012852 14.93489573
const 2.63391417 2.84243026 0.9266416 0.3541126 -2.9371468 8.20497512
$SR[[4]]
Coef(i=4) StdErr z P>|z| Low95%
ec4 -0.01226321 0.007701996 -1.59221142 0.11133721 -0.02735884
dopeness -0.77268159 3.387631973 -0.22808900 0.81957705 -7.41231825
diip -0.01801169 0.475599099 -0.03787158 0.96979007 -0.95016879
ldcrisk 0.24045768 0.130799546 1.83836788 0.06600822 -0.01590472
ddcpi 11.39009171 10.456336572 1.08930041 0.27602143 -9.10395138
const 2.20722341 1.335799806 1.65236093 0.09846100 -0.41089610
High95%
ec4 0.002832429
dopeness 5.866955073
diip 0.914145417
ldcrisk 0.496820084
ddcpi 31.884134800
const 4.825342920
$SR[[5]]
Coef(i=5) StdErr z P>|z| Low95%
ec5 -0.108259329 0.02843895 -3.80672715 0.0001408180 -0.1639986
dopeness 1.062649919 4.92344303 0.21583471 0.8291165881 -8.5871211
diip 0.094635822 1.46369103 0.06465560 0.9484482145 -2.7741459
ldcrisk 0.002816201 0.07754723 0.03631595 0.9710304343 -0.1491736
ddcpi -5.590857580 8.67144028 -0.64474383 0.5190932105 -22.5865682
const 10.766531935 2.63696208 4.08293014 0.0000444714 5.5981812
High95%
ec5 -0.05252001
dopeness 10.71242095
diip 2.96341752
ldcrisk 0.15480598
ddcpi 11.40485307
const 15.93488263
$SR[[6]]
Coef(i=6) StdErr z P>|z| Low95% High95%
ec6 -0.6613952 0.1085913 -6.090681 1.124311e-09 -0.8742303 -0.4485601
dopeness 3.5862064 2.8373976 1.263907 2.062634e-01 -1.9749908 9.1474036
diip 9.5282203 4.2552125 2.239188 2.514369e-02 1.1881571 17.8682835
ldcrisk 0.3823801 0.1656203 2.308776 2.095603e-02 0.0577703 0.7069899
ddcpi -7.9951253 11.4489864 -0.698326 4.849733e-01 -30.4347263 14.4444756
const 53.7559053 9.3244673 5.765038 8.163961e-09 35.4802852 72.0315254
$SR[[7]]
Coef(i=7) StdErr z P>|z| Low95% High95%
ec7 -0.1194344 0.03979726 -3.0010701 0.002690327 -0.1974356 -0.04143317
dopeness 5.3567786 3.61597832 1.4814189 0.138494981 -1.7304086 12.44396592
diip 2.3327065 2.37340615 0.9828518 0.325680381 -2.3190841 6.98449710
ldcrisk -0.2740193 0.11969588 -2.2892963 0.022062142 -0.5086189 -0.03941972
ddcpi -9.1485216 7.02294527 -1.3026617 0.192690293 -22.9132414 4.61619818
const 7.4783925 2.74365245 2.7257069 0.006416394 2.1009325 12.85585249
$SR[[8]]
Coef(i=8) StdErr z P>|z| Low95% High95%
ec8 -0.02511285 0.02059801 -1.21918808 0.2227728 -0.06548422 0.01525851
dopeness -1.29529758 1.46579880 -0.88368034 0.3768688 -4.16821044 1.57761528
diip 0.01370812 1.18972876 0.01152205 0.9908069 -2.31811740 2.34553363
ldcrisk -0.04158100 0.09265287 -0.44878262 0.6535885 -0.22317727 0.14001528
ddcpi 3.57092127 9.91379909 0.36019706 0.7186998 -15.85976791 23.00161045
const 2.63783434 2.14297910 1.23091930 0.2183530 -1.56232751 6.83799619
$SR[[9]]
Coef(i=9) StdErr z P>|z| Low95% High95%
ec9 0.004225679 0.04286397 0.09858347 0.92146899 -0.07978616 0.08823752
dopeness -1.467380039 2.90674421 -0.50481911 0.61368589 -7.16449401 4.22973393
diip -1.286021868 2.04436890 -0.62905568 0.52931260 -5.29291128 2.72086754
ldcrisk 0.128452009 0.07472078 1.71909355 0.08559734 -0.01799803 0.27490204
ddcpi 10.996486420 5.83318469 1.88516000 0.05940824 -0.43634548 22.42931832
const -0.261817297 3.88473341 -0.06739646 0.94626608 -7.87575487 7.35212027
$DiagTests
R^2 Rbar^2 statJB probJB statGQ probGQ
i=1 0.30080890 0.17132907 0.71304772 7.001058e-01 1.171858 0.4050681554
i=2 0.11834714 -0.04492191 173.18220471 0.000000e+00 3.298566 0.0313337851
i=3 0.09058024 -0.07783083 1.24067570 5.377627e-01 2.437157 0.0858215193
i=4 0.20357789 0.05609231 51.82313445 5.581424e-12 3.450052 0.0269277283
i=5 0.55013643 0.46682836 0.03904985 9.806645e-01 1.583561 0.2385894048
i=6 0.78296297 0.74277092 0.82869197 6.607723e-01 1.210697 0.3853192999
i=7 0.45998113 0.35997763 1.59647161 4.501224e-01 1.424955 0.2922699445
i=8 0.08191103 -0.08810545 2.81556452 2.446853e-01 2.072518 0.1306343786
i=9 0.21505445 0.06969416 2.14641446 3.419102e-01 9.256368 0.0007294311
statCON probCON statBG(F) probBG(F) statBG(Chi^2) probBG(Chi^2)
i=1 0.1003408 0.951067335 0.04856346 0.9950357 0.1942539 0.99557776
i=2 3.1754160 0.204393548 0.10580439 0.9785707 0.4232176 0.98053226
i=3 7.7181030 0.021087992 0.50897006 0.7301476 2.0358802 0.72915943
i=4 7.1914359 0.027440974 0.31071878 0.8660435 1.2428751 0.87099031
i=5 4.4001322 0.110795835 0.35108547 0.8389765 1.4043419 0.84344003
i=6 5.8912389 0.052569485 1.67433552 0.2115386 6.6973421 0.15277321
i=7 3.9016396 0.142157481 0.17147565 0.9493820 0.6859026 0.95305597
i=8 1.3961242 0.497548578 0.37641769 0.8216791 1.5056708 0.82563649
i=9 11.9132322 0.002588657 3.48988983 0.0354738 13.9595593 0.00742525
$residuals
$residuals[[1]]
[,1]
i=1,t=3,2005q4 0.34318765
i=1,t=4,2006q1 -0.39040416
i=1,t=5,2006q2 0.58024534
i=1,t=6,2006q3 -0.15044840
i=1,t=7,2006q4 -0.17693157
i=1,t=8,2007q1 -0.11127411
i=1,t=9,2007q2 0.30474005
i=1,t=10,2007q3 0.30858050
i=1,t=11,2007q4 0.01052414
i=1,t=12,2008q1 0.14234326
i=1,t=13,2008q2 0.54540505
i=1,t=14,2008q3 -0.20241249
i=1,t=15,2008q4 -0.32335071
i=1,t=16,2009q1 0.14298986
i=1,t=17,2009q2 -0.36639824
i=1,t=18,2009q3 0.02550564
i=1,t=19,2009q4 0.21071933
i=1,t=20,2010q1 -0.31833937
i=1,t=21,2010q2 0.03916362
i=1,t=22,2010q3 -0.26555037
i=1,t=23,2010q4 0.57088649
i=1,t=24,2011q1 0.34986787
i=1,t=25,2011q2 -0.29012151
i=1,t=26,2011q3 -0.16939701
i=1,t=27,2011q4 -0.32368506
i=1,t=28,2012q1 -0.43015691
i=1,t=29,2012q2 -0.03391308
i=1,t=30,2012q3 0.11646648
i=1,t=31,2012q4 -0.69875724
i=1,t=32,2013q1 0.16561443
i=1,t=33,2013q2 -0.11225239
i=1,t=34,2013q3 0.46980457
i=1,t=35,2013q4 0.03734836
$residuals[[2]]
[,1]
i=2,t=3,2005q4 0.26138815
i=2,t=4,2006q1 0.53256047
i=2,t=5,2006q2 0.13915654
i=2,t=6,2006q3 -0.27397754
i=2,t=7,2006q4 0.19196413
i=2,t=8,2007q1 0.10095382
i=2,t=9,2007q2 0.05159331
i=2,t=10,2007q3 0.23508937
i=2,t=11,2007q4 0.53021788
i=2,t=12,2008q1 -0.22562174
i=2,t=13,2008q2 0.01823027
i=2,t=14,2008q3 -0.04268963
i=2,t=15,2008q4 2.51773433
i=2,t=16,2009q1 -0.10327622
i=2,t=17,2009q2 -0.48045656
i=2,t=18,2009q3 -0.04024994
i=2,t=19,2009q4 -0.65808308
i=2,t=20,2010q1 -0.61946243
i=2,t=21,2010q2 0.34593957
i=2,t=22,2010q3 -0.36476066
i=2,t=23,2010q4 0.11771764
i=2,t=24,2011q1 -0.26445692
i=2,t=25,2011q2 -0.15142643
i=2,t=26,2011q3 -0.27630004
i=2,t=27,2011q4 0.21920105
i=2,t=28,2012q1 -0.14452806
i=2,t=29,2012q2 -0.11795151
i=2,t=30,2012q3 -1.09601071
i=2,t=31,2012q4 -0.36964872
i=2,t=32,2013q1 0.03445872
i=2,t=33,2013q2 -0.36273371
i=2,t=34,2013q3 0.20371578
i=2,t=35,2013q4 0.09171287
$residuals[[3]]
[,1]
i=3,t=3,2005q4 0.276955304
i=3,t=4,2006q1 0.003447832
i=3,t=5,2006q2 0.458121999
i=3,t=6,2006q3 -0.066496275
i=3,t=7,2006q4 -0.030286296
i=3,t=8,2007q1 0.064869613
i=3,t=9,2007q2 0.586400798
i=3,t=10,2007q3 0.117059879
i=3,t=11,2007q4 0.127007934
i=3,t=12,2008q1 -0.108117305
i=3,t=13,2008q2 0.433679162
i=3,t=14,2008q3 -0.749972511
i=3,t=15,2008q4 -0.112924381
i=3,t=16,2009q1 0.344957567
i=3,t=17,2009q2 0.157436890
i=3,t=18,2009q3 -0.473204998
i=3,t=19,2009q4 -1.109710528
i=3,t=20,2010q1 0.016619063
i=3,t=21,2010q2 0.461716980
i=3,t=22,2010q3 -0.733644524
i=3,t=23,2010q4 0.731807310
i=3,t=24,2011q1 0.397476460
i=3,t=25,2011q2 -0.231405818
i=3,t=26,2011q3 -0.555445925
i=3,t=27,2011q4 0.761239990
i=3,t=28,2012q1 -0.016923075
i=3,t=29,2012q2 -0.264595797
i=3,t=30,2012q3 -0.631508797
i=3,t=31,2012q4 -0.458511525
i=3,t=32,2013q1 0.002688231
i=3,t=33,2013q2 0.301876874
i=3,t=34,2013q3 0.424372925
i=3,t=35,2013q4 -0.124987056
$residuals[[4]]
[,1]
i=4,t=3,2005q4 0.43935255
i=4,t=4,2006q1 -0.12766498
i=4,t=5,2006q2 -0.34599212
i=4,t=6,2006q3 -0.25197512
i=4,t=7,2006q4 -1.10731705
i=4,t=8,2007q1 -0.08081356
i=4,t=9,2007q2 -0.21543982
i=4,t=10,2007q3 0.20625854
i=4,t=11,2007q4 -0.08834092
i=4,t=12,2008q1 1.21362870
i=4,t=13,2008q2 -0.11845459
i=4,t=14,2008q3 -0.15021419
i=4,t=15,2008q4 0.45466205
i=4,t=16,2009q1 3.34018217
i=4,t=17,2009q2 -1.21198064
i=4,t=18,2009q3 -1.40543733
i=4,t=19,2009q4 0.07392237
i=4,t=20,2010q1 -0.68005379
i=4,t=21,2010q2 0.98032993
i=4,t=22,2010q3 -0.63419601
i=4,t=23,2010q4 0.77739712
i=4,t=24,2011q1 -0.84405258
i=4,t=25,2011q2 -0.14103946
i=4,t=26,2011q3 0.81084414
i=4,t=27,2011q4 0.38926667
i=4,t=28,2012q1 0.18548942
i=4,t=29,2012q2 0.50767521
i=4,t=30,2012q3 -0.31297382
i=4,t=31,2012q4 -0.79634881
i=4,t=32,2013q1 -0.22473140
i=4,t=33,2013q2 -0.09128778
i=4,t=34,2013q3 0.14646992
i=4,t=35,2013q4 -0.69716481
$residuals[[5]]
[,1]
i=5,t=3,2005q4 -0.64994342
i=5,t=4,2006q1 -0.44099373
i=5,t=5,2006q2 0.19730503
i=5,t=6,2006q3 0.17460290
i=5,t=7,2006q4 0.55312193
i=5,t=8,2007q1 0.04272684
i=5,t=9,2007q2 -0.01656373
i=5,t=10,2007q3 -0.82797683
i=5,t=11,2007q4 -0.54883372
i=5,t=12,2008q1 -0.63591484
i=5,t=13,2008q2 0.03651034
i=5,t=14,2008q3 -0.50075451
i=5,t=15,2008q4 1.46557503
i=5,t=16,2009q1 1.01797149
i=5,t=17,2009q2 0.12552509
i=5,t=18,2009q3 0.54728318
i=5,t=19,2009q4 1.30248670
i=5,t=20,2010q1 -1.71752724
i=5,t=21,2010q2 0.82110063
i=5,t=22,2010q3 0.91352476
i=5,t=23,2010q4 -0.66249605
i=5,t=24,2011q1 0.21056586
i=5,t=25,2011q2 0.30030135
i=5,t=26,2011q3 0.39885031
i=5,t=27,2011q4 0.88444033
i=5,t=28,2012q1 -0.31322920
i=5,t=29,2012q2 0.11167879
i=5,t=30,2012q3 -1.32507970
i=5,t=31,2012q4 -0.80688255
i=5,t=32,2013q1 -0.22970842
i=5,t=33,2013q2 -0.18465714
i=5,t=34,2013q3 -0.05597270
i=5,t=35,2013q4 -0.18703677
$residuals[[6]]
[,1]
i=6,t=3,2005q4 -0.55090067
i=6,t=4,2006q1 -0.35656781
i=6,t=5,2006q2 -0.05285696
i=6,t=6,2006q3 -0.72218756
i=6,t=7,2006q4 0.33861180
i=6,t=8,2007q1 -0.05694310
i=6,t=9,2007q2 0.70684262
i=6,t=10,2007q3 0.45337348
i=6,t=11,2007q4 0.14106840
i=6,t=12,2008q1 -0.72139055
i=6,t=13,2008q2 0.24619043
i=6,t=14,2008q3 -1.71421789
i=6,t=15,2008q4 0.80542725
i=6,t=16,2009q1 0.83114371
i=6,t=17,2009q2 -0.17448990
i=6,t=18,2009q3 1.40852703
i=6,t=19,2009q4 -0.78530293
i=6,t=20,2010q1 -1.62770176
i=6,t=21,2010q2 0.24957428
i=6,t=22,2010q3 0.79799896
i=6,t=23,2010q4 1.19530368
i=6,t=24,2011q1 0.68737060
i=6,t=25,2011q2 -0.02225626
i=6,t=26,2011q3 -0.01514204
i=6,t=27,2011q4 -0.07358283
i=6,t=28,2012q1 -1.14960561
i=6,t=29,2012q2 -1.41739764
i=6,t=30,2012q3 0.34972199
i=6,t=31,2012q4 0.03926383
i=6,t=32,2013q1 1.33751084
i=6,t=33,2013q2 -0.05408932
i=6,t=34,2013q3 0.06307056
i=6,t=35,2013q4 -0.15636664
$residuals[[7]]
[,1]
i=7,t=3,2005q4 0.28947885
i=7,t=4,2006q1 -0.07063205
i=7,t=5,2006q2 0.21848913
i=7,t=6,2006q3 0.70875811
i=7,t=7,2006q4 0.49910657
i=7,t=8,2007q1 0.43561372
i=7,t=9,2007q2 -0.10936579
i=7,t=10,2007q3 -0.09084952
i=7,t=11,2007q4 0.06139217
i=7,t=12,2008q1 -0.38210782
i=7,t=13,2008q2 -0.90448204
i=7,t=14,2008q3 0.42375343
i=7,t=15,2008q4 -0.22534739
i=7,t=16,2009q1 -0.04947686
i=7,t=17,2009q2 1.10786934
i=7,t=18,2009q3 0.13591977
i=7,t=19,2009q4 -1.43535870
i=7,t=20,2010q1 -0.79422416
i=7,t=21,2010q2 0.02357752
i=7,t=22,2010q3 0.57059078
i=7,t=23,2010q4 -0.72463897
i=7,t=24,2011q1 0.49099496
i=7,t=25,2011q2 0.64489241
i=7,t=26,2011q3 -0.05914003
i=7,t=27,2011q4 0.52630472
i=7,t=28,2012q1 -0.48127378
i=7,t=29,2012q2 0.23008485
i=7,t=30,2012q3 -0.10152833
i=7,t=31,2012q4 0.11177367
i=7,t=32,2013q1 -0.33967876
i=7,t=33,2013q2 -0.18346727
i=7,t=34,2013q3 -0.52917278
i=7,t=35,2013q4 0.00214424
$residuals[[8]]
[,1]
i=8,t=3,2005q4 0.30641436
i=8,t=4,2006q1 0.10477160
i=8,t=5,2006q2 0.52184968
i=8,t=6,2006q3 0.18014651
i=8,t=7,2006q4 -0.70181818
i=8,t=8,2007q1 0.07450220
i=8,t=9,2007q2 0.49279876
i=8,t=10,2007q3 0.07315398
i=8,t=11,2007q4 0.14591297
i=8,t=12,2008q1 -0.09541717
i=8,t=13,2008q2 0.71888478
i=8,t=14,2008q3 0.09620153
i=8,t=15,2008q4 -0.43180113
i=8,t=16,2009q1 -0.31424605
i=8,t=17,2009q2 0.09003509
i=8,t=18,2009q3 -0.35520970
i=8,t=19,2009q4 -0.51105586
i=8,t=20,2010q1 -0.23428649
i=8,t=21,2010q2 -0.22197307
i=8,t=22,2010q3 -0.07536075
i=8,t=23,2010q4 0.53919542
i=8,t=24,2011q1 0.40750981
i=8,t=25,2011q2 0.30621073
i=8,t=26,2011q3 -0.04912902
i=8,t=27,2011q4 0.89660880
i=8,t=28,2012q1 -0.21186299
i=8,t=29,2012q2 -0.05253682
i=8,t=30,2012q3 -0.48319926
i=8,t=31,2012q4 -0.29007320
i=8,t=32,2013q1 0.10533683
i=8,t=33,2013q2 -1.38755324
i=8,t=34,2013q3 0.69609341
i=8,t=35,2013q4 -0.34010352
$residuals[[9]]
[,1]
i=9,t=3,2005q4 -0.03163499
i=9,t=4,2006q1 0.13217580
i=9,t=5,2006q2 -0.27896294
i=9,t=6,2006q3 -0.05130029
i=9,t=7,2006q4 -0.04915133
i=9,t=8,2007q1 0.43231692
i=9,t=9,2007q2 0.04626241
i=9,t=10,2007q3 -0.03751935
i=9,t=11,2007q4 -0.33001563
i=9,t=12,2008q1 -0.13550630
i=9,t=13,2008q2 0.56160708
i=9,t=14,2008q3 -0.22490167
i=9,t=15,2008q4 -0.21373149
i=9,t=16,2009q1 -0.17147750
i=9,t=17,2009q2 -0.12929533
i=9,t=18,2009q3 -0.47632230
i=9,t=19,2009q4 -0.34548243
i=9,t=20,2010q1 0.66412166
i=9,t=21,2010q2 -0.35914338
i=9,t=22,2010q3 0.19937297
i=9,t=23,2010q4 0.31753333
i=9,t=24,2011q1 0.03606469
i=9,t=25,2011q2 -0.49196462
i=9,t=26,2011q3 0.59580898
i=9,t=27,2011q4 1.66144100
i=9,t=28,2012q1 -0.94941829
i=9,t=29,2012q2 0.09683310
i=9,t=30,2012q3 0.80518549
i=9,t=31,2012q4 -1.19511193
i=9,t=32,2013q1 -0.19562901
i=9,t=33,2013q2 1.12549412
i=9,t=34,2013q3 0.31979755
i=9,t=35,2013q4 -1.32744631
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