# optimPMG: optimPMG In PooledMeanGroup: Pooled Mean Group Estimation of Dynamic Heterogenous Panels

## 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=,1], row.names=row.names(DataExp)) cpi=data.frame(cpi=,7], row.names=row.names(DataExp)) dy10=DiffPanel(variable=y10, quantity=rep(35,9)) dopeness=DiffPanel(variable=,6], quantity=rep(35,9)) ly10=LagPanel(variable=y10, quantity=rep(35,9)) diip=DiffPanel(variable=,11], quantity=rep(35,9)) dcrisk=DiffPanel(variable=,9], quantity=rep(35,9)) ldcrisk=LagPanel(variable=dcrisk, quantity=rep(35,9)) dcpi=DiffPanel(variable=,7], quantity=rep(35,9)) ddcpi=DiffPanel(variable=dcpi, quantity=rep(35,9)) ldebt=LagPanel(variable=,4], quantity=rep(35,9)) # create homogenous preliminary dataset (containing "NA" as a result of DiffPanel, LagPanel) dataPanel=cbind(y10, dy10, ly10, ,6], dopeness, diip, ,11], ldcrisk, ,9], ddcpi, ,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
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
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i=3,t=10,2007q3  0.117059879
i=3,t=11,2007q4  0.127007934
i=3,t=12,2008q1 -0.108117305
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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
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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
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\$residuals[[4]]
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i=4,t=19,2009q4  0.07392237
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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
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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]
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i=5,t=4,2006q1  -0.44099373
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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
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i=5,t=26,2011q3  0.39885031
i=5,t=27,2011q4  0.88444033
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i=5,t=29,2012q2  0.11167879
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i=5,t=32,2013q1 -0.22970842
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\$residuals[[6]]
[,1]
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i=6,t=10,2007q3  0.45337348
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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
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i=6,t=34,2013q3  0.06307056
i=6,t=35,2013q4 -0.15636664

\$residuals[[7]]
[,1]
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i=7,t=4,2006q1  -0.07063205
i=7,t=5,2006q2   0.21848913
i=7,t=6,2006q3   0.70875811
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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
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i=7,t=21,2010q2  0.02357752
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i=7,t=23,2010q4 -0.72463897
i=7,t=24,2011q1  0.49099496
i=7,t=25,2011q2  0.64489241
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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]
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i=8,t=4,2006q1   0.10477160
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i=8,t=8,2007q1   0.07450220
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i=8,t=10,2007q3  0.07315398
i=8,t=11,2007q4  0.14591297
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i=8,t=13,2008q2  0.71888478
i=8,t=14,2008q3  0.09620153
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i=8,t=17,2009q2  0.09003509
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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.