Cannot fix the lack of memory problem in running "pvargmm"

Question

My computer uses a CPT of Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz 2.59 GHz. Also my RAM memory size is 16 GB. When I run the following panel VAR model "pvargmm" in R,

library(imputeTS)
library("panelvar")
data1=data.frame(na.remove(cbind(Country,   Date,   x1, x2, x3, x4, x5, x6, x7, x8, x9, x10,    x11,    x12,    x13,    x14,x15,x16,x17,x18)))
                                                            
colnames(data1)<-cbind("Country",   "Date", "x1",   "x2",   "x3",   "x4",   "x5",   "x6",   "x7",   "x8",   "x9",   "x10",  "x11",  "x12",  "x13",  "x14","x15","x16","x17","x18")
                                                            
                                                            
regp=pvargmm(dependent_vars = c("x13","x2","x3","x4","x5","x6"),lags = 1,                                                           
             exog_vars = c("x14"),                                                          
             data = data1,steps= c("mstep"),                                                            
             panel_identifier = c("Country", "Date"))                                                           

I always get the following error:

Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'current' in selecting a method for function 'all.equal': cannot allocate vector of size 7.1 Gb

So I tried using only two dependent variables to see whether the memory can afford instead of six dependent variables I had earlier.

Then still I had the memory error but in different form as follows:

Error in .dense2C(from) :                                                                                                                                                                            
  Cholmod error 'out of memory' at file ../Core/cholmod_memory.c, line 146

But I currently use the following codes trying to boost the memory:

options(java.parameters = "- Xmx800000000000000m")
memory.limit(size=8e+14)

My windows is 64 bit and my R program is also 64 bit as well.

The data is balanced with 2060 number of rows with no missing values.

The snippet of the first 50 rows using dput(data1) are as follows:

    > dput(data1[1:50,])
structure(list(Country = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), Date = c(48, 
49, 52, 53, 54, 57, 59, 60, 64, 65, 69, 71, 86, 87, 88, 92, 101, 
102, 105, 106, 110, 113, 118, 119, 121, 123, 124, 125, 126, 127, 
129, 132, 133, 136, 137, 143, 144, 148, 149, 151, 152, 155, 156, 
157, 158, 161, 162, 166, 167, 168), x1 = c(0.014748522, 
0.118574701, 0.014776643, 0.110949861, 0.01481079, 0.118697229, 
0.109259581, 0.106920507, 0.09964718, 0.107359397, 0.100214624, 
0.101336456, 0.084556183, 0.109388135, 0.049318414, 0.083084846, 
0.101614654, 0.09898533, 0.08605765, 0.099262524, 0.097317145, 
0.094441761, 0.088059271, 0.101287244, 0.102545664, 0.106297825, 
0.097040955, 0.080330986, 0.103339081, 0.108313506, 0.100936735, 
0.10794291, 0.11167398, 0.111364648, 0.108089542, 0.110835368, 
0.112419189, 0.110474815, 0.112116887, 0.122428299, 0.114857692, 
0.115030436, 0.119601122, 0.114017072, 0.114926991, 0.113645471, 
0.117205805, 0.115805775, 0.11617135, 0.114326404), x2 = c(0.044647275, 
0.053976585, 0.030403218, 0.044558117, 0.063132462, 0.103456438, 
0.117170791, 0.104951921, 0.108145525, 0.107693444, 0.096528502, 
0.095931022, 0.083300776, 0.080563349, 0.076819818, 0.084028311, 
0.095892312, 0.096190825, 0.091091159, 0.090343147, 0.096242416, 
0.085306606, 0.085667078, 0.09251297, 0.105269247, 0.095251763, 
0.093446551, 0.096549008, 0.100387759, 0.101508899, 0.100509418, 
0.107830747, 0.109448071, 0.110830736, 0.109078427, 0.109318996, 
0.112848661, 0.110987973, 0.112196608, 0.115601933, 0.114478704, 
0.116686745, 0.116382225, 0.113006561, 0.109417021, 0.114979708, 
0.115397391, 0.115777083, 0.114273074, 0.111343996), x3 = c(25, 
25, 41.67, 75, 88.89, 93.52, 93.52, 93.52, 93.52, 93.52, 93.52, 
93.52, 90.74, 90.74, 90.74, 90.74, 90.74, 88.89, 88.89, 88.89, 
88.89, 88.89, 88.89, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 
92.59, 92.59, 90.74, 90.74, 90.74, 90.74, 88.89, 87.96, 87.96, 
87.96, 87.96, 87.96, 87.96, 87.96, 87.96, 87.96, 87.96, 87.96, 
87.96, 87.96, 87.96), x4 = c(0, 0, 0, 0, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 
    x5 = c(4.815325122, 4.815325122, 4.815325122, 
    4.815325122, 4.815325122, 4.815325122, 4.815325122, 4.815325122, 
    4.815325122, 4.815325122, 4.815325122, 4.815325122, 4.815325122, 
    4.815325122, 4.815325122, 4.815325122, 4.815325122, 4.815325122, 
    4.815325122, 4.815325122, 6.041347309, 6.041347309, 6.041347309, 
    6.041347309, 6.041347309, 6.041347309, 6.041347309, 6.041347309, 
    6.041347309, 6.041347309, 6.041347309, 6.041347309, 6.041347309, 
    6.041347309, 6.041347309, 6.041347309, 6.041347309, 6.041347309, 
    6.041347309, 6.041347309, 6.041347309, 6.041347309, 6.041347309, 
    6.041347309, 6.041347309, 6.041347309, 6.041347309, 6.041347309, 
    6.041347309, 6.041347309), x6 = c(0.7935, 
    0.7303, 0.5763, 0.5331, 0.4907, 0.3064, 0.2461, 0.1939, 0.1127, 
    0.096, 0.0012, -0.0282, -0.2368, -0.2497, -0.2622, -0.3073, 
    -0.4152, -0.425, -0.4503, -0.461, -0.5089, -0.5376, -0.5856, 
    -0.5956, -0.6147, -0.6337, -0.6429, -0.652, -0.6779, -0.6863, 
    -0.7033, -0.7285, -0.7366, -0.7596, -0.7673, -0.8152, -0.8226, 
    -0.8511, -0.8582, -0.8817, -0.8897, -0.913, -0.9206, -0.9285, 
    -0.9366, -0.9632, -0.9714, -1.0053, -1.0137, -1.0223), x7 = c(38, 
    38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 
    38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 
    38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 
    38, 38, 38, 38), X8 = c(-4.397966662, -6.304929628, 
    0.488928104, -6.304929628, 2.54486109, -3.296545249, 1.344450099, 
    3.782659735, -0.844822382, 4.83150399, -6.304929628, 2.159834672, 
    1.420876501, -3.354324242, 3.589037795, 1.061780955, 4.228123326, 
    -0.404162634, -5.056291726, 0.010801841, -5.328349718, -1.493660218, 
    -0.696633142, -4.105707617, -0.871840445, 5.29044444, -1.962123959, 
    0.586428005, 1.138495764, 1.753597336, 0.275856688, 2.375667683, 
    3.884202996, 1.723158621, -1.047778386, -2.310359726, 0.175022741, 
    -4.057753192, 1.331212028, -4.328358106, 2.086407315, -1.432959593, 
    -0.337455739, -1.618003031, -3.500966569, -0.620899578, -3.649420293, 
    -0.459085095, 2.257504544, 0.745875601), X9 = c(-4.302658422, 
    -6.110280589, 0.490125308, -6.110280589, 2.577519125, -3.242801379, 
    1.353528468, 3.855112975, -0.841263786, 4.950123801, -6.110280589, 
    2.183327935, 1.431018931, -3.298690566, 3.654221238, 1.067437852, 
    4.318781661, -0.403346996, -4.930588828, 0.010802424, -5.188881247, 
    -1.482560447, -0.694212278, -4.022565186, -0.868050937, 5.432889579, 
    -1.942999592, 0.58815086, 1.145001292, 1.769063124, 0.276237523, 
    2.404111465, 3.960624404, 1.738090643, -1.04230831, -2.28387527, 
    0.175175995, -3.976528721, 1.340112104, -4.236021695, 2.108324957, 
    -1.422741592, -0.336886997, -1.604983674, -3.440391694, -0.61897598, 
    -3.583631679, -0.45803291, 2.283179015, 0.748664182), X10 = c(0.022036057, 
    0.022099114, 0.022148854, 0.022295818, 0.022296321, 0.022417636, 
    0.022468635, 0.022471382, 0.022464479, 0.022474524, 0.022565, 
    0.022556508, 0.022628762, 0.022632952, 0.022636849, 0.022625484, 
    0.022663127, 0.022660331, 0.022713486, 0.022710519, 0.022745041, 
    0.022848741, 0.022858749, 0.022866118, 0.022865227, 0.022874749, 
    0.022874749, 0.022874749, 0.022874749, 0.022874749, 0.022873025, 
    0.022861229, 0.022866133, 0.022853027, 0.022850894, 0.022853874, 
    0.022850921, 0.022855289, 0.022853114, 0.022862262, 0.022861413, 
    0.022849419, 0.022846619, 0.022845453, 0.022850036, 0.022871213, 
    0.022874749, 0.022860246, 0.022859786, 0.022857052), x11 = c(0.02205167, 
    0.022114713, 0.022164428, 0.022311364, 0.022311864, 0.022433137, 
    0.022484114, 0.022486855, 0.022479932, 0.022489972, 0.022580409, 
    0.022571904, 0.022644075, 0.022648261, 0.022652155, 0.022640772, 
    0.022678364, 0.022675565, 0.022728696, 0.022725727, 0.022760221, 
    0.022863891, 0.022873875, 0.02288124, 0.022880342, 0.022889387, 
    0.022889387, 0.022889387, 0.022889387, 0.022889387, 0.022888096, 
    0.022876286, 0.022881185, 0.022868066, 0.02286593, 0.022868884, 
    0.022865929, 0.022870278, 0.0228681, 0.022877231, 0.022876379, 
    0.022864371, 0.022861568, 0.022860399, 0.022864979, 0.022886138, 
    0.022889387, 0.022875151, 0.022874688, 0.022871951), x12 = c(0.021513181, 
    0.021571753, 0.021617452, 0.02174688, 0.021747569, 0.021882247, 
    0.021932113, 0.021935407, 0.021929198, 0.021940171, 0.022036504, 
    0.022028441, 0.022112581, 0.02211688, 0.022121171, 0.022110325, 
    0.022152497, 0.022149788, 0.022207397, 0.022204502, 0.022237638, 
    0.022350023, 0.022361011, 0.022368394, 0.022367831, 0.022392916, 
    0.022392916, 0.022392916, 0.022385136, 0.022383687, 0.022381105, 
    0.022369664, 0.022375024, 0.022362253, 0.02236023, 0.022365686, 
    0.022362796, 0.022367793, 0.022365675, 0.022375336, 0.022374587, 
    0.022363052, 0.022360332, 0.022359293, 0.022363957, 0.022387616, 
    0.022392877, 0.022377085, 0.02237674, 0.022374056), x13 = c(0.021528877, 
    0.021587435, 0.021633108, 0.021762508, 0.021763194, 0.021897824, 
    0.021947669, 0.021950955, 0.021944726, 0.021955694, 0.022051985, 
    0.022043909, 0.022127962, 0.022132257, 0.022136544, 0.02212568, 
    0.022167799, 0.022165088, 0.022222671, 0.022219773, 0.022252881, 
    0.022365232, 0.022376196, 0.022383574, 0.022383005, 0.022407741, 
    0.022407741, 0.022407741, 0.022400273, 0.022398821, 0.022396232, 
    0.022384778, 0.022390134, 0.022377348, 0.022375323, 0.022380752, 
    0.02237786, 0.022382837, 0.022380717, 0.022390361, 0.022389608, 
    0.02237806, 0.022375337, 0.022374295, 0.022378955, 0.022402595, 
    0.022407741, 0.022392044, 0.022391696, 0.022389009), x14 = c(355.7064977, 
    355.7064977, 355.7064977, 355.7064977, 355.7064977, 355.7064977, 
    355.7064977, 366.871849, 366.871849, 366.871849, 366.871849, 
    366.871849, 436.6764361, 436.6764361, 436.6764361, 436.6764361, 
    343.7874609, 343.7874609, 343.7874609, 343.7874609, 343.7874609, 
    343.7874609, 343.7874609, 343.7874609, 351.4579307, 351.4579307, 
    351.4579307, 351.4579307, 351.4579307, 351.4579307, 351.4579307, 
    351.4579307, 351.4579307, 351.4579307, 351.4579307, 313.8276295, 
    313.8276295, 313.8276295, 313.8276295, 313.8276295, 313.8276295, 
    313.8276295, 313.8276295, 313.8276295, 313.8276295, 299.7095158, 
    299.7095158, 299.7095158, 299.7095158, 299.7095158), x15 = c(13, 
    13, 13, 13, 13, 13, 13, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, 
    -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, -1.5, 
    -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, 
    -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, 
    -5.5, -5.5, -5.5, -5.5, -5.5, -5.5, -5.5), x16 = c(2, 2, 
    2, 2, 2, 2, 2, 3.3, 3.3, 3.3, 3.3, 3.3, 1.5, 1.5, 1.5, 1.5, 
    1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2.2, 2.2, 2.2, 2.2, 2.2, 
    2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 1.9, 1.9, 1.9, 1.9, 1.9, 
    1.9, 1.9, 1.9, 1.9, 1.9, 2.7, 2.7, 2.7, 2.7, 2.7), x17 = c(53.9, 
    75.47, 75.91, 75.91, 72, 61, 57.08, 57.06, 46.7, 43.35, 40.11, 
    43.83, 33.04, 35.28, 32.61, 27.99, 25.66, 25.81, 27.57, 27.57, 
    33.47, 31.77, 31.78, 30.43, 27.68, 27.94, 29.43, 28.08, 32.19, 
    29.52, 28, 24.84, 24.32, 24.74, 25.44, 22.99, 22.65, 22.28, 
    22.13, 21.51, 22.54, 22.37, 22.03, 23.27, 24.47, 26.12, 26.57, 
    31.46, 28.81, 29.71), x18 = c(13.95348837, 40.01855288, 
    -8.199298585, 0.711368726, -5.820797907, -4.61297889, -12.9081477, 
    6.574523721, 3.227232538, -7.173447537, -1.787463271, 14.88859764, 
    19.84040624, 6.779661017, -7.568027211, -8.319685555, -4.396423249, 
    0.58456742, 6.819062379, 0, -0.594000594, -9.538724374, -8.494097322, 
    -4.247954688, -3.284416492, 0.939306358, 5.33285612, -4.587155963, 
    17.95529498, -8.294501398, 0.864553314, 1.553556827, -2.093397746, 
    -4.256965944, 2.829426031, -3.240740741, -1.478903871, -7.282563462, 
    -0.673249551, 0.74941452, 4.788470479, -0.754214729, -1.519892713, 
    5.628688153, 5.156854319, -1.098068913, 1.722817764, 2.308943089, 
    -8.423394787, 3.123915307)), row.names = c(NA, 50L), class = "data.frame")

If I show the first 50 rows of the raw data itself with data1[1:50,], it shows as below:

Country Date    x1  x2  x3  x4  x5  x6  x7  x8  x9  x10 x11 x12 x13 x14 x15 x16 x17 x18
1   48  0.01474852  0.04464728  25  0   4.815325    0.7935  38  -4.39796666 -4.30265842 0.02203606  0.02205167  0.02151318  0.02152888  355.7065    13  2   53.9    13.9534884
1   49  0.1185747   0.05397659  25  0   4.815325    0.7303  38  -6.30492963 -6.11028059 0.02209911  0.02211471  0.02157175  0.02158743  355.7065    13  2   75.47   40.0185529
1   52  0.01477664  0.03040322  41.67   0   4.815325    0.5763  38  0.4889281   0.49012531  0.02214885  0.02216443  0.02161745  0.02163311  355.7065    13  2   75.91   -8.1992986
1   53  0.11094986  0.04455812  75  0   4.815325    0.5331  38  -6.30492963 -6.11028059 0.02229582  0.02231136  0.02174688  0.02176251  355.7065    13  2   75.91   0.7113687
1   54  0.01481079  0.06313246  88.89   1   4.815325    0.4907  38  2.54486109  2.57751912  0.02229632  0.02231186  0.02174757  0.02176319  355.7065    13  2   72  -5.8207979
1   57  0.11869723  0.10345644  93.52   1   4.815325    0.3064  38  -3.29654525 -3.24280138 0.02241764  0.02243314  0.02188225  0.02189782  355.7065    13  2   61  -4.6129789
1   59  0.10925958  0.11717079  93.52   1   4.815325    0.2461  38  1.3444501   1.35352847  0.02246864  0.02248411  0.02193211  0.02194767  355.7065    13  2   57.08   -12.9081477
1   60  0.10692051  0.10495192  93.52   1   4.815325    0.1939  38  3.78265974  3.85511297  0.02247138  0.02248686  0.02193541  0.02195096  366.8718    -1.5    3.3 57.06   6.5745237
1   64  0.09964718  0.10814553  93.52   1   4.815325    0.1127  38  -0.84482238 -0.84126379 0.02246448  0.02247993  0.0219292   0.02194473  366.8718    -1.5    3.3 46.7    3.2272325
1   65  0.1073594   0.10769344  93.52   1   4.815325    0.096   38  4.83150399  4.9501238   0.02247452  0.02248997  0.02194017  0.02195569  366.8718    -1.5    3.3 43.35   -7.1734475
1   69  0.10021462  0.0965285   93.52   1   4.815325    0.0012  38  -6.30492963 -6.11028059 0.022565    0.02258041  0.0220365   0.02205198  366.8718    -1.5    3.3 40.11   -1.7874633
1   71  0.10133646  0.09593102  93.52   1   4.815325    -0.0282 38  2.15983467  2.18332793  0.02255651  0.0225719   0.02202844  0.02204391  366.8718    -1.5    3.3 43.83   14.8885976
1   86  0.08455618  0.08330078  90.74   1   4.815325    -0.2368 38  1.4208765   1.43101893  0.02262876  0.02264407  0.02211258  0.02212796  436.6764    -1.5    1.5 33.04   19.8404062
1   87  0.10938813  0.08056335  90.74   1   4.815325    -0.2497 38  -3.35432424 -3.29869057 0.02263295  0.02264826  0.02211688  0.02213226  436.6764    -1.5    1.5 35.28   6.779661
1   88  0.04931841  0.07681982  90.74   1   4.815325    -0.2622 38  3.58903779  3.65422124  0.02263685  0.02265216  0.02212117  0.02213654  436.6764    -1.5    1.5 32.61   -7.5680272
1   92  0.08308485  0.08402831  90.74   1   4.815325    -0.3073 38  1.06178095  1.06743785  0.02262548  0.02264077  0.02211033  0.02212568  436.6764    -1.5    1.5 27.99   -8.3196856
1   101 0.10161465  0.09589231  90.74   1   4.815325    -0.4152 38  4.22812333  4.31878166  0.02266313  0.02267836  0.0221525   0.0221678   343.7875    -1.5    1.5 25.66   -4.3964232
1   102 0.09898533  0.09619082  88.89   1   4.815325    -0.425  38  -0.40416263 -0.403347   0.02266033  0.02267557  0.02214979  0.02216509  343.7875    -1.5    1.5 25.81   0.5845674
1   105 0.08605765  0.09109116  88.89   1   4.815325    -0.4503 38  -5.05629173 -4.93058883 0.02271349  0.0227287   0.0222074   0.02222267  343.7875    -1.5    1.5 27.57   6.8190624
1   106 0.09926252  0.09034315  88.89   1   4.815325    -0.461  38  0.01080184  0.01080242  0.02271052  0.02272573  0.0222045   0.02221977  343.7875    -1.5    1.5 27.57   0
1   110 0.09731714  0.09624242  88.89   1   6.041347    -0.5089 38  -5.32834972 -5.18888125 0.02274504  0.02276022  0.02223764  0.02225288  343.7875    -1.5    1.5 33.47   -0.5940006
1   113 0.09444176  0.08530661  88.89   1   6.041347    -0.5376 38  -1.49366022 -1.48256045 0.02284874  0.02286389  0.02235002  0.02236523  343.7875    -1.5    1.5 31.77   -9.5387244
1   118 0.08805927  0.08566708  88.89   1   6.041347    -0.5856 38  -0.69663314 -0.69421228 0.02285875  0.02287387  0.02236101  0.0223762   343.7875    -1.5    1.5 31.78   -8.4940973
1   119 0.10128724  0.09251297  92.59   1   6.041347    -0.5956 38  -4.10570762 -4.02256519 0.02286612  0.02288124  0.02236839  0.02238357  343.7875    -5.5    2.2 30.43   -4.2479547
1   121 0.10254566  0.10526925  92.59   1   6.041347    -0.6147 38  -0.87184045 -0.86805094 0.02286523  0.02288034  0.02236783  0.02238301  351.4579    -5.5    2.2 27.68   -3.2844165
1   123 0.10629782  0.09525176  92.59   1   6.041347    -0.6337 38  5.29044444  5.43288958  0.02287475  0.02288939  0.02239292  0.02240774  351.4579    -5.5    2.2 27.94   0.9393064
1   124 0.09704095  0.09344655  92.59   1   6.041347    -0.6429 38  -1.96212396 -1.94299959 0.02287475  0.02288939  0.02239292  0.02240774  351.4579    -5.5    2.2 29.43   5.3328561
1   125 0.08033099  0.09654901  92.59   1   6.041347    -0.652  38  0.58642801  0.58815086  0.02287475  0.02288939  0.02239292  0.02240774  351.4579    -5.5    2.2 28.08   -4.587156
1   126 0.10333908  0.10038776  92.59   1   6.041347    -0.6779 38  1.13849576  1.14500129  0.02287475  0.02288939  0.02238514  0.02240027  351.4579    -5.5    2.2 32.19   17.955295
1   127 0.10831351  0.1015089   92.59   1   6.041347    -0.6863 38  1.75359734  1.76906312  0.02287475  0.02288939  0.02238369  0.02239882  351.4579    -5.5    2.2 29.52   -8.2945014
1   129 0.10093673  0.10050942  92.59   1   6.041347    -0.7033 38  0.27585669  0.27623752  0.02287303  0.0228881   0.0223811   0.02239623  351.4579    -5.5    2.2 28  0.8645533
1   132 0.10794291  0.10783075  90.74   1   6.041347    -0.7285 38  2.37566768  2.40411147  0.02286123  0.02287629  0.02236966  0.02238478  351.4579    -5.5    2.2 24.84   1.5535568
1   133 0.11167398  0.10944807  90.74   1   6.041347    -0.7366 38  3.884203    3.9606244   0.02286613  0.02288118  0.02237502  0.02239013  351.4579    -5.5    2.2 24.32   -2.0933977
1   136 0.11136465  0.11083074  90.74   1   6.041347    -0.7596 38  1.72315862  1.73809064  0.02285303  0.02286807  0.02236225  0.02237735  351.4579    -5.5    2.2 24.74   -4.2569659
1   137 0.10808954  0.10907843  90.74   1   6.041347    -0.7673 38  -1.04777839 -1.04230831 0.02285089  0.02286593  0.02236023  0.02237532  351.4579    -5.5    2.2 25.44   2.829426
1   143 0.11083537  0.109319    88.89   1   6.041347    -0.8152 38  -2.31035973 -2.28387527 0.02285387  0.02286888  0.02236569  0.02238075  313.8276    -5.5    1.9 22.99   -3.2407407
1   144 0.11241919  0.11284866  87.96   1   6.041347    -0.8226 38  0.17502274  0.175176    0.02285092  0.02286593  0.0223628   0.02237786  313.8276    -5.5    1.9 22.65   -1.4789039
1   148 0.11047482  0.11098797  87.96   1   6.041347    -0.8511 38  -4.05775319 -3.97652872 0.02285529  0.02287028  0.02236779  0.02238284  313.8276    -5.5    1.9 22.28   -7.2825635
1   149 0.11211689  0.11219661  87.96   1   6.041347    -0.8582 38  1.33121203  1.3401121   0.02285311  0.0228681   0.02236568  0.02238072  313.8276    -5.5    1.9 22.13   -0.6732496
1   151 0.1224283   0.11560193  87.96   1   6.041347    -0.8817 38  -4.32835811 -4.23602169 0.02286226  0.02287723  0.02237534  0.02239036  313.8276    -5.5    1.9 21.51   0.7494145
1   152 0.11485769  0.1144787   87.96   1   6.041347    -0.8897 38  2.08640732  2.10832496  0.02286141  0.02287638  0.02237459  0.02238961  313.8276    -5.5    1.9 22.54   4.7884705
1   155 0.11503044  0.11668674  87.96   1   6.041347    -0.913  38  -1.43295959 -1.42274159 0.02284942  0.02286437  0.02236305  0.02237806  313.8276    -5.5    1.9 22.37   -0.7542147
1   156 0.11960112  0.11638223  87.96   1   6.041347    -0.9206 38  -0.33745574 -0.336887   0.02284662  0.02286157  0.02236033  0.02237534  313.8276    -5.5    1.9 22.03   -1.5198927
1   157 0.11401707  0.11300656  87.96   1   6.041347    -0.9285 38  -1.61800303 -1.60498367 0.02284545  0.0228604   0.02235929  0.02237429  313.8276    -5.5    1.9 23.27   5.6286882
1   158 0.11492699  0.10941702  87.96   1   6.041347    -0.9366 38  -3.50096657 -3.44039169 0.02285004  0.02286498  0.02236396  0.02237895  313.8276    -5.5    1.9 24.47   5.1568543
1   161 0.11364547  0.11497971  87.96   1   6.041347    -0.9632 38  -0.62089958 -0.61897598 0.02287121  0.02288614  0.02238762  0.0224026   299.7095    -5.5    2.7 26.12   -1.0980689
1   162 0.1172058   0.11539739  87.96   1   6.041347    -0.9714 38  -3.64942029 -3.58363168 0.02287475  0.02288939  0.02239288  0.02240774  299.7095    -5.5    2.7 26.57   1.7228178
1   166 0.11580577  0.11577708  87.96   1   6.041347    -1.0053 38  -0.45908509 -0.45803291 0.02286025  0.02287515  0.02237709  0.02239204  299.7095    -5.5    2.7 31.46   2.3089431
1   167 0.11617135  0.11427307  87.96   1   6.041347    -1.0137 38  2.25750454  2.28317901  0.02285979  0.02287469  0.02237674  0.0223917   299.7095    -5.5    2.7 28.81   -8.4233948
1   168 0.1143264   0.111344    87.96   1   6.041347    -1.0223 38  0.7458756   0.74866418  0.02285705  0.02287195  0.02237406  0.02238901  299.7095    -5.5    2.7 29.71   3.1239153

May I get help on fixing this error please?

Answer 1

The problem happened because there is a lot of huge matrix multiplication with this operation %*%. I have faced a similar problem when creating an R package. Thus, I apply a similar approach to solve this problem by changing that operation with the Rcpp code in some part of panelvar package.

The modified code was uploaded to my GitHub repository here ==> https://github.com/zaenalium/panelvar.

To use that modified package please run this code:

devtools::install_github('zaenalium/panelvar')

That code has been tested and compared to the original version, the result is identical, also it reduces a lot of memory consumption.

Let me know if there is still any problem. Thanks.

Note: if you are using Windows OS, please install Rtools first.

Answer 2

As I tried to allude in my comment, this behavior is a feature and not a bug. In dynamic panel GMM the most popular procedure is the Arellano-Bond where the first difference of the dependent variable in t-1 is instrumented by all the observations of the dependent variable up to t-2. Therefore the size of the instrument matrix grows quite rapidly: it is of order $T^3$.

The procedure you are using is an extension of this idea to panel VAR, which includes many dependent variables, weakly exogenous variables and contemporary variables which makes this issue even worse. Much worse in fact.

To learn more about the details consult the companion paper to the package:

Sigmund, M., Ferstl, R. (2017) Panel Vector Autoregression in R with the Package panelvar

Especially equations (4) - (11)

The solution is to limit the maximal lag of the instrument using the options:

max_instr_dependent_vars and max_instr_predet_vars

This decreases size of the instrument matrix accordingly. From the point of view of efficiency the answer to the question what is the best number of lags does not have a general answer. Any number of lags yields a consistent result. I would discourage setting minimum lags i.e.

min_instr_dependent_vars and min_instr_predet_vars as the most recent observations are most highly correlated with the instrumented variable. Throwing them out should worsen the relative efficiency of the estimate.

Answer 3

Not an answer, but this might help someone else answer this. I coded this to re-create a data.frame of the size @Eric is working with.

#create example dataset
#OP said data was 20 x 2060
#Creating sample with 5 countries and 412 dates 
#(not sure of original number of distinct dates and countries, so just picked some numbers)

Country <- rep(1:5, 412)
Date <-   as.integer(0:2059/5)

xdata_matrix<-matrix(data = runif(2060*18, min = -100, max = 100),
                    nrow = 2060,
                    ncol = 18)
colnames(xdata_matrix)<-  paste0("x", 1:18)   
data1<- data.frame(Country, Date, xdata_matrix)
rm(Country, Date, xdata_matrix)