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?
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.