无法修复 运行 "pvargmm" 中的内存不足问题
Cannot fix the lack of memory problem in running "pvargmm"
我的电脑使用 Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz 2.59 GHz 的 CPT。我的 RAM 内存大小也是 16 GB。
当我 运行 R 中的以下面板 VAR 模型“pvargmm”时，
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"))
我总是收到以下错误：
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
所以我尝试只使用两个因变量来查看内存是否可以承受，而不是我之前使用的六个因变量。
然后我仍然有内存错误，但形式如下：
Error in .dense2C(from) :
Cholmod error 'out of memory' at file ../Core/cholmod_memory.c, line 146
但我目前使用以下代码来提高内存：
options(java.parameters = "- Xmx800000000000000m")
memory.limit(size=8e+14)
我的 windows 是 64 位的，我的 R 程序也是 64 位的。
数据平衡，行数为 2060，没有缺失值。
前50行使用dput(data1)的片段如下：
> 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")
如果我用data1[1:50,]显示原始数据的前50行，如下所示：
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
请问我可以得到修复此错误的帮助吗？
不是答案，但这可能有助于其他人回答这个问题。我对此进行编码以重新创建@Eric正在使用的大小的data.frame。
#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)
这个问题的发生是因为这个操作有很多巨大的矩阵乘法%*%。我在创建 R 包时遇到过类似的问题。因此，我通过使用 panelvar 包的某些部分中的 Rcpp 代码更改该操作来应用类似的方法来解决此问题。
修改后的代码已上传到我的 GitHub 存储库 ==> https://github.com/zaenalium/panelvar.
要使用修改后的包，请运行此代码：
devtools::install_github('zaenalium/panelvar')
该代码已经过测试并与原始版本进行了比较，结果是相同的，并且减少了大量内存消耗。
如果还有问题，请告诉我。谢谢
注意：如果您使用的是WindowsOS，请先安装Rtools。
正如我在评论中试图暗示的那样，此行为是一项功能而非错误。在动态面板 GMM 中，最流行的过程是 Arellano-Bond，其中 t-1 中因变量的一阶差分由直到 t-2 的因变量的所有观测值进行检测。因此，仪器矩阵的大小增长得非常快：它的数量级为 $T^3$。
您使用的程序是将这个想法扩展到panel VAR，其中包括许多因变量、弱外生变量和当代变量，这使得这个问题更加严重。事实上更糟。
要了解更多详情，请参阅软件包的配套文件：
Sigmund, M., Ferstl, R. (2017) Panel Vector Autoregression in R with the Package panelvar
尤其是等式 (4) - (11)
解决方案是使用以下选项限制工具的最大滞后：
max_instr_dependent_vars 和 max_instr_predet_vars
这相应地减小了仪器矩阵的大小。从效率的角度来看，什么是最佳滞后数这个问题没有一个普遍的答案。任何数量的滞后都会产生一致的结果。我不鼓励设置最小滞后，即
min_instr_dependent_vars 和 min_instr_predet_vars 作为最近的观察结果与检测变量的相关性最高。将它们扔掉应该会降低估计的相对效率。
我的电脑使用 Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz 2.59 GHz 的 CPT。我的 RAM 内存大小也是 16 GB。 当我 运行 R 中的以下面板 VAR 模型“pvargmm”时，
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"))
我总是收到以下错误：
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
所以我尝试只使用两个因变量来查看内存是否可以承受，而不是我之前使用的六个因变量。
然后我仍然有内存错误，但形式如下：
Error in .dense2C(from) :
Cholmod error 'out of memory' at file ../Core/cholmod_memory.c, line 146
但我目前使用以下代码来提高内存：
options(java.parameters = "- Xmx800000000000000m")
memory.limit(size=8e+14)
我的 windows 是 64 位的，我的 R 程序也是 64 位的。
数据平衡，行数为 2060，没有缺失值。
前50行使用dput(data1)的片段如下：
> 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")
如果我用data1[1:50,]显示原始数据的前50行，如下所示：
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
请问我可以得到修复此错误的帮助吗？
不是答案，但这可能有助于其他人回答这个问题。我对此进行编码以重新创建@Eric正在使用的大小的data.frame。
#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)
这个问题的发生是因为这个操作有很多巨大的矩阵乘法%*%。我在创建 R 包时遇到过类似的问题。因此，我通过使用 panelvar 包的某些部分中的 Rcpp 代码更改该操作来应用类似的方法来解决此问题。
修改后的代码已上传到我的 GitHub 存储库 ==> https://github.com/zaenalium/panelvar.
要使用修改后的包，请运行此代码：
devtools::install_github('zaenalium/panelvar')
该代码已经过测试并与原始版本进行了比较，结果是相同的，并且减少了大量内存消耗。
如果还有问题，请告诉我。谢谢
注意：如果您使用的是WindowsOS，请先安装Rtools。
正如我在评论中试图暗示的那样，此行为是一项功能而非错误。在动态面板 GMM 中，最流行的过程是 Arellano-Bond，其中 t-1 中因变量的一阶差分由直到 t-2 的因变量的所有观测值进行检测。因此，仪器矩阵的大小增长得非常快：它的数量级为 $T^3$。
您使用的程序是将这个想法扩展到panel VAR，其中包括许多因变量、弱外生变量和当代变量，这使得这个问题更加严重。事实上更糟。
要了解更多详情，请参阅软件包的配套文件：
Sigmund, M., Ferstl, R. (2017) Panel Vector Autoregression in R with the Package panelvar
尤其是等式 (4) - (11)
解决方案是使用以下选项限制工具的最大滞后：
max_instr_dependent_vars 和 max_instr_predet_vars
这相应地减小了仪器矩阵的大小。从效率的角度来看，什么是最佳滞后数这个问题没有一个普遍的答案。任何数量的滞后都会产生一致的结果。我不鼓励设置最小滞后，即
min_instr_dependent_vars 和 min_instr_predet_vars 作为最近的观察结果与检测变量的相关性最高。将它们扔掉应该会降低估计的相对效率。