8

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。当我在 R 中运行以下面板 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，而不是我之前的六个因变量。

然后我仍然有内存错误，但形式不同，如下所示：

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尝试增加内存：

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。

数据与 2060 行平衡，没有缺失值。

使用 dput(data1) 的前 50 行代码片段如下：

    > 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，它显示如下：

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

请问我可以得到解决这个错误的帮助吗？

4

3 回答 3

4

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 包时遇到了类似的问题。因此，我应用了类似的方法来解决这个问题，方法是使用包的某些部分中的 Rcpp 代码更改该操作panelvar。

修改后的代码在这里上传到我的 GitHub 存储库 ==> https://github.com/zaenalium/panelvar。

要使用修改后的包，请运行以下代码：

devtools::install_github('zaenalium/panelvar')

该代码已经过测试并与原始版本进行比较，结果相同，也减少了很多内存消耗。

让我知道是否还有任何问题。谢谢。

注意：如果您使用的是 Windows 操作系统，请先安装 Rtools。

于 2021-12-14T03:05:21.800 回答
2

不是答案，但这可能会帮助其他人回答这个问题。我对此进行了编码以重新创建@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)
于 2021-12-14T00:24:34.187 回答
2

正如我试图在我的评论中暗示的那样，这种行为是一个特性，而不是一个错误。在动态面板 GMM 中，最流行的程序是 Arellano-Bond，其中 t-1 中因变量的一阶差异由直到 t-2 的因变量的所有观测值来检测。因此，工具矩阵的大小增长得非常快：它的数量级为 $T^3$。

您正在使用的程序是这个想法对面板 VAR 的扩展，其中包括许多因变量、弱外生变量和当代变量，这使这个问题变得更糟。事实上更糟。

要了解有关详细信息的更多信息，请参阅软件包的配套文件：

Sigmund, M., Ferstl, R. (2017) R 中的面板向量自回归与 Package panelvar

尤其是方程 (4) - (11)

解决方案是使用以下选项限制仪器的最大滞后：

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，最好的滞后数是多少这个问题的答案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并且min_instr_predet_vars由于最近的观察结果与工具变量高度相关。把它们扔掉应该会降低估计的相对效率。

于 2021-12-23T17:31:02.340 回答