最近我遇到了一个问题，这个问题花了我很长时间才搞清楚，但最终却无法解决。我想在包pgmm中使用plm函数对包含180个国家和65个时段的横截面国家/地区数据进行GMM估算。这是我的代码：
pgmm(D_rcr ~ lag(D_rcr,1) +
eco_cycle + I(log(Human_trend)) +
I(log(capital_trend)) + I(log(rtfpna)) + exp_rate + urban +
industry + service| plm::lag(mpk3_delta,3:6),data= data_test,
index = c("country","year"),effect = "twoways",transformation = "ld")
数据如下：
country year D_rcr eco_cycle Human_trend capital_trend rtfpna exp_rate urban industry service
1000 Burkina Faso 1999 0.0074201618 0.0295545705 4.644064 23946.998 0.8284378 -8.221149e-06 17.166 25.19151 42.19550
1001 Burkina Faso 2000 -0.0046062428 -0.0085762554 4.781708 25026.203 0.8177401 -8.013943e-06 17.844 21.52736 45.66413
1002 Burkina Faso 2001 -0.0074698958 -0.0022468581 4.942214 26203.394 0.8430429 -4.433730e-06 18.540 19.47667 43.47496
1003 Burkina Faso 2002 -0.0072339948 -0.0180040290 5.102502 27513.395 0.8564266 -4.243651e-06 19.258 17.52184 43.92530
1004 Burkina Faso 2003 0.0208224248 -0.0013267292 5.262760 28994.841 0.8928111 -4.900598e-06 19.996 21.18051 41.74380
1005 Burkina Faso 2004 0.0077643394 -0.0164384391 5.424015 30686.577 0.9057222 -5.039807e-06 20.757 21.17522 44.30414
1006 Burkina Faso 2005 -0.0162568441 0.0079704026 5.588694 32625.279 0.9540021 -6.000714e-06 21.537 17.97970 42.98950
1007 Burkina Faso 2006 0.0157383040 0.0101814490 5.759905 34843.140 0.9746150 -6.004488e-06 22.339 17.62221 45.65378
1008 Burkina Faso 2007 0.0200791048 -0.0074020766 5.940701 37366.725 0.9920313 -5.925001e-06 23.163 18.95747 48.37608
1009 Burkina Faso 2008 -0.0329526715 -0.0083514921 6.134060 40213.051 1.0026470 -6.737820e-06 23.993 16.22030 43.57783
1010 Burkina Faso 2009 0.0108550329 -0.0364106100 6.341043 43385.046 0.9904070 -5.581967e-06 24.828 19.32466 45.10181
1011 Burkina Faso 2010 0.0003792556 -0.0105232997 6.561223 46865.511 1.0080181 -3.757856e-06 25.665 23.00269 41.38044
1012 Burkina Faso 2011 0.0036570272 -0.0008078762 6.808363 50612.776 1.0000000 -1.947466e-06 26.505 27.15270 39.00203
1013 Burkina Faso 2012 -0.0133615481 0.0088997716 7.066275 54562.488 0.9885733 -4.819380e-06 27.346 24.91152 40.03299
1014 Burkina Faso 2013 -0.0124167169 0.0180233629 7.332700 58635.384 0.9726224 -4.963807e-06 28.186 20.99917 43.38722
1015 Burkina Faso 2014 -0.0093625110 0.0183559642 7.605543 62756.115 0.9531422 -2.547616e-06 29.024 20.47991 44.29216
1016 Burundi 1980 -0.0076063659 -0.0518049023 2.122768 4760.103 0.8508636 -4.026274e-05 4.339 12.61903 25.13108
1017 Burundi 1981 0.0062886770 0.0123692536 2.204532 5003.674 0.9142978 -2.922222e-05 4.503 13.41068 25.27003
1018 Burundi 1982 -0.0073451957 -0.0326804079 2.286727 5257.374 0.8792791 -3.623259e-05 4.674 15.44782 27.69609
1019 Burundi 1983 -0.0048256924 -0.0422295228 2.369051 5513.472 0.8658228 -3.508869e-05 4.850 15.50037 27.25349
1020 Burundi 1984 -0.0083655241 -0.0846945313 2.450960 5763.198 0.8221024 -3.652248e-05 5.033 13.84020 26.02882
1021 Burundi 1985 0.0062427433 -0.0081527185 2.531672 5997.500 0.8820450 -2.695840e-05 5.221 13.00197 25.46080
1022 Burundi 1986 0.0085330972 -0.0050508112 2.610223 6208.165 0.8880036 -2.122831e-05 5.417 13.51567 27.95960
1023 Burundi 1987 0.0013978951 0.0048612717 2.685521 6388.418 0.8895743 -2.379938e-05 5.620 17.12692 27.76170
1024 Burundi 1988 0.0120351151 0.0273960217 2.756473 6533.799 0.9023147 -1.564180e-05 5.830 16.66728 29.08768
1025 Burundi 1989 -0.0040708811 0.0237706740 2.822166 6643.176 0.8862884 -1.854892e-05 6.047 19.66485 26.65657
1026 Burundi 1990 0.0031577402 0.0461310277 2.882089 6718.144 0.9082311 -2.401352e-05 6.271 18.96324 25.15806
1027 Burundi 1991 0.0053723287 0.0913896512 2.944615 6763.149 0.9525304 -2.121296e-05 6.455 19.59612 26.09317
1028 Burundi 1992 -0.0006242234 0.1118378705 3.002747 6784.165 0.9633930 -2.326152e-05 6.637 21.17273 25.29381
1029 Burundi 1993 -0.0140939288 0.0566603249 3.058046 6787.775 0.8787442 -2.704195e-05 6.823 22.44800 24.93331
1030 Burundi 1994 -0.0051914045 0.0446976884 3.112606 6780.856 0.8391036 -2.185189e-05 7.014 22.47806 30.74383
1031 Burundi 1995 -0.0101237974 -0.0044596262 3.168960 6770.888 0.7683206 -2.265241e-05 7.211 19.24821 32.60719
1032 Burundi 1996 -0.0120905700 -0.0733896784 3.230019 6765.241 0.6975604 -1.365663e-05 7.412 12.63033 30.14807
1033 Burundi 1997 -0.0018359105 -0.0465258303 3.298963 6770.493 0.6909751 -9.966291e-06 7.618 15.62753 36.64545
1034 Burundi 1998 0.0010393142 0.0151532770 3.379006 6792.311 0.7113460 -1.557693e-05 7.830 15.84338 36.12517
1035 Burundi 1999 -0.0087320046 0.0148961511 3.473067 6835.203 0.6845708 -1.100638e-05 8.036 16.20578 35.90429
1036 Burundi 2000 -0.0036065995 0.0062503238 3.583363 6902.337 0.6644523 -1.597629e-05 8.246 16.93214 35.00821
1037 Burundi 2001 -0.0015909534 0.0154145281 3.715491 6997.577 0.6616966 -1.724226e-05 8.461 16.49441 37.06893
1038 Burundi 2002 0.0000723279 0.0336641982 3.865710 7124.685 0.6701715 -1.645656e-05 8.682 16.69844 37.51544
1039 Burundi 2003 -0.0081492568 -0.0184660371 4.033378 7286.589 0.6383393 -1.518514e-05 8.908 17.03472 36.60505
1040 Burundi 2004 -0.0053202507 -0.0321812288 4.217188 7484.465 0.6333758 -1.595708e-05 9.139 17.70286 36.85237
1041 Burundi 2005 -0.0073075515 -0.1193560863 4.415421 7718.514 0.5999591 -2.871464e-05 9.375 18.45308 37.05039
1042 Burundi 2006 0.0953559671 -0.1570653899 4.626188 7989.549 0.6067449 -3.880530e-05 9.617 16.71110 38.94535
1043 Burundi 2007 0.0094422379 -0.1988202659 4.847617 8298.925 0.6223342 -3.338575e-05 9.864 18.03837 44.62553
1044 Burundi 2008 0.0217479374 -0.1709468848 5.077926 8647.895 0.6832167 -3.469164e-05 10.118 15.98312 43.42593
1045 Burundi 2009 0.0516023614 -0.0108370357 5.315478 9036.445 0.8509285 -2.165262e-05 10.376 16.63140 42.83632
1046 Burundi 2010 0.0128353068 0.0302280799 5.558862 9461.720 0.9318257 -1.805687e-05 10.641 16.70423 42.84727
1047 Burundi 2011 0.0164302478 0.0549153842 5.820920 9917.799 1.0000000 -1.674949e-05 10.912 16.89924 42.75338
1048 Burundi 2012 0.0177947374 0.0834531229 6.088302 10396.854 1.0781934 -1.448994e-05 11.189 16.88556 42.53204
1049 Burundi 2013 -0.0094655827 0.0443990294 6.360665 10890.133 1.0653062 -1.141332e-05 11.472 17.73397 42.43866
1050 Burundi 2014 -0.0061952542 0.0121767175 6.637850 11389.026 1.0576664 -1.038206e-05 11.761 18.31099 42.42737
错误是
Error in solve.default(crossprod(WX, t(crossprod(WX, A1)))) :
Lapack routine dgesv: system is exactly singular: U[10,10] = 0
有时在调整之后，即data_test <- dplyr::filter(data_test,!is.na(rtfpna))，错误将变为：
Error in solve.default(A1) :
system is computationally singular: reciprocal condition number = 1.14054e-16
或
Error in solve.default(crossprod(WX, t(crossprod(WX, A2)))) :
system is computationally singular: reciprocal condition number = 1.69599e-24
我认为pgmm函数1）无法很好地处理不平衡数据帧plm函数，尤其是当数据包含10％NA时。 2）当特征值太小时，求解函数没有用于求解逆矩阵的替代。另外，据我的同事主要在Stata上工作，Stata也没有这样的问题。所以我的问题是，如何解决这个问题，我的代码是正确的方向吗？
任何建议都会有所帮助。
答案 0 :(得分：0)
从提供的数据来看，这可能会导致错误：您的数据集不平衡。看来您对布隆迪的数据始于1980年，而布基纳法索的数据始于1999年。
我有同样的错误。在我的数据集中，我有1891年到1899年，但是缺少1892年-我忘记了清理数据，因此它是平衡的。当我卸下1891年时，问题解决了。
从直觉上讲这是有道理的：Sys-GMM使用高级延迟来检测第一个延迟。但是，如果随机丢失年份，则显然不能始终如一地工作。
当然，也可能是您的解释变量中具有高度相关的var。