I am trying to run a pgmm regression (Arellano Bond estimator) following the example online with the EmplUK dataset.
My dataset is unbalanced, with some missing values (that I also removed, without any difference). This is the paste from R' dataframe.
row.names ID Year p I
1 23 1 1992 NA NA
2 22 1 1993 17.01 NA
3 21 1 1994 15.86 NA
4 20 1 1995 17.02 7.512347
5 19 1 1996 20.64 7.685104
6 18 1 1997 19.11 12.730282
7 17 1 1998 12.76 12.633871
8 16 1 1999 17.90 7.416381
9 15 1 2000 28.66 6.396114
10 14 1 2001 24.46 9.213729
11 13 1 2002 24.99 20.117159
12 12 1 2003 28.85 11.117816
13 11 1 2004 38.26 11.242638
14 10 1 2005 54.57 13.015168
15 9 1 2006 65.16 18.507212
16 8 1 2007 72.44 18.875281
17 7 1 2008 96.94 24.459170
18 6 1 2009 61.74 21.332035
19 5 1 2010 79.61 17.119038
20 4 1 2011 111.26 16.941914
21 3 1 2012 111.63 19.964875
22 2 1 2013 108.56 28.863894
23 1 1 2014 99.03 15.182615
24 45 2 1993 17.01 NA
25 44 2 1994 15.86 NA
26 43 2 1995 17.02 NA
27 42 2 1996 20.64 NA
28 41 2 1997 19.11 NA
29 40 2 1998 12.76 NA
30 39 2 1999 17.90 11.428262
31 38 2 2000 28.66 20.232613
32 37 2 2001 24.46 25.811754
33 36 2 2002 24.99 18.959958
34 35 2 2003 28.85 20.767074
35 34 2 2004 38.26 29.260406
36 33 2 2005 54.57 25.837434
37 32 2 2006 65.16 32.675618
38 31 2 2007 72.44 48.415190
39 30 2 2008 96.94 42.444435
40 29 2 2009 61.74 40.047462
41 28 2 2010 79.61 49.090816
42 27 2 2011 111.26 53.828050
43 26 2 2012 111.63 61.684020
44 25 2 2013 108.56 68.394140
45 24 2 2014 99.03 55.738584
46 76 3 1984 NA NA
47 75 3 1985 NA NA
48 74 3 1986 NA NA
49 73 3 1987 18.53 NA
50 72 3 1988 14.91 NA
51 71 3 1989 18.23 NA
52 70 3 1990 23.76 17.046268
53 69 3 1991 20.04 30.191128
54 68 3 1992 19.32 30.414108
55 67 3 1993 17.01 27.916000
56 66 3 1994 15.86 26.437651
57 65 3 1995 17.02 25.895513
58 64 3 1996 20.64 26.791996
59 63 3 1997 19.11 30.074375
60 62 3 1998 12.76 42.636103
61 61 3 1999 17.90 46.862510
62 60 3 2000 28.66 30.154079
63 59 3 2001 24.46 30.297644
64 58 3 2002 24.99 34.851205
65 57 3 2003 28.85 38.854943
66 56 3 2004 38.26 37.542447
67 55 3 2005 54.57 38.456399
68 54 3 2006 65.16 43.465535
69 53 3 2007 72.44 41.749414
70 52 3 2008 96.94 48.371262
71 51 3 2009 61.74 54.914470
72 50 3 2010 79.61 65.444964
73 49 3 2011 111.26 76.888119
74 48 3 2012 111.63 81.833602
75 47 3 2013 108.56 83.800483
76 46 3 2014 99.03 79.713947
my codes are the following:
data <- plm.data(Autoregression,index=c("ID","Year"))
Panel <- subset(data, !is.na(I) )
Are <- pgmm( I~p+lag( I , 0:1)
| lag(I, 2:99),
data = Panel, effect = "twoways", model = "onestep")
I have tried also many other versions, including every possible number of the lags, shorter or longer. I suppose that the problem is related to the lag function inside the pgmm, that for some reason does not create lags and simply past again and again the variable, obviously making the matrix non singular. I have also tried to create proper lags with excel and then import the text file, and use the lagged variables from excel instead of the lag function. Unfortunately, I am not sure about the syntax of the pgmm and again didn't work.
The error is the following :
Errore in solve.default(crossprod(WX, t(crossprod(WX, A1)))) :
Lapack routine dgesv: system is exactly singular: U[3,3] = 0
Inoltre: Warning message:
In pgmm(I ~ lag(I, 1) + p | lag(I, 2:10), Panel, effect = "twoways", :
the first-step matrix is singular, a general inverse is used
Can you please help me?
