Hi, I am working with quarterly data and having some problem in calculating a variable.
My data contains a variable name ‘mangro’ which shows institutional managers id no and another variable name ‘typecode’ which divides the managers data into different group. However, after 1998, the classification is known to be affected by data error so following previous literature I want to use pre-1998 classification of manager typecode for 1998 and beyond.
Would anyone kindly advice me how I can do it?
my dataset is tooooo big so created following dataset for illustration purpose
clear
input double(mgrno typecode) float year
980 5 1997
82100 4 1997
59450 4 1997
63500 1 1997
22085 4 1997
81540 1 1997
72484 4 1998
39580 4 1998
12120 5 1998
47430 4 1998
41500 5 1998
11800 4 1998
73500 4 1998
45590 5 1998
67600 1 1998
2730 1 1998
1365 2 1999
22230 4 1999
72300 4 1999
77090 5 1999
67744 4 1999
64600 5 2000
5850 5 2000
76558 4 2000
76960 5 2000
70300 5 2000
485 4 2000
70740 5 2000
11800 5 2000
76960 5 2000
78280 5 2000
67820 5 2000
12800 5 2000
41900 5 2000
55390 5 2000
76760 5 2000
66695 1 2000
37700 5 2000
52750 4 2000
5810 4 2000
36920 5 2000
488 1 2000
61180 5 2000
34430 1 2000
19500 1 2000
18740 5 2000
10005 5 2000
44460 5 2000
71110 5 2000
52260 5 2000
74010 5 2000
end
[/CODE]
My data contains a variable name ‘mangro’ which shows institutional managers id no and another variable name ‘typecode’ which divides the managers data into different group. However, after 1998, the classification is known to be affected by data error so following previous literature I want to use pre-1998 classification of manager typecode for 1998 and beyond.
Would anyone kindly advice me how I can do it?
my dataset is tooooo big so created following dataset for illustration purpose
clear
input double(mgrno typecode) float year
980 5 1997
82100 4 1997
59450 4 1997
63500 1 1997
22085 4 1997
81540 1 1997
72484 4 1998
39580 4 1998
12120 5 1998
47430 4 1998
41500 5 1998
11800 4 1998
73500 4 1998
45590 5 1998
67600 1 1998
2730 1 1998
1365 2 1999
22230 4 1999
72300 4 1999
77090 5 1999
67744 4 1999
64600 5 2000
5850 5 2000
76558 4 2000
76960 5 2000
70300 5 2000
485 4 2000
70740 5 2000
11800 5 2000
76960 5 2000
78280 5 2000
67820 5 2000
12800 5 2000
41900 5 2000
55390 5 2000
76760 5 2000
66695 1 2000
37700 5 2000
52750 4 2000
5810 4 2000
36920 5 2000
488 1 2000
61180 5 2000
34430 1 2000
19500 1 2000
18740 5 2000
10005 5 2000
44460 5 2000
71110 5 2000
52260 5 2000
74010 5 2000
end
[/CODE]