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Generate dummy variables

August 22, 2019, 9:09 am
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Dear Statalisters,

Below is part of my data, which includes fundid, manager name, gender, begin day, end day (the tenure of the manager in a particular fund), and year_month(the manager worked in the fund at the particular time).
I want to
1. drop the fundid manage by individual manager at year_month level;
2. create dummy that represents whether a team(fundid) have a female manager(1) or not(0) at a particular time period(year_month).

However, different team at every time period contains different number of managers and the manager turnover exists therefore I have no clue on this.

Please ignore the gender with N/A, I have to hand cleaning this part and have not finished yet.

DATA

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str10 fundid str30 managername str3 male_0_female_1_notsure_NA float year_month
"FS00008LYU" "James N. Mordy"      "0"   684
"FS00008LYU" "Greg Sleight"        "0"   684
"FS00008LYU" "Menno Vermeulen"     "N/A" 684
"FS00008LYU" "Guy Lakonishok"      "0"   685
"FS00008LYU" "John D. Schaeffer"   "0"   685
"FS00008LYU" "James N. Mordy"      "0"   685
"FS00008LYU" "Josef Lakonishok"    "0"   685
"FS00008LYU" "Menno Vermeulen"     "N/A" 685
"FS00008LYU" "Elaine F. Hahn"      "1"   685
"FS00008LYU" "Greg Sleight"        "0"   685
"FS00008LYU" "Puneet Mansharamani" "N/A" 685
"FS00008LYU" "Puneet Mansharamani" "N/A" 686
"FS00008LYU" "Josef Lakonishok"    "0"   686
"FS00008LYU" "Greg Sleight"        "0"   686
"FS00008LYU" "John D. Schaeffer"   "0"   686
"FS00008LYU" "Elaine F. Hahn"      "1"   686
"FS00008LYU" "Guy Lakonishok"      "0"   686
"FS00008LYU" "James N. Mordy"      "0"   686
"FS00008LYU" "Menno Vermeulen"     "N/A" 686
"FS00008LYU" "Elaine F. Hahn"      "1"   687
"FS00008LYU" "Puneet Mansharamani" "N/A" 687
"FS00008LYU" "James N. Mordy"      "0"   687
"FS00008LYU" "Jody I. Hrazanek"    "1"   687
"FS00008LYU" "Guy Lakonishok"      "0"   687
"FS00008LYU" "John D. Schaeffer"   "0"   687
"FS00008LYU" "Josef Lakonishok"    "0"   687
"FS00008LYU" "Menno Vermeulen"     "N/A" 687
"FS00008LYU" "Greg Sleight"        "0"   687
"FS00008LYU" "Halvard Kvaale"      "0"   687
"FS00008LYU" "Guy Lakonishok"      "0"   688
"FS00008LYU" "John D. Schaeffer"   "0"   688
"FS00008LYU" "Menno Vermeulen"     "N/A" 688
"FS00008LYU" "James N. Mordy"      "0"   688
"FS00008LYU" "Elaine F. Hahn"      "1"   688
"FS00008LYU" "Puneet Mansharamani" "N/A" 688
"FS00008LYU" "Halvard Kvaale"      "0"   688
"FS00008LYU" "Greg Sleight"        "0"   688
"FS00008LYU" "Josef Lakonishok"    "0"   688
"FS00008LYU" "Jody I. Hrazanek"    "1"   688
"FS00008LYU" "James N. Mordy"      "0"   689
"FS00008LYU" "Guy Lakonishok"      "0"   689
"FS00008LYU" "Josef Lakonishok"    "0"   689
"FS00008LYU" "Elaine F. Hahn"      "1"   689
"FS00008LYU" "Menno Vermeulen"     "N/A" 689
"FS00008LYU" "John D. Schaeffer"   "0"   689
"FS00008LYU" "Greg Sleight"        "0"   689
"FS00008LYU" "Puneet Mansharamani" "N/A" 689
"FS00008LYU" "Jody I. Hrazanek"    "1"   689
"FS00008LYU" "Halvard Kvaale"      "0"   689
"FS00008LYU" "Puneet Mansharamani" "N/A" 690
"FS00008LYU" "John D. Schaeffer"   "0"   690
"FS00008LYU" "Halvard Kvaale"      "0"   690
"FS00008LYU" "Elaine F. Hahn"      "1"   690
"FS00008LYU" "Greg Sleight"        "0"   690
"FS00008LYU" "Jody I. Hrazanek"    "1"   690
"FS00008LYU" "Guy Lakonishok"      "0"   690
"FS00008LYU" "Menno Vermeulen"     "N/A" 690
"FS00008LYU" "James N. Mordy"      "0"   690
"FS00008LYU" "Josef Lakonishok"    "0"   690
"FS00008LYU" "John D. Schaeffer"   "0"   691
"FS00008LYU" "Halvard Kvaale"      "0"   691
"FS00008LYU" "Guy Lakonishok"      "0"   691
"FS00008LYU" "James N. Mordy"      "0"   691
"FS00008LYU" "Menno Vermeulen"     "N/A" 691
"FS00008LYU" "Elaine F. Hahn"      "1"   691
"FS00008LYU" "Josef Lakonishok"    "0"   691
"FS00008LYU" "Greg Sleight"        "0"   691
"FS00008LYU" "Puneet Mansharamani" "N/A" 691
"FS00008LYU" "Jody I. Hrazanek"    "1"   691
"FS00008LYU" "Guy Lakonishok"      "0"   692
"FS00008LYU" "Greg Sleight"        "0"   692
"FS00008LYU" "James N. Mordy"      "0"   692
"FS00008LYU" "Halvard Kvaale"      "0"   692
"FS00008LYU" "Josef Lakonishok"    "0"   692
"FS00008LYU" "Puneet Mansharamani" "N/A" 692
"FS00008LYU" "Elaine F. Hahn"      "1"   692
"FS00008LYU" "John D. Schaeffer"   "0"   692
"FS00008LYU" "Menno Vermeulen"     "N/A" 692
"FS00008LYU" "Jody I. Hrazanek"    "1"   692
"FS00008LYU" "John D. Schaeffer"   "0"   693
"FS00008LYU" "Greg Sleight"        "0"   693
"FS00008LYU" "Elaine F. Hahn"      "1"   693
"FS00008LYU" "James N. Mordy"      "0"   693
"FS00008LYU" "Jody I. Hrazanek"    "1"   693
"FS00008LYU" "Josef Lakonishok"    "0"   693
"FS00008LYU" "Menno Vermeulen"     "N/A" 693
"FS00008LYU" "Halvard Kvaale"      "0"   693
"FS00008LYU" "Puneet Mansharamani" "N/A" 693
"FS00008LYU" "Guy Lakonishok"      "0"   693
"FS00008LYU" "Elaine F. Hahn"      "1"   694
"FS00008LYU" "Jody I. Hrazanek"    "1"   694
"FS00008LYU" "Guy Lakonishok"      "0"   694
"FS00008LYU" "Puneet Mansharamani" "N/A" 694
"FS00008LYU" "James N. Mordy"      "0"   694
"FS00008LYU" "Greg Sleight"        "0"   694
"FS00008LYU" "Josef Lakonishok"    "0"   694
"FS00008LYU" "John D. Schaeffer"   "0"   694
"FS00008LYU" "Menno Vermeulen"     "N/A" 694
"FS00008LYU" "Halvard Kvaale"      "0"   694
"FS00008LYU" "Halvard Kvaale"      "0"   695
end
format %tm year_month


Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str10 fundid str30 managername str3 male_0_female_1_notsure_NA float year_month
"FS00008N69" "Aziz V. Hamzaogullari" "0" 696
"FS00008N69" "John (Jed) E. Stocks"  "0" 696
"FS00008N69" "C. Frank Feng"         "0" 697
"FS00008N69" "Jonathan W. Sage"      "0" 697
"FS00008N69" "Edward B. Wagner"      "0" 697
"FS00008N69" "Julian Albornoz"       "1" 697
"FS00008N69" "John (Jed) E. Stocks"  "0" 697
"FS00008N69" "David J. Pavan"        "0" 697
"FS00008N69" "Matthew W. Krummell"   "0" 697
"FS00008N69" "Aziz V. Hamzaogullari" "0" 697
"FS00008N69" "James C. Fallon"       "0" 697
end
format %tm year_month


Could anyone hlep?



Regards
Ulrica
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Aggregating Responses from Questionnaire

August 22, 2019, 9:38 am
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Hi,

I am having some trouble aggregating data from a survey I created.

There are 5 questions (F, E, N, R, P) which appear 3 times slightly modified. i.e. F1, F2, and F3. The idea is to see if individual's choices change through F1 to F3, E1 to E3 etc. Each time, a person answers "A", "B" or "C". The dependent variable would be a categorical variable (A,B, or C).

I think I should aggregate the responses, create dummy variables for each question type, and create a dummy for version 1,2, or 3.

But I have no idea how I should do this. I have been searching through various commands and forums.

This how the data looks in an Excel document:

[ATTACH=CONFIG]temp_15501_1566491464370_866[/ATTACH]

I would really apprecaite some help on this. I feel out of my depth.

Thanks,

Tash
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Distribution of variable based on regression observations

August 22, 2019, 10:02 am
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Dear community,
I want to create a table of graph to show the distribution of responses of variable based on regression observations. Could you help me with this please?
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esttab indicator order creating a coefficient not found error r(111)

August 22, 2019, 10:46 am
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I am using Stata 16.0.

I am having a very weird error. When I run the following code, I get the error "coefficient bbb_spd not found" with the return code r(111). Note that I have changed the variable names for this.

Code:
global GLOB1 "a1 a2 a3 a4 a5"
global GLOB2 "b1 b2 b3 b4 b5 b6 b7"
global GLOB3 c8 c2 c3 c5 c9
global GLOB4 aaa aa a bbb bb b ccc cc_def
global GLOB5 bbb_spd

reg depVar indic any any_x_indic $GLOB1 $GLOB2 $GLOB3 $GLOB4 $GLOB5
est store r1
estadd local cond "All"

reg depVar indic any any_x_indic $GLOB1 $GLOB2 $GLOB3 $GLOB4 $GLOB5 if cond== 1
est store r2
estadd local cond "Some"


esttab * using `output'/`1'_2.tex, indicate ("First Controls = $GLOB2" ///
 "Second Controls = $GLOB3" "Third Controls = $GLOB4" "Fourth Controls = $GLOB5") ///
 scalars("cond") style(tex) replace
Note, though, that looking at the output of each of the regressions, the variable bbb_spd does have a coefficient for each of them.

Yet, when I simply switch the order inside the esttab indicate option, such that the final command reads:

Code:
esttab * using `output'/`1'_2.tex, indicate ("First Controls = $GLOB2" ///
 "Second Controls = $GLOB3" "Fourth Controls = $GLOB5" "Third Controls = $GLOB4") ///
 scalars("cond") style(tex) replace
The code runs perfectly fine. Does anyone have an idea of what could be going wrong? I recognize that "just switch the order" is a solution, but I have this code written in many different locations, so it would be burdensome. Also I am simply curious about the solution.
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GEE vs RE models for unbalanced panel data

August 22, 2019, 10:47 am
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Hello!

I have an unbalanced data set on female employees' corporate experiences.


id: 1401, 1502, ..., 800243 n = 2558
year: 2010, 2012, ..., 2016 T = 4
Delta(year) = 1 unit
Span(year) = 7 periods
(id*year uniquely identifies each observation)

Distribution of T_i: min 5% 25% 50% 75% 95% max
1 1 2 3 3 4 4

Freq. Percent Cum. Pattern*
---------------------------+----------
612 23.92 23.92 1111
599 23.42 47.34 .111
376 14.70 62.04 1...
186 7.27 69.31 .11.
159 6.22 75.53 11..
154 6.02 81.55 111.
151 5.90 87.45 .1..
78 3.05 90.50 .1.1
58 2.27 92.77 1.11
185 7.23 100.00 (other patterns)
---------------------------+----------
2558 100.00 XXXX
--------------------------------------
*Each column represents 2 periods.


And there are two dependent variables I would like to explore.

DV1 = assessment of relationship with other employees (continuous)

DV2 = presence or absence of mentor (binary)

Other than two dependent variables, independent variables are identical for two models.


For DV2, the presence or absence of mentor vary each year for individuals (that is, one year she reported she has a mentor and in the subsequent year, she changed her response to not having a mentor within organization). Thus, combined with the unbalanced nature of the data, my judgement is that for DV2 GEE with exchangeable correlation structure makes more sense than RE model. Although GEE models have to meet MCAR assumption, I believe attrition in data is associated with other covariates (e.g. having a child) in the model.


However, for DV1, I do not feel comfortable running GEE model. My question is, for unbalanced panels with continuous outcome variables, how do I decide between my two options, GEE and RE?






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Using value of a variabel as an index for input in another variable

August 22, 2019, 10:51 am
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Hello,

I'm trying to create a new variable based on another which serves as an index.

"Parentid" is an id for a household. For example, the first three rows correspond to the same household. "id" is an index for people in the household (e.g. first household has three persons with id 1,2,3). Now, "sec13_02" corresponds to id number for a biological mother of a child in a given household. "sec4q8" is the education of each household member. What I need to do is to create a new variable that would give education of the biological mother:

newvar= sec4q8 --> but I want stata to take the id specified in sec13_2, look-up education and print it in a newvar

For the first household newvar would spit out result in the third row and it will be Grade 8. It will be able to do so because it will look into sec13_02 find value 2, go back to id and find row where number 2 for this household is and go along that row and see that Grade 8 is there. But I have no clue how to do it.

Thank you for your help!



Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str32 parentid1 float id byte(sec13_02 sec4q8)
"0011e7ca5a064a59a100b64c205d9689"  1 . 11
"0011e7ca5a064a59a100b64c205d9689"  2 .  8
"0011e7ca5a064a59a100b64c205d9689"  3 2  .
"0024ae5ebbdd417e95c31e18c90a1ab6"  1 . 15
"0024ae5ebbdd417e95c31e18c90a1ab6"  2 . 13
"0024ae5ebbdd417e95c31e18c90a1ab6"  3 2  .
"0024ae5ebbdd417e95c31e18c90a1ab6"  4 .  8
"0024ae5ebbdd417e95c31e18c90a1ab6"  5 .  9
"0024ae5ebbdd417e95c31e18c90a1ab6"  6 .  .
"0024ae5ebbdd417e95c31e18c90a1ab6"  7 5  .
"002e912ee9974a1987e27b1bebf909e3"  1 . 10
"002e912ee9974a1987e27b1bebf909e3"  2 .  5
"002e912ee9974a1987e27b1bebf909e3"  3 .  .
"002e912ee9974a1987e27b1bebf909e3"  4 .  .
"002e912ee9974a1987e27b1bebf909e3"  5 .  .
"002e912ee9974a1987e27b1bebf909e3"  6 .  .
"002e912ee9974a1987e27b1bebf909e3"  7 .  .
"002e912ee9974a1987e27b1bebf909e3"  8 .  .
"002e912ee9974a1987e27b1bebf909e3"  9 .  .
"0031d76399104961b5a58c71722f84f2"  1 .  6
"0031d76399104961b5a58c71722f84f2"  2 .  3
"0031d76399104961b5a58c71722f84f2"  3 2  .
"0035f38396e14cb99cae6f2fa3c14e96"  1 .  9
"0035f38396e14cb99cae6f2fa3c14e96"  2 .  9
"0035f38396e14cb99cae6f2fa3c14e96"  3 .  .
"0035f38396e14cb99cae6f2fa3c14e96"  4 .  .
"0035f38396e14cb99cae6f2fa3c14e96"  5 .  .
"0035f38396e14cb99cae6f2fa3c14e96"  6 .  .
"0035f38396e14cb99cae6f2fa3c14e96"  7 2  .
"0035f38396e14cb99cae6f2fa3c14e96"  8 .  .
"0038ab99d05a433e946d077f166cfb89"  1 .  9
"0038ab99d05a433e946d077f166cfb89"  2 .  .
"0038ab99d05a433e946d077f166cfb89"  3 . 12
"0038ab99d05a433e946d077f166cfb89"  4 .  .
"0038ab99d05a433e946d077f166cfb89"  5 . 12
"0038ab99d05a433e946d077f166cfb89"  6 .  8
"0038ab99d05a433e946d077f166cfb89"  7 .  7
"0038ab99d05a433e946d077f166cfb89"  8 .  .
"0038ab99d05a433e946d077f166cfb89"  9 .  .
"0038ab99d05a433e946d077f166cfb89" 10 .  .
"0038ab99d05a433e946d077f166cfb89" 11 .  .
"003f5cb4aaa04e5bad13ce6b8b20357e"  1 .  .
"003f5cb4aaa04e5bad13ce6b8b20357e"  2 .  .
"003f5cb4aaa04e5bad13ce6b8b20357e"  3 .  .
"003f5cb4aaa04e5bad13ce6b8b20357e"  4 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  1 . 14
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  2 . 12
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  3 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  4 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  5 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  6 . 12
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  7 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9"  9 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9" 10 .  .
"0043b1c8d9eb4bb78ab563f9fc7bf1b9" 11 .  .
"004d58321bda4aa68fa1249f1089666c"  1 . 14
"004d58321bda4aa68fa1249f1089666c"  2 .  9
"004d58321bda4aa68fa1249f1089666c"  3 .  .
"004d58321bda4aa68fa1249f1089666c"  4 2  .
"005292e5271e481cabca643ad9f1282b"  1 . 12
"005292e5271e481cabca643ad9f1282b"  2 . 12
"005292e5271e481cabca643ad9f1282b"  3 .  .
"005292e5271e481cabca643ad9f1282b"  4 2  .
"005292e5271e481cabca643ad9f1282b"  5 .  .
"0056aab91de84f62878681a2b17f5e41"  1 . 10
"0056aab91de84f62878681a2b17f5e41"  2 . 12
"0056aab91de84f62878681a2b17f5e41"  3 .  .
"0056aab91de84f62878681a2b17f5e41"  4 .  .
"0056aab91de84f62878681a2b17f5e41"  5 .  .
"0056aab91de84f62878681a2b17f5e41"  6 .  .
"0056aab91de84f62878681a2b17f5e41"  7 .  .
"0056aab91de84f62878681a2b17f5e41"  8 .  .
"0056aab91de84f62878681a2b17f5e41"  9 .  .
"00573b776a10435bb9b0bcf4f2ec55c0"  1 .  .
"00573b776a10435bb9b0bcf4f2ec55c0"  2 .  .
"00573b776a10435bb9b0bcf4f2ec55c0"  3 .  .
"00573b776a10435bb9b0bcf4f2ec55c0"  4 .  .
"006bdeca4cd648e0968cf3cc380ba989"  1 .  7
"006bdeca4cd648e0968cf3cc380ba989"  2 .  6
"006bdeca4cd648e0968cf3cc380ba989"  3 .  1
"006bdeca4cd648e0968cf3cc380ba989"  4 .  .
"0074e191fa154a85858dbf1fb66cc3b0"  1 .  .
"0074e191fa154a85858dbf1fb66cc3b0"  2 .  .
"0085340bbd934c6da7c2d8cdf2552ad9"  1 . 12
"0085340bbd934c6da7c2d8cdf2552ad9"  2 .  8
"0085340bbd934c6da7c2d8cdf2552ad9"  3 . 12
"0085340bbd934c6da7c2d8cdf2552ad9"  4 .  .
"0085340bbd934c6da7c2d8cdf2552ad9"  5 .  .
"0085340bbd934c6da7c2d8cdf2552ad9"  6 .  .
"008814d6dfad457e9b2987628460ae90"  1 . 10
"008814d6dfad457e9b2987628460ae90"  4 .  .
"008814d6dfad457e9b2987628460ae90"  5 .  .
"008814d6dfad457e9b2987628460ae90"  6 .  .
"008814d6dfad457e9b2987628460ae90"  7 .  .
"008d2cbe304845938701183139ad3281"  2 .  7
"008d2cbe304845938701183139ad3281"  3 .  .
"008d2cbe304845938701183139ad3281"  4 .  .
end
label values sec4q8 l_48
label def l_48 1 "Grade 1", modify
label def l_48 3 "Grade 3", modify
label def l_48 5 "Grade 5", modify
label def l_48 6 "Grade 6", modify
label def l_48 7 "Grade 7", modify
label def l_48 8 "Grade 8", modify
label def l_48 9 "Grade 9", modify
label def l_48 10 "Grade 10", modify
label def l_48 11 "Grade 11", modify
label def l_48 12 "Grade 12", modify
label def l_48 13 "12 A Levels", modify
label def l_48 14 "Certificate", modify
label def l_48 15 "Diploma", modify
↧

Correlation issue

August 22, 2019, 11:06 am
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Dear community,
I have variable for social classes (X1), which measured in 5 categories. I have variable for attitudes (X2) and another variable for satisfaction (X3). I would like to create a table to show the statistical correlation between X2 and X3 for each category of X1. Do you know how to do this?
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Confusion Matrix / Accuracy Table after XTLOGIT

August 22, 2019, 11:34 am
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Hi,

I have searched high and low for an answer to this question and can't come up with much. Does anyone know if it's possible to generate a confusion matrix (accuracy table) after an xtlogit with random effects or fixed effects? I would like to see which outcomes are classified correctly and which outcomes are misclassified by my model.

Thank you for any help you can provide!

Roger
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Search

faster to re-load data or preserve/restore?

August 22, 2019, 12:28 pm
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I have a workflow that requires frequently collapsing data according to different specifications and then running regressions. The data are very large and typically take >15-30 seconds to do, what feels like, anything. In principle, is it computationally less expensive to use preserve/restore repeatedly or simply clear and re-load data sets every time?

I know that my workflow is probably more cumbersome than it needs to be and that the real answer is to bite the bullet and learn how to leverage mata but it would be helpful to know for future reference.
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Multiple Imputation Problem

August 22, 2019, 12:37 pm
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Hi there,

I used a planned missing data design, the three form design with missing individual items. I am trying to impute data now using auxiliary variables to help predict the missing data but I'm getting two different error messages depending on whether I create an index for my dependent variable or not. Here is my code with error messages next to asterisks:

mi register imputed gn1 gn2 gn3 gn4 gn5 gn6 gn7 gn8 gn9 gn10 gn11 gn12 gn13 gn14 gn15 gn16 gn17 gn18 gn19 gn20 gn21 gn22 gn23 gn24 gn25 gn26 gn27 gn28

mi register imputed gen_norms
*index of the above individual items

mi impute mvn gn1 gn2 gn3 gn4 gn5 gn6 gn7 gn8 gn9 gn10 gn11 gn12 gn13 gn14 gn15 gn16 gn17 gn18 gn19 gn20 gn21 gn22 gn23 gn24 gn25 gn26 gn27 gn28 = violence sn com, add(50)
*violence sn and com are auxiliary variables to help predict imputation and they do not have missing values

*error message: Iteration 0: imputed data contain missing values
*This may occur when imputation variables are used as independent variables, when independent variables contain missing values, or when
*variance-covariance matrix becomes not positive definite. You can specify option force if you wish to proceed anyway.


mi impute mvn gen_norms = violence sn com, add(50)
*Performing EM optimization:
*note: 2165 observations omitted from EM estimation because of all imputation variables missing
*no observations to estimate initial parameters using EM

Thank you for any assistance.

Best,
Erica
↧

Problem updating*to Stata 16.0 21 Aug 2019 on Windows Server 2012R2

August 22, 2019, 1:00 pm
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I installed Stata 16 MP2 on a computer running Windows Server 2012R2 a few weeks ago without problems and I installed the previous update without any problem either.

This time, the update fails and I get the following message:

installing utility files ...
Could not move jar to .old/
r(695);

The Stata folder contains a folder named ".tmp" whose name begins with a dot. It contains and "ado" folder and a "utilities" folder. I guess ".old" should be another folder created during the update process and to be erased afterwards.

There is no ".old" folder in the Stata folder. I tried to create it and I got an error message. Apparently, Windows does not allow explicitly creating a folder whose name begins with a dot.

I updated Stata SE on a Windows 10 computer without any problem.

I have admin privileges on both computers.

Someone could help?


↧

help in coding this function

August 22, 2019, 1:42 pm
↧

Constructing Portfolio Returns Ranked on Customer Returns

August 22, 2019, 1:54 pm
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Dear community,

I am trying to construct a portfolio with Stata.
For a little story behind the data:
I work with data of supplier-customer relationships. For every months &years known relationship, the stock prices of both supplier &customer are collected. Monthly returns have also been calculated.

Now I want to construct 5quintile portfolio returns. These portfolios consist of the supplier returns, formed by sorting stocks on the 1 month lagged customer returns (which the supplier is linked to). The portfolio has to be re-balanced for every month, based on the customer returns of t-1.

Example of dataset:
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str8(stic ctic) long date float(ret_sup ret_cus)
"AAOI" "AMZN" 19723   .13798337    .01313453
"AAOI" "AMZN" 19754  -.09193871    -.1005542
"AAOI" "AMZN" 19782    .7358767   .009506826
"AAOI" "AMZN" 19813   .04268809   -.07107157
"AAOI" "AMZN" 19843   -.0879611   -.09583335
"AAOI" "AMZN" 19873  -.04844445    .02768547
"AAOI" "AMZN" 19904   .08360583    .03912978
"AAOI" "AMZN" 19935  -.22413796  -.036301523
"AAOI" "AMZN" 19964         .18    .08322956
"AAOI" "AMZN" 19996   -.2419962   -.04896179
"AAOI" "AMZN" 20027   .00248441   -.05266099
"AAOI" "AMZN" 20055   -.3277571    .10862314
"AAOI" "AMZN" 20088   .03410137   -.08354007
"AAOI" "AMZN" 20118   -.2032086    .14235538
"AAOI" "AMZN" 20146    .3959732    .07229291
"AAOI" "AMZN" 20178   .11217954   -.02120159
"AAOI" "AMZN" 20208  .028818415    .13351248
"AAOI" "AMZN" 20237   .25070035   .017663267
"AAOI" "AMZN" 20269  -.02799552   .011322566
"AAOI" "AMZN" 20300   .12039171     .2351126
"AAOI" "AMZN" 20331   .05964009    -.0433834
"AAOI" "AMZN" 20361  -.08879184 -.0019497358
"AAOI" "AMZN" 20391   .09797657    .22272365
"AAOI" "AMZN" 20422   -.0906887    .06215044
"AAOI" "AMZN" 20453      -.0848    .01668175
"AAOI" "AMZN" 20482  -.04603724    -.1315155
"AAOI" "AMZN" 20513   .09896144   -.05873932
"AAOI" "AMZN" 20544   -.1712062   .074422635
"AAOI" "AMZN" 20573   -.2488263    .11109428
"AAOI" "AMZN" 20605  -.05803568    .09581702
"AAOI" "AMZN" 20635   .05687198   -.00991987
"AAOI" "AMZN" 20664   .06188346    .06035326
"AAOI" "AMZN" 20697    .4231419   .013639746
"AAOI" "AMZN" 20727    .3181008    .08860318
"AAOI" "AMZN" 20758  -.13417378   -.05671733
"AAOI" "AMZN" 20788   .28809157   -.04969487
"AAOI" "AMZN" 20818  -.05369398 -.0009326408
"AAOI" "ARRS" 20088   .03410137   .014108165
"AAOI" "ARRS" 20118   -.2032086   -.13150054
"AAOI" "ARRS" 20146    .3959732    .12051868
"AAOI" "ARRS" 20178   .11217954  -.016507784
"AAOI" "ARRS" 20208  .028818415     .1654265
"AAOI" "ARRS" 20237   .25070035  -.019747615
"AAOI" "ARRS" 20269  -.02799552   -.07300812
"AAOI" "ARRS" 20300   .12039171   .010457506
"AAOI" "ARRS" 20331   .05964009   -.14553687
"AAOI" "ARRS" 20361  -.08879184   -.01703258
"AAOI" "ARRS" 20391   .09797657     .0881787
"AAOI" "ARRS" 20422   -.0906887    .08174096
"AAOI" "ARRS" 20453      -.0848            0
"AAOI" "ARRS" 20482  -.04603724   -.16683024
"AAOI" "ARRS" 20513   .09896144   -.06203377
"AAOI" "ARRS" 20544   -.1712062   -.04060274
"AAOI" "ARRS" 20573   -.2488263  -.006544486
"AAOI" "ARRS" 20605  -.05803568    .05841018
"AAOI" "ARRS" 20635   .05687198   -.13029051
"AAOI" "ARRS" 20664   .06188346     .2996184
"AAOI" "ARRS" 20697    .4231419   .030469894
"AAOI" "ARRS" 20727    .3181008   .009262566
"AAOI" "ARRS" 20758  -.13417378   -.01941402
"AAOI" "ARRS" 20788   .28809157    .03275737
"AAOI" "ARRS" 20818  -.05369398    .05019166
"AAOI" "CSCO" 19723   .13798337    .05552943
"AAOI" "CSCO" 19754  -.09193871  -.023183256
"AAOI" "CSCO" 19782    .7358767  -.005020566
"AAOI" "CSCO" 19813   .04268809   .028211087
"AAOI" "CSCO" 19843   -.0879611    .03100601
"AAOI" "CSCO" 19873  -.04844445   .065339684
"AAOI" "CSCO" 19904   .08360583    .00934198
"AAOI" "CSCO" 19935  -.22413796   .015291717
"AAOI" "CSCO" 19964         .18  -.009512477
"AAOI" "CSCO" 19996   -.2419962   .007202893
"AAOI" "CSCO" 20027   .00248441  -.027810916
"AAOI" "CSCO" 20055   -.3277571    .12954639
"AAOI" "CSCO" 20088   .03410137   .006331445
"AAOI" "CSCO" 20118   -.2032086   -.05213017
"AAOI" "CSCO" 20146    .3959732    .11928695
"AAOI" "CSCO" 20178   .11217954  -.067265354
"AAOI" "CSCO" 20208  .028818415    .04741146
"AAOI" "CSCO" 20237   .25070035   .016649308
"AAOI" "CSCO" 20269  -.02799552  -.063118406
"AAOI" "CSCO" 20300   .12039171    .03495998
"AAOI" "CSCO" 20331   .05964009   -.08937372
"AAOI" "CSCO" 20361  -.08879184   .014296787
"AAOI" "CSCO" 20391   .09797657    .09904763
"AAOI" "CSCO" 20422   -.0906887   -.05545928
"AAOI" "CSCO" 20453      -.0848  -.003486213
"AAOI" "CSCO" 20482  -.04603724   -.12391824
"AAOI" "CSCO" 20513   .09896144    .10046235
"AAOI" "CSCO" 20544   -.1712062    .08747131
"AAOI" "CSCO" 20573   -.2488263  -.034422185
"AAOI" "CSCO" 20605  -.05803568    .05674789
"AAOI" "CSCO" 20635   .05687198  -.012392382
"AAOI" "CSCO" 20664   .06188346   .064133845
"AAOI" "CSCO" 20697    .4231419    .02980674
"AAOI" "CSCO" 20727    .3181008   .008905813
"AAOI" "CSCO" 20758  -.13417378  -.032786854
"AAOI" "CSCO" 20788   .28809157   -.02803131
"AAOI" "CSCO" 20818  -.05369398   .013413804
"AAOI" "MSFT" 19723   .13798337    -.0188828
"AAOI" "MSFT" 19754  -.09193871    .01149426
"AAOI" "MSFT" 19782    .7358767    .01242075
"AAOI" "MSFT" 19813   .04268809    .06995563
"AAOI" "MSFT" 19843   -.0879611  -.014393758
"AAOI" "MSFT" 19873  -.04844445   .013366264
"AAOI" "MSFT" 19904   .08360583   .018563805
"AAOI" "MSFT" 19935  -.22413796    .03501197
"AAOI" "MSFT" 19964         .18      .052595
"AAOI" "MSFT" 19996   -.2419962    .02047106
"AAOI" "MSFT" 20027   .00248441    .01272649
"AAOI" "MSFT" 20055   -.3277571   .018317372
"AAOI" "MSFT" 20088   .03410137  -.028445944
"AAOI" "MSFT" 20118   -.2032086   -.13024756
"AAOI" "MSFT" 20146    .3959732    .08539596
"AAOI" "MSFT" 20178   .11217954   -.07286202
"AAOI" "MSFT" 20208  .028818415    .19640882
"AAOI" "MSFT" 20237   .25070035   -.03659537
"AAOI" "MSFT" 20269  -.02799552   -.05783182
"AAOI" "MSFT" 20300   .12039171    .05775762
"AAOI" "MSFT" 20331   .05964009  -.068094224
"AAOI" "MSFT" 20361  -.08879184   .017003627
"AAOI" "MSFT" 20391   .09797657     .1893358
"AAOI" "MSFT" 20422   -.0906887   .032484785
"AAOI" "MSFT" 20453      -.0848    .02079119
"AAOI" "MSFT" 20482  -.04603724  -.007029549
"AAOI" "MSFT" 20513   .09896144    -.0764204
"AAOI" "MSFT" 20544   -.1712062    .08549525
"AAOI" "MSFT" 20573   -.2488263   -.09704871
"AAOI" "MSFT" 20605  -.05803568    .06276321
"AAOI" "MSFT" 20635   .05687198  -.034528337
"AAOI" "MSFT" 20664   .06188346    .10768033
"AAOI" "MSFT" 20697    .4231419   .013761446
"AAOI" "MSFT" 20727    .3181008   .002436467
"AAOI" "MSFT" 20758  -.13417378    .04027778
"AAOI" "MSFT" 20788   .28809157   .005674235
"AAOI" "MSFT" 20818  -.05369398    .03119816
"AAON" "WMT"  11687   -.0952381     .2046036
"AAON" "WMT"  11718    .2631579   -.08492569
"AAON" "WMT"  11746  -.16666667  .0023201855
"AAON" "WMT"  11778         .05   -.01388889
"AAON" "WMT"  11808  -.04761905  -.018779343
"AAON" "WMT"  11837         .05   .014354067
"AAON" "WMT"  11869  -.14285715   .014150944
"AAON" "WMT"  11900          .5    .03953489
"AAON" "WMT"  11931   -.3333333   .022371365
"AAON" "WMT"  11961   .22222222    .03501094
"AAON" "WMT"  11991    .1818182    .02959831
"AAON" "WMT"  12022   .07692308    .05749487
"AAON" "WMT"  12053           0  -.005825242
"AAON" "WMT"  12082    .2142857   .017578125
"AAON" "WMT"  12110   .05882353    -.5009597
"AAON" "WMT"  12143    .3333333  -.023076924
"AAON" "WMT"  12173    .3333333    -.1574803
"AAON" "WMT"  12201      .40625    .04205608
"AAON" "WMT"  12234  -.15555556   -.05829597
"AAON" "WMT"  12264    .2368421  -.033333335
"AAON" "WMT"  12296    .3191489   .014778325
"AAON" "WMT"  12326   2.8709676   -.04368932
"AAON" "WMT"  12355        -.05    .07106599
"AAON" "WMT"  12387  -.01754386    .08530806
"AAON" "WMT"  12418        .125   -.12663755
"AAON" "WMT"  12449   .25396827          .06
"AAON" "WMT"  12477   .20253165   .070754714
"AAON" "WMT"  12508  .010526316   -.08810572
"AAON" "WMT"  12537    .3854167   -.02415459
"AAON" "WMT"  12569  -.03007519   -.06930693
"AAON" "WMT"  12599  -.07751938   .031914894
"AAON" "WMT"  12628 -.016806724   .030927835
"AAON" "WMT"  12661  -.05128205        -.015
"AAON" "WMT"  12691  -.08108108   -.05076142
"AAON" "WMT"  12722   .12745099   .005347594
"AAON" "WMT"  12752 -.026086956  -.010638298
"AAON" "WMT"  12782  -.08035714    -.0860215
"AAON" "WMT"  12814 -.029126214     .0764706
"AAON" "WMT"  12842           0    .03825137
"AAON" "WMT"  12873       -.075    .07894737
"AAON" "WMT"  12901  -.25405404   -.07317073
"AAON" "WMT"  12934  -.11594203    .04736842
"AAON" "WMT"  12964  -.06557377    .07537688
"AAON" "WMT"  12995   .05263158  -.004672897
"AAON" "WMT"  13026  -.04166667    -.0798122
"AAON" "WMT"  13055  -.07826087   .010204081
"AAON" "WMT"  13087  -.20754717   -.12626262
"AAON" "WMT"  13117    .2142857    .10982659
"AAON" "WMT"  13146  -.09803922  -.072916664
"AAON" "WMT"  13179  -.06521739   -.08426967
"AAON" "WMT"  13208  .069767445    .04294479
"AAON" "WMT"  13237  -.17391305    .08235294
"AAON" "WMT"  13269  -.07894737    .03804348
"AAON" "WMT"  13300   .02857143    .08376963
"AAON" "WMT"  13328   .16666667   -.01932367
"AAON" "WMT"  13361  .023809524   -.05418719
"AAON" "WMT"  13391           0    .09895834
"AAON" "WMT"  13422   .04651163            0
"AAON" "WMT"  13453   -.0888889   .004739337
"AAON" "WMT"  13482  .024390243   -.03773585
"AAON" "WMT"  13514 -.071428575   -.10784314
"AAON" "WMT"  13545    .3333333    .04395605
"AAON" "WMT"  13573  .067307696    .11052632
"AAON" "WMT"  13604           0    .05687204
"AAON" "WMT"  13634  -.06306306    .00896861
"AAON" "WMT"  13664   .09615385    .06222222
"AAON" "WMT"  13695    .1754386    .13179916
"AAON" "WMT"  13726  -.08955224     .1090573
"AAON" "WMT"  13755           0   -.05333333
"AAON" "WMT"  13787   .14754099    .03169014
"AAON" "WMT"  13818  -.12857144    -.0443686
"AAON" "WMT"  13846           0    .14464286
"AAON" "WMT"  13879 -.008196721  -.015600624
"AAON" "WMT"  13909   .07438017   .009508716
"AAON" "WMT"  13937   .12307692     .1632653
"AAON" "WMT"  13969   .16438356      .097166
"AAON" "WMT"  13999   .05294118  -.004920049
"AAON" "WMT"  14028  -.05586592    .09023486
"AAON" "WMT"  14060  -.07692308    .10204082
"AAON" "WMT"  14091  .025641026    .03909465
"AAON" "WMT"  14122       -.325   -.06534653
"AAON" "WMT"  14152   .16666667  -.074152544
"AAON" "WMT"  14182   .14285715    .26430205
"AAON" "WMT"  14213   .02777778    .09049774
"AAON" "WMT"  14244  .006756757     .0813278
"AAON" "WMT"  14273   .02013423    .05602456
"AAON" "WMT"  14301  -.09868421  .0014534884
"AAON" "WMT"  14334   .28467155    .07039187
"AAON" "WMT"  14364   .05681818     -.501017
"AAON" "WMT"  14392  -.09139785   -.07336956
"AAON" "WMT"  14425    .0887574     .1319648
"AAON" "WMT"  14455   .20108695   -.12435233
"AAON" "WMT"  14487   -.1402715    .04881657
"AAON" "WMT"  14517    .1368421    .07334273
"AAON" "WMT"  14546  -.02777778    .19185282
"AAON" "WMT"  14578  -.03809524   .014332966
"AAON" "WMT"  14609   .13861386     .2021739
"AAON" "WMT"  14640   .10869565    -.2079566
"AAON" "WMT"  14669   .07058824   -.10730594
"AAON" "WMT"  14700   .02930403    .15601023
"AAON" "WMT"  14728   .19572954  -.019911505
"AAON" "WMT"  14761    .1607143    .04740406
"AAON" "WMT"  14791  .005128205  -.006465517
"AAON" "WMT"  14822 -.025510205   -.04663774
"AAON" "WMT"  14853   .02617801   -.13651878
"AAON" "WMT"  14882 -.020408163   .014492754
"AAON" "WMT"  14914  -.11979166   -.05714286
"AAON" "WMT"  14944   .01775148    .15013774
"AAON" "WMT"  14973  -.17732558   .017964073
"AAON" "WMT"  15006   .30035335    .06917646
"AAON" "WMT"  15034  -.10326087    -.1181338
"AAON" "WMT"  15064  -.06060606   .008185264
"AAON" "WMT"  15095  -.01935484    .02455449
"AAON" "WMT"  15126     .181579  .0001932416
end
format %d date

For constructing the portfolio returns I have used the following codes, since only the first and fifth quintile are important I only show those codes :

Code:
xtile q_ret = ret_cus , nq(5)
bysort yr mnth: egen ret_q5 = mean(cond( q_ret == 5, ret_sup , .) )
by yr mnth : egen ret_q1 = mean(cond( q_ret == 1, ret_sup , .))
I have looked a lot on the internet and this forum, but I cant seem to find codes for portfolio constructing, which applies to my wishes with my data. The code I found applies to Nick Cox. Since the code feels to easy, I am being a bit of suspicious here. I checked the data and it seems to be fine. Although I still have some questions:

- Is the code right as I used it and does it fit my purpose?
- How can I Implement the formation to be sorted on lagged customer returns? In stead of 1 month lagged return, I have used customer returns, just at t, for convenience right now.
- With the xtile code, am I ranking the customer returns at every month level or for the whole dataset? I tried to use bysort with an generated year and month level data, but then xtile will not work.
- Any others with expertise in finance, which can approve my methodology of finance portfolio strategy?

I would appreciate hearing form you.

Best,
Arslan
↧
↧

Reshape?expand?order?

August 22, 2019, 2:30 pm
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Hi,

I need the below data set1 to look like dataset2:
Dataset1
version set id seller1 status1 price1 seller2 status2 price2
1 1 10 1 1 4 1 2 2
1 2 12 0 1 4 2 2 3
1 3 15 0 2 2 2 1 4
1 4 7 2 0 1 0 2 4
1 5 2 0 1 3 2 0 3
1 6 14 2 1 1 0 0 1
2 1 11 1 2 2 0 0 4
2 2 17 1 1 2 1 0 1
2 3 4 1 0 3 1 1 2
2 4 9 2 2 3 0 1 2
2 5 8 0 1 1 2 0 1
2 6 1 2 1 2 0 2 1
3 1 16 1 2 2 1 0 3
3 2 13 1 0 1 1 2 4
3 3 5 0 0 3 2 2 4
3 4 18 1 0 4 1 1 1
3 5 3 2 2 4 0 1 2
3 6 6 0 2 1 2 1 3

Dataset2
version set alt seller status price
1 1 seller1 status1 price1
1 2 seller2 status2 price2
1 1
1 2
1 1
1 2
i.e. the data for seller1, seller 2 in the positions mentioned.

Any idea? Any help will be much appreciated.

Thank you,

Maria Vas
↧

converting quantiles to tertiles

August 22, 2019, 2:31 pm
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We summarize the associations corresponding to the top versus bottom third of the baseline FGF-23 concentrations. In studies where different measures of association were reported, we
are trying to calculate a measure of association corresponding to the top versus bottom third of FGF-23concentration .
in some studies, associations were reported for top versus bottom quintile, quartile or half of the FGF-23 distribution. in some other studies, associations were reported per unit of standard deviation (SD)

I wonder if you can help me to convert the risk ratio as reported in the studies and calculate the risk ratio and confidence interval of top versus bottom tertiles?
(ln/unit=per unit on lnFGF-23 scale; Q4vsQ1=top quartile vs bottomquartile; log/SD or ln/SD=per SD on logFGF-23/lnFGF-23 scale)

Example of the data I got is :
Author Risk ratio (95% CI) as reported
in study report1; comparison
Parker 2010 1.05 (0.85, 1.3); ln/unit
di Giuseppe 2015 1.62 (1.07, 2.45); Q4vsQ1
Ix* 2012 1.19 (0.77, 1.83); Q4vsQ1
Ix** 2012 1.29 (0.75, 2.22); Q4vsQ1
Kendrick 2011 2.44 (1.25,4.76); Q4vsQ1
Moe 2015 1.2 (1.02,1.41); log/SD
Tags: None




I have found a paper that did this conversion without getting back to the raw data.
They did the following:
For example, in studies where associations were
reported for top versus bottom quintile, quartile or half of the FGF-23 distribution, the log hazard
ratios were scaled by factors of 0.779, 0.858 and 1.371, respectively, to reflect the respective
ratios of the distance between the means of the baseline FGF-23 measurements in top and
bottom third and the distances between means in top and bottom quintile, quartile or half in a
normal distribution (2.18/2.80, 2.18/2.54 and 2.18/1.59, respectively). Similarly, in studies
where associations were reported per unit of standard deviation (SD) increase (e.g.,logtransformed
FGF-23), the scaling factor used was 2.18 (as the distance between the means of
baseline FGF-23 measurements in top and bottom third of a normal distribution is 2.18x SDs).
For studies reporting associations per unit (or multiples thereof) increase in log-transformed
FGF-23, the respective units were converted to SDs (provided the SD of log-transformed
FGF-23 for the population was also reported) and the above approach employed. Where the
SD was required but not reported, it was estimated from the interquartile range


I am trying to do exactly the same but do not know how to do this on STATA . Also I am not sure if the way and the factors they used to scale the log hazard can be standardized
and used the same to do a similar analysis or not?

Looking forward to hear back from you
↧
Search

correlations between ordinal, dichotomous variables

August 22, 2019, 2:58 pm
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Hello,
I'm asked to do correlations between 2 variables, both of which are ranked and dichotomous; I feel logistic regression with Odds Ratio might be a suitable option. Does anyone have any other ideas? I mean, are there any other methods that can produce correlation coefficients for the aforementioned scenario? Thanks very much!
Sincerely
MN
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Converting a .dta file into a flat file (not delimited) (outfile)

August 22, 2019, 3:23 pm
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Dear Statalisters,

I am used to import flat .txt datasets with dictionary in Stata. I was wondering if it is possible to do the opposite... I want to convert a .dta dataset into a .txt flat file (no spaces, no quotation marks for strings, etc.) where variables are defined according the character positions.

I tried to do it by using the -outfile- command (outfile using "file name here", nolabel noquote) but it doesn't work if I type the options nolabel noquote and other.

Could you please give me a hand,

Thanks in advance

Edgar

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↧

Generate variable if other observation has value on date

August 22, 2019, 3:27 pm
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I am doing peer effects analysis based on geography. I have panel data organized by an id for the unit and the date and then the id of the 4 closest units. If a fraction of units received a treatment on a particular date then I would like to know if a unit's neighbors were exposed to this treatment over time. For example,


Code:
 ssc install dataex input long id float date long neighbor1 long neighbor2 long neighbor3 long neighbor4 float treated 1 0101960 9 10 2 3 0 1 0201960 9 10 2 3 0 1 0301960 9 10 2 3 1 1 0401960 9 10 2 3 1 1 0501960 9 10 2 3 1 2 0101960 10 1 3 4 0 2 0201960 10 1 3 4 0 2 0301960 10 1 3 4 0 2 0401960 10 1 3 4 0 2 0501960 10 1 3 4 0 3 0101960 1 2 4 5 0 3 0201960 1 2 4 5 0 3 0301960 1 2 4 5 0 3 0401960 1 2 4 5 0 3 0501960 1 2 4 5 0 4 0101960 2 3 5 6 0 4 0201960 2 3 5 6 0 4 0301960 2 3 5 6 0 4 0401960 2 3 5 6 0 4 0501960 2 3 5 6 0 5 0101960 3 4 6 7 0 5 0201960 3 4 6 7 0 5 0301960 3 4 6 7 0 5 0401960 3 4 6 7 0 5 0501960 3 4 6 7 0 6 0101960 4 5 7 8 0 6 0201960 4 5 7 8 0 6 0301960 4 5 7 8 0 6 0401960 4 5 7 8 0 6 0501960 4 5 7 8 0 7 0101960 5 6 8 9 0 7 0201960 5 6 8 9 0 7 0301960 5 6 8 9 0 7 0401960 5 6 8 9 0 7 0501960 5 6 8 9 0 8 0101960 6 7 9 10 0 8 0201960 6 7 9 10 0 8 0301960 6 7 9 10 0 8 0401960 6 7 9 10 0 8 0501960 6 7 9 10 0 9 0101960 7 8 10 1 0 9 0201960 7 8 10 1 0 9 0301960 7 8 10 1 0 9 0401960 7 8 10 1 1 9 0501960 7 8 10 1 1 10 0101960 8 9 1 2 0 10 0201960 8 9 1 2 0 10 0301960 8 9 1 2 0 10 0401960 8 9 1 2 0 10 0501960 8 9 1 2 0 end
Would hopefully have the outcome of:
Code:
 ssc install dataex input long id float date long neighbor1 long neighbor2 long neighbor3 long neighbor4 float treated float exposed_D 1 0101960 9 10 2 3 0 0 1 0201960 9 10 2 3 0 0 1 0301960 9 10 2 3 1 1 1 0401960 9 10 2 3 1 1 1 0501960 9 10 2 3 1 1 2 0101960 10 1 3 4 0 0 2 0201960 10 1 3 4 0 0 2 0301960 10 1 3 4 0 1 2 0401960 10 1 3 4 0 1 2 0501960 10 1 3 4 0 1 3 0101960 1 2 4 5 0 0 3 0201960 1 2 4 5 0 0 3 0301960 1 2 4 5 0 1 3 0401960 1 2 4 5 0 1 3 0501960 1 2 4 5 0 1 4 0101960 2 3 5 6 0 0 4 0201960 2 3 5 6 0 0 4 0301960 2 3 5 6 0 0 4 0401960 2 3 5 6 0 0 4 0501960 2 3 5 6 0 0 5 0101960 3 4 6 7 0 0 5 0201960 3 4 6 7 0 0 5 0301960 3 4 6 7 0 0 5 0401960 3 4 6 7 0 0 5 0501960 3 4 6 7 0 0 6 0101960 4 5 7 8 0 0 6 0201960 4 5 7 8 0 0 6 0301960 4 5 7 8 0 0 6 0401960 4 5 7 8 0 0 6 0501960 4 5 7 8 0 0 7 0101960 5 6 8 9 0 0 7 0201960 5 6 8 9 0 0 7 0301960 5 6 8 9 0 0 7 0401960 5 6 8 9 0 1 7 0501960 5 6 8 9 0 1 8 0101960 6 7 9 10 0 0 8 0201960 6 7 9 10 0 0 8 0301960 6 7 9 10 0 0 8 0401960 6 7 9 10 0 1 8 0501960 6 7 9 10 0 1 9 0101960 7 8 10 1 0 0 9 0201960 7 8 10 1 0 0 9 0301960 7 8 10 1 0 1 9 0401960 7 8 10 1 1 1 9 0501960 7 8 10 1 1 1 10 0101960 8 9 1 2 0 0 10 0201960 8 9 1 2 0 0 10 0301960 8 9 1 2 0 1 10 0401960 8 9 1 2 0 1 10 0501960 8 9 1 2 0 1 end
So units 7 and 8 were exposed on 04/01/1960 and 05/01/1960, the days that unit 9 received treatment. But units 9, 10, 2 and 3 were exposed on 03/01/1960, 04/01/1960 and 05/01/1960 because those were the dates that unit 1 received treatment.

My understanding is that there must be some deviation from the mata solution provided by Sergio here: https://www.statalist.org/forums/for...tion-has-value

But it is unclear to me how I can generalize it to be sensitive to dates.

Thank you,
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xtbcfe erroe

August 22, 2019, 5:26 pm
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Dear All,

I am trying to use the user written routine xtbcfe for bias corrected fixed effect estimation of a panel data model. I installed the routine and attempted to replicate the estimations suggested in the help file.

I got the following error message, which I cannot understand:

Code:
. xtbcfe n w wL1 k kL1 kL2 ys ysL1 ysL2, bciters(250) res(iid) ini(bi) lags(2)
command distinct is unrecognized
r(199);
Hence I installed distinct. Then I receive the following error:

Code:
 xtbcfe n w wL1 k kL1 kL2 ys ysL1 ysL2, bciters(250) res(iid) ini(bi) lags(2)
               bcfe_ub():  3499  mm_repeat() not found
                 <istmt>:     -  function returned error
r(3499);
As far as you know (if you know the routine), is there any bug in it?

Best

Dario

EDIT

I found the solution in another thread in this forum. But still I have problems with the routine. The results of the estimation (the same suggested in the help file) is reported below:

Code:
. xtbcfe n w wL1 k kL1 kL2 ys ysL1 ysL2, bciters(250) res(iid) ini(bi) lags(2) te crit(0.01)


Bootstrap corrected dynamic FE regression       Number of obs      =       751
Group variable : id                             Number of groups   =       140

Resample       : i.i.d.                         Obs per group: min =         5
Initialization : Burn-in                                       avg =       5.4
Convergence    : Yes                                           max =         7


Dependent variable : n
------------------------------------------------------------------------------
                Results                                                      
                 Coefs.  Std. Err.          t      P>|t| [95% Conf.  Interval]
------------------------------------------------------------------------------
L.n           1.0507655          .          .          .          .          .
L2.n         -0.1656689          .          .          .          .          .
w            -0.5516643          .          .          .          .          .
wL1           0.5132341          .          .          .          .          .
k             0.3880882          .          .          .          .          .
kL1          -0.2208651          .          .          .          .          .
kL2          -0.0465805          .          .          .          .          .
ys            0.4757484          .          .          .          .          .
ysL1         -0.7717861          .          .          .          .          .
ysL2          0.1648291          .          .          .          .          .
year4         0.0198841          .          .          .          .          .
year5         0.0347159          .          .          .          .          .
year6         0.0006001          .          .          .          .          .
year7         0.0054584          .          .          .          .          .
year8         0.0242108          .          .          .          .          .
year9         0.0480169          .          .          .          .          .
------------------------------------------------------------------------------
Notes:
- No standard errors computed
Any idea why standard errors are not computed?
↧

Creating portfolios with double sorting and calculating mean returns of the portfolios.(please help)

August 22, 2019, 7:00 pm
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This is the third time I write this topic but did not get any answer from statalists members, and cannot find the answer in the forum.
I need to create portfolios by sorting past 1 month returns (past_ret_1) and standard deviation (volatility_1) and portfolios should be generated every month. And, I need to calculate the mean future returns(ri1) of portfolios.
In other words, sort stocks by sorting past 1 month returns (past_ret_1) and standard deviation (volatility_1). And group top 20% and SD top 50% stocks, portfolios should be created and replaced every month and calculate the portfolios' 1 month mean future return (ri1).
I have sorted stocks by past returns and price standard deviation. And, I tried to create portfolios using xtile. I want to create portfolios with double sorting; the first is top 20% past 1month returns stocks and top 50% standard deviation; the second is that bottom past 1month 20% return stocks and bottom 50% standard deviation ( portfolios should be generated every month)
I know if I have one sorting standard I could have used
bys mdate : egen portfolios = xtile(past_ret_1), nq(5)
But I cannot create portfolio with two sorting standards.

I have to create portfolios which
1. have top 20% 1month past returns and top 50% past 1 month volatility(standard deviation)
2. have bottom 20% past 1month returns and bottom 50% past 1 month volatility(standard deviation)



Could anyone help me?
I describe the variables which I need to code this.
permno is stock code,
date is date, prc is price of stock,
ast_ret_1 is past 1 month return,
volatility_1 is past 1 month volatility (standard deviation)
r1r is 5 quantiles of past_ret_1,
vol_grade1 is 2 quantiles of volatility_1
ri1 is 1 month future return of



I tried
// devide stocks by 20% quintiles with respect to past 1, 3, 6, month returns
. xtile r1r = past_ret_1, n(5)

. xtile r3r = past_ret_3, n(5)

. xtile r6r = past_ret_6, n(5)


// devide stocks by 50% quintiles with respect to past 1, 3, 6, month volatility

. xtile vol_grade1 = volatility_1, n(2)

. xtile vol_grade3 = volatility_3, n(2)

. xtile vol_grade6 = volatility_6, n(2)

//To generate portfolios which have top 20% returns and top 50% standard deviation - consctruct portfolios every month
bys mdate : gen winner1 = ri1 if r1r==5 & vol_grade1==2
(ri1 is the following month return)

//To get mean of winner1's following month return
bys mdate : egen meanwinner1 = mean(winner1)

But I cannot get the following month returns of portfoilios which include top 20% past return and top 50% standard deviation stocks.
Can anyone help?

My dataex is below

----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input double permno long date double(shrcd exchcd siccd prc ret) int mdate double volatility_1 float past_ret_1 double volatility_3 float past_ret_3 double volatility_6 float(past_ret_6 ri1 ri3 ri6) byte(r1r r3r r6r vol_grade1 vol_grade3 vol_grade6)
10001 18266 11 2 4925              10.25    -.004854387138038874 600                   .            .                  .            .                  .           .  -.025365876            0   .05951216 . . . . . .
10001 18294 11 2 4925  9.989999771118164    -.006958314683288336 601  .18640435354468468  -.025365876 .18640435354468468  -.025365876 .18640435354468468 -.025365876  .0010010239     .1811812   .20620625 2 2 3 1 1 1
10001 18322 11 2 4925                 10   -.0008392913150601089 602  .07973949391474112  .0010010239 .15942680476776733  -.024390243 .15942680476776733 -.024390243         .025         .137   .04350004 3 2 3 1 1 1
10001 18353 11 2 4925              10.25     .007866266183555126 603  .07564392234132054         .025 .13787852330894748            0 .13787852330894748           0    .15121953    .05951216   .07317073 4 3 3 1 1 1
10001 18385 11 2 4925 11.800000190734863      .03599647432565689 604  .40177915615148524    .15121953 .44905645667481014     .1811812 .40594366205503735   .15121953  -.036440704    .02118644  -.03898305 5 5 4 1 1 1
10001 18414 11 2 4925 11.369999885559082   -.0026315555442124605 605  .39753444140417754  -.036440704  .5543317709609373         .137  .5137325483141435   .10926828    -.0448549    -.0822339  -.10729994 2 5 4 1 1 1
10001 18444 11 2 4925 10.859999656677246                       0 606   .3195789827141747    -.0448549  .5530637553214912    .05951216  .7111912906637656   .05951216    .10957648   .012891376  -.03775321 2 4 4 1 1 1
10001 18476 11 2 4925 12.050000190734863     .028156990185379982 607  .38256143087180816    .10957648  .4674535510182806    .02118644  .7228745528019987   .20620625   -.13402487   -.05892117  -.10622405 5 3 5 1 1 1
10001 18506 11 2 4925   10.4350004196167      .00617106631398201 608   .6820635243785057   -.13402487  .5199689092052626    -.0822339  .6988548611401296   .04350004    .05414466  -.027312007   .05414466 1 2 3 2 1 1
10001 18536 11 2 4925                 11    -.010791356675326824 609  .23167174266670273    .05414466  .5051961032408528   .012891376  .5273756839519413   .07317073   .030909104   -.05000002   .06818182 4 3 4 1 1 1
10001 18567 11 2 4925  11.34000015258789    -.008732615038752556 610  .15765161420960289   .030909104 .45920004131705155   -.05892117 .47373630755060436  -.03898305   -.10493832   -.05026452 -.022045854 4 2 2 1 1 1
10001 18597 11 2 4925 10.149999618530273     .010956141166388988 611   .5875475317883132   -.10493832  .5126182310801831  -.027312007   .609981220237303  -.10729994    .02955667    .08374389    .1251232 1 2 2 2 1 1
10001 18630 11 2 4925 10.449999809265137   -.0066540539264678955 612  .12508096619524547    .02955667  .5297362400926223   -.05000002  .5926389438470832  -.03775321    .03062207    .12440193   .12057418 4 2 2 1 1 1
10001 18659 11 2 4925 10.770000457763672   -.0009275765623897314 613   .1578552469346256    .03062207  .3723881033701738   -.05026452  .5563828464510988  -.10622405   .021355575   .029712133   .04828222 4 2 2 1 1 1
10001 18687 11 2 4925                 11    .0018215354066342115 614  .07125262232538056   .021355575  .2324524127880026    .08374389 .41657432956710305   .05414466    .06818182    .03818183  -.00999997 4 4 4 1 1 1
10001 18718 11 2 4925              11.75    .0017050689784809947 615    .181595891063371    .06818182  .2560189479973279    .12440193  .4327399562258367   .06818182    -.0561702  -.003404252 -.065531954 5 5 4 1 1 1
10001 18749 11 2 4925  11.09000015258789     -.01246655359864235 616  .17742968647816745    -.0561702 .34554503303207285   .029712133  .5184807669582139 -.022045854    .02975653   .018034248 -.006311965 2 3 3 1 1 1
10001 18779 11 2 4925 11.420000076293945    -.006956514902412891 617  .10122221615745265    .02975653  .2562096761145746    .03818183 .43820764162425335    .1251232   .025394043   -.04640978  -.04991241 4 4 4 1 1 1
10001 18809 11 2 4925 11.710000038146973     .013852800242602825 618  .06950455926252143   .025394043 .18129218532038652  -.003404252 .35840022726152915   .12057418  -.035866786   -.06233992 -.023057256 4 3 4 1 1 1
10001 18840 11 2 4925 11.289999961853027     .009838967584073544 619  .22807671595986842  -.035866786 .18267390602016262   .018034248  .2969734976720698   .04828222   -.03542955  -.023914926  -.01416295 2 3 3 1 1 1
10001 18871 11 2 4925 10.890000343322754    -.008196649141609669 620   .1584367410581936   -.03542955 .26967476101129784   -.04640978 .26210378054041233  -.00999997   .008264389  -.003673091   .02387505 2 2 3 1 1 1
10001 18903 11 2 4925 10.979999542236328   -.0009099389426410198 621   .1362248386336823   .008264389 .36503089311441256   -.06233992  .3312659481663802 -.065531954   .003643071    .04189436   .04007291 3 2 2 1 1 1
10001 18932 11 2 4925 11.020000457763672    .0018182233907282352 622    .064879644634678   .003643071  .1754350624607524  -.023914926  .3031444882221424 -.006311965  -.015426503   .009990906  .014446435 3 2 3 1 1 1
10001 18962 11 2 4925 10.850000381469727    -.004587085917592049 623  .08011199628563465  -.015426503 .14098885706925854  -.003673091 .30494546040380033  -.04991241     .0543778     .0276497  -.05437789 2 3 2 1 1 1
10001 18995 11 2 4925   11.4399995803833    .0017512700287625194 624  .16471602550200323     .0543778  .1109390323861021    .04189436 .27715720948418837 -.023057256  -.027089104 -.0017482084   -.1171328 4 4 3 1 1 1
10001 19024 11 2 4925  11.13010025024414   -.0017846968257799745 625  .12695825733412597  -.027089104  .1505072818312588   .009990906 .17297727649822103  -.01416295  .0017878877   .004411454   -.0943478 2 3 3 1 1 1
10001 19053 11 2 4925 11.149999618530273   -.0009677049820311368 626   .0733472854133795  .0017878877  .1506023838790185     .0276497 .17794872754080054   .02387505    .02421529   -.07982057  -.10672642 3 3 3 1 1 1
10001 19085 11 2 4925 11.420000076293945    -.020583171397447586 627   .1590294799085855    .02421529 .14011209706522632 -.0017482084  .1718818116493562   .04007291  -.021085806   -.11558666  -.12872157 4 3 3 1 1 1
10001 19114 11 2 4925 11.179200172424316    -.022805705666542053 628  .17402728265658138  -.021085806 .15136438495279056   .004411454  .1767343709664558  .014446435   -.08222412   -.09832548   -.1027981 2 3 3 1 1 1
10001 19145 11 2 4925 10.260000228881836    -.016299143433570862 629   .2630426561283295   -.08222412 .25490894451350693   -.07982057 .21065142226993364  -.05437789  -.015594527  -.029239785  -.07602346 1 2 2 1 1 1
10001 19176 11 2 4925 10.100000381469727                       0 630    .262437261852884  -.015594527  .4555340676353445   -.11558666  .3951759012681891   -.1171328 -.0019802433   -.01485154  -.05148519 2 2 2 1 1 1
10001 19206 11 2 4925 10.079999923706055    -.006896521896123886 631  .06224752051650344 -.0019802433 .42426073529192365   -.09832548  .5055695043194937   -.0943478   -.01190475  -.004960336  -.02777775 3 2 2 1 1 1
10001 19240 11 2 4925  9.960000038146973    -.002003958448767662 632  .07820865732721459   -.01190475  .2119991506535575  -.029239785  .5751764591194123  -.10672642 -.0010040391   -.04819282  .003012021 3 2 2 1 1 1
10001 19267 11 2 4925  9.949999809265137    .0010060592321678996 633 .046484553045708754 -.0010040391 .10346999210154871   -.01485154   .524455669371931  -.12872157  .0080401935   -.03718592   .03919601 3 3 2 1 1 1
10001 19298 11 2 4925 10.029999732971191     .000898096477612853 634  .03227864172652164  .0080401935 .06351416957895344  -.004960336  .3920297684823155   -.1027981   -.05483551   -.02293116   .02392829 3 3 2 1 1 1
10001 19330 11 2 4925  9.479999542236328    -.004201776813715696 635   .4917846959868616   -.05483551 .37806184764038636   -.04819282 .34091790047554354  -.07602346   .010548564     .0537975   .08649796 2 2 2 1 1 1
10001 19360 11 2 4925  9.579999923706055       .0267952848225832 636  .12748062644580277   .010548564 .40215772419513063   -.03718592 .34743378822254384  -.05148519   .022964537    .07933196    .0960334 3 2 2 1 1 1
10001 19390 11 2 4925  9.800000190734863   -.0070921676233410835 637   .1730536192179159   .022964537  .3363810478802017   -.02293116 .32662120860743593  -.02777775    .01938771    .04795921   .06224486 4 2 2 1 1 1
10001 19418 11 2 4925  9.989999771118164    .0050302003510296345 638  .05680673034385046    .01938771 .25360936079801805     .0537975  .3271844424437017  .003012021   .035035074   .031031074     .016016 4 4 3 1 1 1
10001 19449 11 2 4925  10.34000015258789     .013725523836910725 639  .04287068869956355   .035035074 .18079184222215358    .07933196   .340254399421649   .03919601  -.006769796   .015473872 -.004835608 4 4 3 1 1 1
10001 19479 11 2 4925 10.270000457763672    -.005808273795992136 640  .14054930450066883  -.006769796 .13558691824147576    .04795921  .3700833577673592   .02392829   .002921103   .013631877 -.022395374 3 4 3 1 1 1
10001 19512 11 2 4925 10.300000190734863    .0019455698784440756 641  .12124643005719198   .002921103  .1874594287183265   .031031074 .35141358137286594   .08649796    .01941746  -.014563162  -.19708735 3 3 4 1 1 1
10001 19540 11 2 4925               10.5     .019417457282543182 642  .14604521823753902    .01941746 .17155749358700312   .015473872   .273556838331953    .0960334  -.008571443  -.020000003   -.2342857 4 3 4 1 1 1
10001 19571 11 2 4925  10.40999984741211    -.000959714874625206 643  .22069548057910818  -.008571443  .1684166689431317   .013631877 .22211684000340348   .06224486   -.02497601  -.035542738  -.11911622 3 3 4 1 1 1
10001 19604 11 2 4925 10.149999618530273    -.004901979584246874 644  .07947326195351662   -.02497601  .1584622601198413  -.014563162  .1841185037416441     .016016   .013793138    -.1852216 -.073891625 2 3 3 1 1 1
10001 19632 11 2 4925 10.289999961853027     .008823544718325138 645  .07089471348123667   .013793138 .14480615214672515  -.020000003  .1581658604218184 -.004835608   -.02429543    -.2186589   -.0281827 3 3 3 1 1 1
10001 19663 11 2 4925 10.039999961853027     .007021032273769379 646  .11058593465750331   -.02429543 .09025947741784955  -.035542738 .14096491287365762 -.022395374   -.17629477   -.08665337    .0846614 2 2 3 1 1 1
10001 19694 11 2 4925  8.270000457763672    -.036130473017692566 647    .623414004046124   -.17629477  .5098740407713392    -.1852216 .40667581431779926  -.19708735  -.027811425    .13663834   .25755733 1 1 1 2 1 1
10001 19725 11 2 4925  8.039999961853027    .0012453586095944047 648  .12027350435487831  -.027811425  .9765681054309964    -.2186589   .856076286427567   -.2342857    .14054728     .2437811   .29353228 2 1 1 1 1 1
10001 19757 11 2 4925  9.170000076293945                       0 649   .4675528098777736    .14054728  .7383673068649981   -.08665337  .8653969540244855  -.11911622    .02508174     .1875682    .3631407 5 2 2 1 1 1
10001 19785 11 2 4925  9.399999618530273     .004273500293493271 650  .23728124683069343    .02508174  .6891845208952013    .13663834  .8181591992611321 -.073891625    .06382983    .10638298    .1978724 4 5 2 1 1 1
10001 19814 11 2 4925                 10    -.005964256357401609 651  .16197478259265533    .06382983  .4120083692715949     .2437811  .7576223882289632   -.0281827    .08900003    .03999996         .17 5 5 2 1 1 1
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10001 20149 11 2 4925 10.100000381469727                       0 662   .2446505058044584     .0466322  .6408768982377454   -.11403503  .6438900723449399  -.10301952   -.01485154  -.009901027  -.12871289 4 2 2 1 1 1
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10001 20209 11 2 4925  10.09000015258789     .001986142946407199 664  .06637716411991972   .014070387   .154285966169348    .04559591  .6503682985108398  -.11101323  -.008919737   -.03072353  -.12586723 3 4 2 1 1 1
10001 20240 11 2 4925                 10                       0 665   .0581172805442231  -.008919737   .107328667474757  -.009901027  .5381612061583212  -.12280699    .00699997   -.11999998  -.12700005 3 3 2 1 1 1
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10001 20332 11 2 4925  8.800000190734863    -.012345640920102596 668  .34786478479449284   -.10020445 .46126758551671454   -.11999998   .356839508408627  -.12871289    .01022729  -.007954619  -.05681818 1 2 2 1 1 1
10001 20362 11 2 4925  8.890000343322754    -.005592755973339081 669  .23358382474387235    .01022729  .5509337361041263   -.11717968  .5345813159474855   -.1065326  -.007874088   -.15410577   -.1226097 3 2 2 1 1 1
10001 20394 11 2 4925  8.819999694824219                       0 670  .18473612181984753  -.007874088  .2974306907681954   -.09815951  .5721746581352123  -.12586723    -.0102041   -.04535143  -.21655327 3 2 2 1 1 1
10001 20423 11 2 4925  8.729999542236328   -.0011441910173743963 671  .11627884176868694    -.0102041 .21259853951346633  -.007954619  .5713980810324415  -.12700005   -.13860248   -.04925537  -.18556695 3 3 2 1 1 1
10001 20457 11 2 4925  7.519999980926514     .009395996108651161 672   .5579679723619297   -.13860248  .6512967430482997   -.15410577  .7329853960190996  -.25322738    .11968087    .03723407  -.05851065 1 1 1 1 1 1
10001 20485 11 2 4925  8.420000076293945      .01201927661895752 673  .27671314614485903    .11968087  .5680918456949471   -.04535143  .6159062690213537  -.13905928  -.014251768   -.17933494  -.15083136 5 2 2 1 1 1
10001 20514 11 2 4925  8.300000190734863       .0559796504676342 674   .2349002451136125  -.014251768  .4202801443741118   -.04925537   .561834561666733  -.05681818   -.06024096    -.1433735  -.10361447 2 2 2 1 1 1
10001 20545 11 2 4925  7.800000190734863   -.0012803779682144523 675  .13742525924620014   -.06024096 .22447660547009565    .03723407  .5332888965346905   -.1226097    -.1141026   -.09230772  -.03333336 2 4 2 1 1 1
10001 20576 11 2 4925  6.909999847412109     -.05342470481991768 676   .2633846021305704    -.1141026 .39412138769836946   -.17933494  .5052049115736454  -.21655327     .0289436    .03473231    .7872649 1 1 1 1 1 1
10001 20606 11 2 4925  7.110000133514404    -.004201643168926239 677  .09567675569586913     .0289436 .48335545823210324    -.1433735  .5042947253178054  -.18556695  -.004219439    .04641349    .7510548 4 1 1 1 1 1
10001 20636 11 2 4925  7.079999923706055      .01287555880844593 678  .05673900789326746  -.004219439  .2530382865060027   -.09230772  .5272738438726049  -.05851065    .00988703    .06497176    .7796611 3 2 2 1 1 1
10001 20667 11 2 4925  7.150000095367432     .007042280398309231 679 .048782661566319226    .00988703 .10762584669607418    .03473231  .5020461587462266  -.15083136    .04055943     .7272727    .7692307 3 4 2 1 1 1
10001 20698 11 2 4925  7.440000057220459     .010869554243981838 680   .1220978336810731    .04055943 .17764848751005802    .04641349 .39596763716385475  -.10361447   .013440847     .6733871    .7002687 4 4 2 1 1 1
10001 20730 11 2 4925  7.539999961853027    -.016949167475104332 681   .1715364726753886   .013440847 .23925850670954377    .06497176  .2817927559295505  -.03333336     .6379311     .6710876    .6777188 3 4 2 1 1 1
10001 20759 11 2 4925 12.350000381469727                       0 682   2.115515531436557     .6379311 2.1413539866430558     .7272727 1.7401421430669661    .7872649    .00809712   .024291435  .012145718 5 5 5 2 2 2
10001 20789 11 2 4925 12.449999809265137    -.004000015091150999 683   .0970518319368766    .00809712   2.39688207556103     .6733871 2.3624398394939847    .7510548    .01204824   .016064242  .016064242 3 5 5 1 2 2
10001 20822 11 2 4925 12.600000381469727     .003984078764915466 684  .07054213004398015    .01204824 1.3418394719060822     .6710876  2.550009684547101    .7796611   .003968193   .003968193   .02380946 3 5 5 1 2 2
10001 20851 11 2 4925 12.649999618530273                       0 685  .04757363708671314   .003968193 .09452372153657818   .024291435  2.458981151214114    .7692307            0  -.011857677    .0316206 3 3 5 1 1 2
10001 20879 11 2 4925 12.649999618530273                       0 686 .039366089860446346            0  .0711081492552561   .016064242  2.013523405989526    .7002687            0            0           . 3 3 5 1 1 2
10001 20912 11 2 4925 12.649999618530273    -.003937022760510445 687   .0473913086863199            0 .04519681973146688   .003968193  .9858898764953599    .6777188  -.011857677   .019762846           . 3 3 5 1 1 1
10001 20940 11 2 4925               12.5                       0 688 .041696421737767955  -.011857677 .05335704570218714  -.011857677 .08043606477525421  .012145718    .01199997    .04400001           . 3 3 3 1 1 1
10001 20971 11 2 4925 12.649999618530273    -.003937022760510445 689 .052265164265786236    .01199997 .05701883729893278            0 .06425233168020862  .016064242   .019762846            .           . 3 3 3 1 1 1
10001 21003 11 2 4925 12.899999618530273    -.001934280269779265 690  .10233207953408884   .019762846 .10971372377365043   .019762846 .08449748919907539   .02380946   .011627952            .           . 4 3 3 1 1 1
10001 21032 11 2 4925 13.050000190734863     .007722037378698587 691  .05897991367034467   .011627952 .14225035481618584    .04400001 .13428686175196708    .0316206            .            .           . 3 4 3 1 1 1
10002 18266 11 3 6020 3.0999999046325684      .08013938367366791 600                   .            .                  .            .                  .           .     .2451613     .5644838   .21290326 . . . . . .
10002 18294 11 3 6020  3.859999895095825    -.015306168235838413 601   .4296841825364234     .2451613  .4296841825364234     .2451613  .4296841825364234    .2451613    .17875656     .4145078   -.1554404 5 5 5 1 1 1
10002 18322 11 3 6020  4.550000190734863     .004415006842464209 602  .36717583363251327    .17875656  .7387044837153376     .4677421  .7387044837153376    .4677421    .06591199   -.03736265  -.31428576 5 5 5 1 1 1
10002 18353 11 3 6020  4.849899768829346 -.000020646557459258474 603  .10538855626183742    .06591199  .7850850067661307     .5644838  .7850850067661307    .5644838    .12579647   -.22472624  -.36905915 5 5 5 1 1 1
10002 18385 11 3 6020  5.460000038146973     -.11793214827775955 604   .3809403276799246    .12579647   .589193584993965     .4145078  .9486795247042981    .7612904    -.1978022    -.4029304   -.4578755 5 5 5 1 1 1
10002 18414 11 3 6020  4.380000114440918    -.011286617256700993 605  .46236515967535524    -.1978022 .47578619431157226   -.03736265  .8975770443956432    .4129033   -.14155254   -.28767127   -.4200914 1 2 5 1 1 1
10002 18444 11 3 6020  3.759999990463257     .016216199845075607 606  .33242027453540607   -.14155254   .681697207867179   -.22472624  .8364520181489538   .21290326   -.13297872    -.1861702   -.3005319 1 1 5 1 1 1
10002 18476 11 3 6020  3.259999990463257     .061889272183179855 607    .229305905784321   -.13297872  .7488768581626191    -.4029304  .7608379566539603   -.1554404   -.04294482   -.09202453  -.20858897 1 1 2 1 1 1
end
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