I have a panel dataset with daily data on covid-19 outcomes at country level and others explanatory variables. I am trying to use the - mi - function to solve for missing values.
This is my dataset:
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input double(total_cases_per_million total_deaths_per_million total_tests_per_thousand stringency_index) str13 continent float date_ long n_country float country_system
47390.578 1167.897 147.546 77.31 "South America" 22344 1 1
1721.732 33.344 10.97 92.59 "South America" 22101 1 1
24389.469 648.313 66.444 80.09 "South America" 22214 1 1
. . . 11.11 "South America" 21947 1 1
41202.788 1034.101 116.002 79.17 "South America" 22303 1 1
22951.813 608.884 63.75 82.87 "South America" 22209 1 1
46141.722 1142.806 141.222 77.31 "South America" 22335 1 1
24706.047 657.805 67.029 80.09 "South America" 22215 1 1
. . . 11.11 "South America" 21967 1 1
15543.134 343.904 49.255 87.96 "South America" 22184 1 1
7452.068 151.519 29.617 87.96 "South America" 22149 1 1
48718.844 1191.173 153.933 71.76 "South America" 22354 1 1
5617.073 105.408 23.829 87.96 "South America" 22137 1 1
8.563 .133 .068 100 "South America" 21998 1 1
108.13 5.753 1.716 88.89 "South America" 22039 1 1
20998.931 560.716 60.153 82.87 "South America" 22203 1 1
118.839 6.24 1.976 88.89 "South America" 22042 1 1
28995.873 784.056 76.168 79.17 "South America" 22234 1 1
115.232 6.04 1.891 88.89 "South America" 22041 1 1
31392.468 851.252 82.05 79.17 "South America" 22248 1 1
39178.771 998.434 108.276 79.17 "South America" 22294 1 1
829.945 20.975 7.155 88.89 "South America" 22084 1 1
.022 . .012 11.11 "South America" 21977 1 1
44103.567 1096.695 129.626 79.17 "South America" 22320 1 1
279.407 10.333 3.648 90.74 "South America" 22060 1 1
21365.027 569.146 60.853 82.87 "South America" 22204 1 1
. . . 11.11 "South America" 21968 1 1
26446.63 709.181 70.578 81.94 "South America" 22222 1 1
47562.762 1170.021 148.46 77.31 "South America" 22346 1 1
1.239 .044 .023 41.67 "South America" 21990 1 1
1486.798 29.892 10.032 92.59 "South America" 22097 1 1
20290.281 543.679 58.714 82.87 "South America" 22201 1 1
43.699 1.814 .488 100 "South America" 22015 1 1
946.659 22.369 7.74 88.89 "South America" 22087 1 1
41951.995 1049.545 119.032 79.17 "South America" 22307 1 1
372.845 11.926 4.368 90.74 "South America" 22066 1 1
11852.988 246.66 40.696 87.96 "South America" 22169 1 1
40779.85 1025.649 114.495 79.17 "South America" 22301 1 1
47674.675 1175.353 149.144 71.76 "South America" 22347 1 1
31519.162 856.938 82.475 79.17 "South America" 22249 1 1
34412.043 934.91 92.607 79.17 "South America" 22271 1 1
5049.034 94.057 22.12 88.89 "South America" 22133 1 1
69.564 3.363 1.038 100 "South America" 22027 1 1
35709.708 955.023 97.139 79.17 "South America" 22279 1 1
9036.818 187.119 33.646 87.96 "South America" 22157 1 1
47836.35 1178.252 149.904 71.76 "South America" 22348 1 1
47498.11 1169.8 148.09 77.31 "South America" 22345 1 1
504.339 14.692 5.268 88.89 "South America" 22073 1 1
2006.67 38.057 12.098 92.59 "South America" 22105 1 1
31272.28 847.911 81.845 79.17 "South America" 22247 1 1
41481.774 1040.672 117.309 79.17 "South America" 22305 1 1
97.974 4.823 1.493 88.89 "South America" 22035 1 1
11598.385 241.328 39.99 87.96 "South America" 22168 1 1
17667.27 465.043 53.686 87.96 "South America" 22192 1 1
27.989 .863 .251 100 "South America" 22008 1 1
111.072 5.841 1.805 88.89 "South America" 22040 1 1
. . .011 11.11 "South America" 21973 1 1
3.496 .089 .046 88.89 "South America" 21995 1 1
9475.199 197.341 34.768 87.96 "South America" 22159 1 1
38603.253 992.305 106.44 79.17 "South America" 22292 1 1
152.204 7.279 2.44 90.74 "South America" 22048 1 1
13032.457 268.078 43.448 87.96 "South America" 22174 1 1
8414.326 178.114 32.178 87.96 "South America" 22154 1 1
1547.512 30.644 10.286 81.48 "South America" 22098 1 1
25307.674 673.559 68.26 81.94 "South America" 22217 1 1
83.636 4.093 1.219 100 "South America" 22030 1 1
49084.785 1199.913 155.504 70.37 "South America" 22356 1 1
37086.894 973.011 101.611 79.17 "South America" 22286 1 1
51969.922 1235.912 165.895 71.76 "South America" 22370 1 1
12091.417 249.205 41.233 87.96 "South America" 22170 1 1
1044.412 23.852 8.194 88.89 "South America" 22089 1 1
. . . 11.11 "South America" 21952 1 1
145.213 7.058 2.345 90.74 "South America" 22047 1 1
76.003 3.651 1.097 100 "South America" 22028 1 1
44436.563 1103.51 131.293 79.17 "South America" 22322 1 1
. . .011 11.11 "South America" 21969 1 1
36302.463 962.081 98.926 79.17 "South America" 22283 1 1
49831.47 1209.648 158.771 70.37 "South America" 22361 1 1
8190.765 173.445 31.563 87.96 "South America" 22153 1 1
. . . 11.11 "South America" 21966 1 1
18242.147 482.943 54.929 87.96 "South America" 22194 1 1
48006.565 1180.619 150.668 71.76 "South America" 22349 1 1
.022 . .012 11.11 "South America" 21979 1 1
2365.486 43.544 13.388 92.59 "South America" 22110 1 1
39649.257 1002.195 110.126 79.17 "South America" 22296 1 1
. . . 11.11 "South America" 21974 1 1
46627.476 1149.776 143.905 77.31 "South America" 22339 1 1
9983.189 207.121 35.984 87.96 "South America" 22161 1 1
11.107 .199 .093 100 "South America" 22000 1 1
34.384 1.062 .331 100 "South America" 22011 1 1
30963.756 839.481 80.993 79.17 "South America" 22245 1 1
45960.666 1139.708 140.41 77.31 "South America" 22334 1 1
670.306 18.033 6.307 88.89 "South America" 22079 1 1
. . . 11.11 "South America" 21942 1 1
.376 .022 .014 11.11 "South America" 21984 1 1
39991.081 1014.077 111.655 79.17 "South America" 22298 1 1
.752 .044 .019 25 "South America" 21988 1 1
2459.212 45.358 13.721 92.59 "South America" 22111 1 1
37932.947 982.769 104.195 79.17 "South America" 22289 1 1
45117.578 1119.928 135.678 79.17 "South America" 22328 1 1
end
format %tdCCYY-NN-DD date_
label values n_country n_country
label def n_country 1 "Argentina", modifyand this is my code:
Code:
mi set mlong mi misstable summarize total_cases_per_million total_deaths_per_million mi misstable patterns total_cases_per_million total_deaths_per_million mi reshape wide total_cases_per_million total_deaths_per_million total_tests_per_thousand stringency_index , i(n_country) j(date_) mi register regular total_deaths_per_million* total_tests_per_thousand* stringency_index* mi register imputed total_cases_per_million* mi impute chained (regress) total_cases_per_million* = total_deaths_per_million* total_tests_per_thousand* stringency_index* , add(10) rseed (091107)
and this what I get
Code:
no observations error occurred during imputation of total_cases_per_million21938 total_cases_per_million21939 total_cases_per_million21940 total_cases_per_million21941 total_cases_per_million21942 total_cases_per_million21943 total_cases_per_million21944 total_cases_per_million21945 total_cases_per_million21946 total_cases_per_million21947 total_cases_per_million21948 total_cases_per_million21949 total_cases_per_million21950 total_cases_per_million21951 total_cases_per_million21952 total_cases_per_million21953 total_cases_per_million21954 total_cases_per_million21955 total_cases_per_million21956 total_cases_per_million21957 total_cases_per_million21958 total_cases_per_million21959 total_cases_per_million21960 total_cases_per_million21961 total_cases_per_million21962 total_cases_per_million21963 total_cases_per_million21964 total_cases_per_million21965 total_cases_per_million21966 total_cases_per_million21967 total_cases_per_million21968 total_cases_per_million21969 total_cases_per_million21970 total_cases_per_million21971 total_cases_per_million21972 total_cases_per_million21973 total_cases_per_million21974 total_cases_per_million21975 total_cases_per_million21976 total_cases_per_million21977 total_cases_per_million21978 total_cases_per_million21979 total_cases_per_million21980 total_cases_per_million21981 total_cases_per_million21982 total_cases_per_million21983 total_cases_per_million21984 on m = 1 r(2000); end of do-file
Does anyone knows that is the issue here?
Thank you very much in advance to whoever is willing to help.
Best regards
Alessio Lombini
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