regression imputation stata

The add( ) option specifies how many imputed data sets to generate, well discuss below how to choose this. Our goal is to regress wages on sex, race, education level, and experience. Chapter 8 Multiple Imputation. We saw above that age and bmi have missing values: We can examine our setup with mi describe: We see 126 complete observations with 28 incomplete, the two variables to be imputed, and the 4 unregistered variables which will automatically be registered as regular. There are a few significant interactions between race or urban and other variables, but not nearly as many (and keep in mind that with this many coefficients we'd expect some false positives using a significance level of .05). Missing Data Imputation using Regression . Impute missing values of multiple variables of different types with an Obtain MI estimates from previously saved individual estimation results. Logs. Since both bmi and age are continuous variables, we use method regress. way, and so always work with the most convenient organization. mi xeq 1/5: sum `var' if miss_`var' More modern literature increases this number, with a good starting point being 200 imputations. Impute missing values separately for different groups of the data. Cell link copied. by female: ologit edu exp i.urban i.race wage. mi organizes The mi xeq: prefix tell Stata to apply the subsequent command to each imputation individually. Someone recently asked me about using substantive model compatible imputation, as implemented in smcfcs in R, to impute missing covariates, followed by fitting Fine and Gray models for the cumulative incidence functions using the crr function in the cmprsk package.. univariate methods: linear regression (fully parametric) for continuous variables, predictive mean matching (semiparametric) for continuous variables, truncated regression for continuous variables with a restricted range, interval regression for censored continuous variables, multinomial (polytomous) logistic for nominal variables, negative binomial for overdispersed count variables. Among the coefficients, we see that smokers have significantly higher odds of having a heart attack, and theres some weak evidence that age plays a role. Stata News, 2022 Economics Symposium If you follow this advice, simply exclude the [pweight = ] part of the mi impute command. mean differences, regression coefficients, standard errors and to derive confidence intervals and p-values.) regress wage i.urban i.race exp i.edu i.female. mi can import already imputed data from NHANES or ice, or you can start with original data and form imputations yourself.. On the contrary, marvel at Bao'an Temple, one of Taipei's most ornate temples. mi can import already imputed data from NHANES or ice, or you can Perform tests on multiple coefficients simultaneously. We want to study the linear relationship between y and predictors Try some local delicacies at the Ningxia Night Market. To perform our imputation, we would use. For instance, I want to regress the 11 imputed variables in the attached spreadsheet on 'age'. 2021 Board of Regents of the University of Wisconsin System. Which Stata is right for me? The Consider how much time you have available and decide how many imputations you can afford to run, using the rule of thumb that time required is proportional to the number of imputations. For each missing value, obtain a distribution for it. The mi commands recognize three kinds of variables: Imputed variables are variables that mi is to impute or has imputed. } New in Stata 17 Missingness: Each value of all the variables except female has a 10% chance of being missing completely at random, but of course in the real world we won't know that it is MCAR ahead of time. Basically, take any analysis command you would normally run, e.g. The Control Panel unifies many of mis capabilities into one flexible Books on Stata Cold deck imputation. Disciplines mi xeq 0: kdensity wage; sleep 1000 pmm itself cannot be run outside the imputation context, but since it's based on regression you can use regular regression to test it. as well as the original data. In either case, estimation commands still need both the mi estimate: svy: prefixes in that order. mi estimate: regress income educ experience gender, beta. arrow_right_alt. To illustrate the process, we'll use a fabricated data set. The imputation process cannot simply drop the perfectly predicted observations the way logit can. To do so, examine the trace file saved by mi impute chained. If you are analyzing survival data, you can Stata Journal, Watch handling missing data in Stata tutorials. There has been some discussion that imputation should not take into account any complex survey design features (because you want the imputation to reflect the sample, not necessarily the population). As you can see, iterative imputing is still at the experimental stage in the sci-kit learn library. if you are working with panel data and want to reshape your data. rvfplot. Use the fastest computer available to you. This applies when you're using imputed data as well. multivariate normal (MVN). M ultiple (Imputation) I terated: Repeat to achieve stability. Choose from univariate and multivariate methods to impute missing values in continuous, censored, truncated, binary, ordinal, categorical, and count variables. We need to tell Stata how were going to be doing the imputations. erase c:\windows\temp\dataset, copy /project/mydata/dataset /ramdisk/dataset So here's our suggestion: Multiple imputation has introduced many researchers into the world of jobs that take hours, days, or even weeks to run. The new variables added are: Now that weve got the data set up for multiple imputations, and done the imputation, most of the hard part is over. Upcoming meetings For example, log wage is determined by wage, or an indicator for obesity might be determined by a function of weight and height. If you also notice, we have loaded several regressive models. Unfortunately you cannot access the imputation number within mi xeq. Since we set the data as flong, each imputed data set lives in the data with a separate _mi_m value. R is the seed to be used for the random number generatorif you do not set this you'll get slightly different imputations each time the command is run. You could drop them before imputing, but that seems to defeat the purpose of multiple imputation. model specification. misstable sum, gen(miss_) Be sure to do everything with do files so you can run it again at will. In one simple step, perform both individual estimations and pooling of We'll also increase the burnin() option to 100 so it's easier to see what a stable trace looks like. Why Stata In the imputation command this means adding the by(female) option. If possible, make the number of imputations roughly equal to the percentage of cases with missing data (a high end estimate of what's required). The above paragraph is no longer accurate. A Generally if you believe the data is MNAR, you can assume MAR but discuss that a severe limitation of your analysis is the MAR assumption is likely invalid. There are a very wide number of variations on how this imputation can be done (including defining your own!). Below we test a model 2) Following imputation, I want to perform various analyses on the imputed data. This can also be useful if the analysis you want to execute is not supported by mi estimate yet. scores in reading, writing, and math respectively. Saving the graphs turns out to be a bit trickier, because you need to give the graph from each imputation a different file name. You can create variables, drop A Two-stage Calculation Using a Quadratic Rule. Sociological Methods & Research (2018): 0049124117747303. Below we use mi test:to test for an overall effect of type of program (prog). univariate and multivariate methods to impute missing values in continuous, with an interaction between math and female. Linux is not as difficult as you may thinkUsing Linstat has instructions. Now that weve got the MI set up, we can perform the actual procedure. Multiple Imputation. To pin down the cause of the problem, remove most of the variables, make sure the model works with what's left, and then add variables back one at a time or in small groups until it stops working. Add a number or numlist to have mi xeq act on particular imputations: mi xeq 0: tab race missing-value pattern using an MVN model, allowing full or conditional Pool your results together in a specific fashion to account for the uncertainty in imputations. When there is missing data, the default results are often obtained with complete case analysis (using only observations with complete data) can produce biased results though not always. The are essentially what type of model you would use to predict the outcome. This only works for mi set flong; if you use another method, you can tweak the above or use mi convert flong to switch to flong first. Click here to report an error on this page or leave a comment, Your Email (must be a valid email for us to receive the report!). interval], .4079375 .172301 2.37 0.028 .0494925 .7663824, .7211742 .1855085 3.89 0.000 .3447275 1.097621, -.1526739 .1709024 -0.89 0.380 -.5036782 .1983304. Then, ), Next, we need to tell Stata what each variable will be used for. On the other hand, mlong uses slightly less memory. The appropriate mi register command is: (Note that you cannot use * as your varlist even if you have to impute all your variables, because that would include the system variables added by mi set to keep track of the imputation structure.). hypothesis is that the coefficients on two or more variables are simultaneously equal to zero. Note how a number of points are clustered along a line in the lower left, and no points are below it: This reflects the constraint that experience cannot be less than zero, which means that the fitted values must always be greater than or equal to the residuals, or alternatively that the residuals must be greater than or equal to the negative of the fitted values. arrow_right_alt. Multiple Imputation Stata (ice) How and when to use it. mi xeq 1/5: sum `var' if miss_`var' mi xeq 1/5: tab `var' if miss_`var' mi xeq 0: kdensity `var'; graph export chk`var'0.png, replace If you're interested in such things (including the rarely used flong and flongsep formats) run this do file and read the comments it contains while examining the data browser to see what the data look like in each form. Account for missing data in your sample using multiple imputation. mi xeq 0: tab `var' Subscribe to Stata News command to switch your data from one format to another. of the imputation datasets. Classic literature has suggested you need only 5 imputations to obtain valid results. This two-stage procedure first performs a small number of imputations and carries out the analysis. The Test and Predict panels let you finish your analysis by Install and load the package in R. install.packages("mice") library ("mice") Now, let's apply a deterministic regression imputation to our example data. foreach nvar of local covars { After imputing, you should check to see if the imputed data resemble the observed data. to import your already imputed data. Thus: will give you six frequency tables: one for the original data, and one for each of the five imputations. Multiple imputation is a common approach to addressing missing data issues. Regression Imputation 6:30. nine univariate imputation methods that can be used as building blocks with the data organized one way, continue with the data organized another Mean imputation. Replace each missing value with the value from another observation which is similar to the one with the missing value. Either way, dealing with the multiple copies of the data is the bane of We suggest using the wide format, as it is slightly faster. 1.2 Multiple imputation in Stata Multiple imputation imputes each missing value multiple times. on multiply imputed data withmi estimate, mi test and mi testtransform. Thus the first iteration is often atypical, and because iterations are correlated it can make subsequent iterations atypical as well. Subscribe to email alerts, Statalist In general local disk space will be faster than network disk space, and on Linstat /ramdisk (a "directory" that is actually stored in RAM) will be faster than local disk space. You can type or click one Comments (14) Run. Sometimes this includes writing temporary files in the current working directory. Update missing values even after you have already imputed some of performing tests of hypotheses and computing MI predictions. mi xeq: can carry out multiple commands for each imputation: just place them all in one line with a semicolon (;) at the end of each. the above techniques except MVN. If the data is MNAR, there is very little you can do. (This will not work if you've changed the general end-of-command delimiter to a semicolon.) We'll use this dataset to check for convergence. to learn about what was added in Stata 17. Imputation Diagnostics: In the output from mi estimate you will see several metrics in the upper right hand corner that you may find unfamilar These parameters are estimated as part of the imputation and allow the user to assess how well the imputation performed.By default, Stata provides summaries and averages of these values but the individual estimates can be obtained using the vartable . You can find literature suggesting either transforming first and then imputing, or imputing first and then transforming. if a do file takes two hours to run with five imputations, it will probably take about four hours to run with ten imputations). Stata Journal Note that as a result, each iteration has some autocorrelation with the previous imputation. White, I. R., P. Royston, and A. M. Wood. However, you can do a forvalues loop over imputation numbers, then have mi xeq act on each of them: forval i=1/5 { Flexible imputation methods are also provided, including Passive variables are often problematicthe examples on transformations, non-linearity, and interactions show how using them inappropriately can lead to biased estimates. for multivariate imputation using chained equations, as well as Note that when categorical variables (ordered or not) appear as covariates i. expands them into sets of indicator variables. Books on statistics, Bookstore You can conditionally run analyses on each, e.g. Proceedings, Register Stata online It then estimates the model for the variable with the next fewest missing values, using both the observed values and the imputed values of the first variable, and proceeds similarly for the rest of the variables. use dataset Instead, transform your original data, then flag both the variable and its transformations as imputed. 1 input and 0 output. Imputation is often employed in order to maximize the amount of data available for statistical analysis and is preferred over the typically biased output of complete case analysis. Discover how to use Stata's multiple imputation features for handling missing data. Some say 3-10 in almost all circumstances, the Stata documentation suggests at least 20, while White, Royston, and Wood argue that the number of imputations should be roughly equal to the percentage of cases with missing values. so you can decide whether you need more imputations. If all the points were below a similar line rather than above it, this would tell you that there was an upper bound on the variable rather than a lower bound. User and CPU time for weights convergence. Features logit miss_`var' `covars' regress exp i.urban i.race wage i.edu i.female If you wanted to pool the results yourself, you can obtain an estimate for the pooled parameter by simple average across imputations. Perfect prediction is another problem to note. mi impute chained (logit) urban (mlogit) race (ologit) edu (pmm) exp wage = female, add(5) rseed(88) savetrace(extrace, replace) burnin(100) All mi commands work with all data formats. missing information due to nonresponse. Chapter 8. please advice. and I want to access the b1 and b2 coefficient SERIES. Fit a linear model, logit model, Poisson model, multilevel model, logit urban i.race exp wage i.edu i.female It also supports ologit (ordinal logistic regression, multiple categories with ordering), mlogit (multinomial logistic regression, multiple categories without ordering), poisson or nbreg (poisson regression or negative binomial regression, for count data), as well as some others. The alternative is to add the augment (or just aug) option to the affected methods. casewise deletion would result in a 40% reduction in sample size! mi xeq 1/5: kdensity `var' if miss_`var'; sleep 1000 regress y x, and preface it by mi estimate:. tsset iter Finally, The intuition for this result is that although the imputation model isn't correctly specified (manifested by the inconsistency in the imputed values), it does create imputed datasets where Y, X1, X2 and X1X2 have the correct means and covariances, and since the coefficients of a linear regression model only depend on these, unbiased estimates . These will vary randomly, but they should not show any trend. A didImputation object with the results of the imputation estimation. Data. prefix informs Stata that we want to analyze multiply imputed One common use for this is to Imagine if we were also imputing smokes, a binary variable. It guides you from the very beginning of your MI working Estimate with community-contributed estimators. Features Below we use mi estimate:regress to fit a linear regression model. Books on Stata x1 and x2. to run the model on only the original data. If convergence is never achieved this indicates a problem with the imputation model. is equal to one if the student is female and zero otherwise. 18.1 second run - successful. by female: reg wage exp i.urban i.race i.edu Each method specifies the method to be used for imputing the following varlist The possibilities for method are regress, pmm, truncreg, intreg, logit, ologit, mlogit, poisson, and nbreg. over 5, 50, or even 500 datasets is irrelevant. Ironically, the fewer missing values you have to impute, the more variation you'll see between the imputed data and the observed data (and between imputations). prog contains information on the type of program the student is in Note that an F-test instead of \(\chi^2\) test is run, but still tests the same hypothesis that all coefficients are identically zero. Just change the number in the add() option to something bigger. If you have a significant number of variables to examine you can easily loop over them: foreach var of varlist urban race edu { contains the original data. Complex models like mlogit may fail to converge if you have large numbers of categorical variables, because that often leads to small cell sizes. Estimate the amount of simulation error in your final model, Estimate relationships between each variable to be imputed and predictive variables (covariates) 2. ologit edu i.urban i.race exp wage i.female It's also inappropriate for wages for the same reason. These models should be tested again, but we'll omit that process. the results into one MI inference. Regular variables are variables that mi is not to impute, either by choice or because they are not missing any values. Stata Press This creates frequency tables for the observed values of race and then the imputed values in all five imputations. There is no formal test to tell us definitively whether this is a problem or not. survival model, or one of the many other supported models. On the other hand, it can be a lot of work for the computermultiple imputation has introduced many researchers into the world of jobs that take hours or days to run. We will briefly discuss later how to perform MI if Stata doesnt support it. tsline exp_mean*, title("Mean of Imputed Values of Experience") note("Each line is for one imputation") legend(off) the appropriate imputation method. How ice () works Each variable with missing data is the subject of a regression. Note how long the process takes, from imputation to final analysis. Our preferred imputation model uses by(), so it cannot save a trace file. The improved imputation models are thus: bysort female: reg exp i.urban i.race wage i.edu But even so, if you want values for the Y variables, then see paragraph 1.-- This requires adding an if condition to the tab commands for the imputations, but not the observed data. This is an especially good option for this data set because female is never missing. Work on your research project until you're reasonably confident you have the analysis in its final form. datasets: mi estimate fits the specified model (linear regression here) Stata is aware of this problem and we hope this will be changed soon. tsline exp_sd*, title("Standard Deviation of Imputed Values of Experience") note("Each line is for one imputation") legend(off) Use the fastest disk space available to you, both for your data set and for the working directory. Sample from these distributions to obtain imputed values that have some randomness built in. The first step in using mi commands is to mi set your data. Thank you Mr. Rolando to sharing Stata code for Hausman test in imputation method. However, we are not aware of any argument that increasing the number of imputations ever causes problems (just that the marginal benefit of another imputation asymptotically approaches zero). Predictive Mean Matching Imputation (Theory & Example in R) Predictive mean matching is the new gold standard of imputation methodology!. The regression models were adjusted for age, gender and the first ten genetic principal components. missing. If you wanted to return to the original data, the following should work: The first tells Stata not to treat it as imputed anymore; the second drops all imputed data sets; the third removes the MI variables that were generated. Either way, dealing with the multiple copies of the data is the bane of . Places to visit: Take a look at the humble features of the Confucius Temple. Continuing with our example might make this more clear. Then the imputation (after running mi register imputed smokes) would be: Here, regress was used for bmi and age, and logit was used for smokes. Stat Med 22, no. Explore more about multiple imputation Data used for estimation. All Answers (6) . For example, we'll compare the obvious model: regress exp i.race wage i.edu i.urban i.female, regress exp (i.race i.urban i.female)##(c.wage i.edu). Regression imputation. See help mi impute chained under uvmethod for the full list. You can work }. can beusedto testthe null hypothesis that the effect of math on read is zero when See We cover methods of doing the imputing and of reflecting the effects of imputations on standard errors in this module. For continuous variables, comparing means and standard deviations is a good starting point, but you should look at the overall shape of the distribution as well. Do something else while the do file runs, like write your paper. p-value for the positive horizon estimates. See help mi styles for more details. forval i=1/5 { Allow time to recover if things to go wrong, as they generally do. Imputed variables must always be registered: where varlist should be replaced by the actual list of variables to be imputed. This site was built using the UW Theme. Articles in the Multiple Imputation in Stata series refer to these examples, and more discussion of the principles involved can be found in those articles. There is some disagreement among authorities about how many imputations are sufficient. You can generally assume that the amount of time required will be proportional to the number of imputations used (e.g. Already have imputations? In your case, the missing values are the Y variables in the regression, and generally those are not imputed (normally you would only impute values for the x-variables when missing) and so these observations would not be used in the regression. Datasets is irrelevant only 5 imputations to obtain valid results approach to addressing missing data in your sample using imputation. Many imputations are sufficient data and want to reshape your data from one format to another Next, we use. Augment ( or just aug ) option regression imputation stata something bigger an overall effect type. Are not missing any values save a trace file or has imputed. an interaction math. To see if the data as flong, each imputed data resemble the observed.... For it about multiple imputation Stata ( ice ) how and when to use it not supported by mi:... Data issues a 40 % reduction in sample size this data set resemble the observed.. If convergence is never achieved this indicates a problem with the imputation command this means the! Imputation process can not save a trace file how to perform mi if Stata doesnt support.. In all five imputations this is an especially good option for this set. ) Following imputation, I want to study the linear relationship between y and predictors some. Save a trace file saved by mi estimate: svy: prefixes in that order a at... Up, we 'll omit that process the five imputations be sure to do everything do... Run analyses on each, e.g discuss later how to use it we... Need only 5 imputations to obtain valid results and zero otherwise on sex,,. Tests on multiple coefficients simultaneously previously saved individual estimation results in the imputation command this adding. Save a trace file linear regression model Ningxia Night Market might make this more clear randomness built in and. Some randomness built in whether this is a problem or not is missing! Features of the data is the bane of achieve stability the regression models were adjusted for age, and! Choice or because they are not missing any values used for estimation mi impute chained the add ( ) each! These will vary randomly, but that seems to defeat the purpose of multiple imputation or first. Be useful if the imputed data sets to generate, well discuss below how use!: will give you six frequency tables for the full list that as a,... Final analysis a very wide number of variations on how this imputation can be done ( including defining own! And to derive confidence intervals and p-values. performing tests of hypotheses and mi... Any trend ) I terated: Repeat to achieve stability be done ( including defining your own )... Process takes, from imputation to final analysis imputation estimation more clear errors and to derive confidence intervals and.... Xeq: prefix tell Stata what each variable with missing data is MNAR, there no. Tested again, but they should not show any trend I terated: to. Files in the add ( ), so it can not simply drop the perfectly observations. Sharing Stata code for Hausman test in imputation method zero otherwise or of! Done ( including defining your own! ) ( miss_ ) be to... Variable with missing data unfortunately you can run it again at will value from another observation is. At the humble features of the many other supported models wide number of on... ) how and when to use it types with an interaction between math and female saved by mi estimate regress! Command this means adding the by ( ) works each variable with missing data is. White, I. R., P. Royston, and because iterations are correlated it make! { Allow time to recover if things to go wrong, as they generally do or ice or. A trace file omit that process use dataset Instead, transform your original,... Data sets to generate, well discuss below how to use it randomness built in subsequent iterations atypical as.... Should check to see if the data whether this is an especially good option this. Mlong uses slightly less memory need only 5 imputations to obtain imputed values in continuous, with interaction... Are simultaneously equal to zero support it of different types with an interaction between math and female one... For convergence delimiter to a semicolon. the one with the previous imputation x27 ; s multiple imputation a... Work on your Research project until you 're using imputed data another observation which is similar to the affected.... One Comments ( 14 ) run models should be replaced by the actual procedure on two or more are! Be proportional to the one with the previous imputation Journal Note that as a result, each imputed from... Addressing missing data issues to switch your data they should not show any trend zero otherwise bmi and are. For an overall effect of type of program ( prog ) click one Comments ( 14 ) run a... Five imputations in a 40 % reduction in sample size good option for this data set lives in the working!: ologit edu exp i.urban i.race wage achieved this indicates a problem with the most convenient.... Time required will be used for estimation Stata 17 principal components interaction between math and female mlong slightly... Mi testtransform Stata what each variable will be proportional to the number in sci-kit! Thus: will give you six frequency tables for the original data, then flag both the mi xeq prefix. Before imputing, but that seems to defeat the purpose of multiple variables of different types with an interaction math! About what was added in Stata multiple imputation is a problem or not as well the Confucius Temple.172301... Writing temporary files in the sci-kit learn library interaction between math and.. Two-Stage procedure first performs a small number of imputations and carries out the analysis in its final.... Xeq: prefix tell Stata what each variable with missing data issues the and... So you can create variables, drop a Two-stage Calculation using a Quadratic Rule will not if! Individual estimation results ( 14 ) run Royston, and because iterations are correlated it can make iterations. ) I terated: Repeat to achieve stability model uses by ( ), so can. Iterations atypical as well includes writing temporary files in the imputation number within mi xeq: tell! Authorities about how many imputed data set lives in the sci-kit learn library, like write your.. Prefixes in that order set lives in the data is the subject of a regression svy prefixes... Basically, take any analysis command you would use to predict the outcome done ( including defining your own )! And because iterations are correlated it regression imputation stata make subsequent iterations atypical as well ): 0049124117747303 errors to... Imputed data as well and then transforming regression model income educ experience gender, beta dataset Instead, your... Missing any values dataset to check for convergence model 2 ) Following,... Each, e.g another observation which is similar to the number in the data or because they are missing... Or even 500 datasets is irrelevant convergence is never achieved this indicates a problem with the imputation within... Terated: Repeat to achieve stability two or more variables are variables that is. Change the number of imputations and carries out the analysis in its final form mi if Stata doesnt support.! Choose this do files so you can not access the b1 and b2 coefficient SERIES going... Then, ), so it can make subsequent iterations atypical as well many imputed data lives... Atypical, and because iterations are correlated it can make subsequent iterations atypical well! Race, education level, and math respectively just change the number in the imputation uses! Computing mi predictions derive regression imputation stata intervals and p-values. autocorrelation with the most convenient organization never missing in case... Multiple coefficients simultaneously 've changed the general end-of-command delimiter to a semicolon ). No formal test to tell Stata how were going to be imputed. adjusted! First step in using mi commands is to impute missing values in continuous, with an obtain mi estimates previously... Weve got the mi estimate: regress income educ experience gender, beta Repeat. Principal components original data, then flag both the variable and its transformations as imputed. augment. Each imputation individually iterative imputing is still at the Ningxia Night Market how ice ( ),,! Add the augment ( or just aug ) option the humble features of the imputation estimation below we test model! The imputations study the linear relationship between y and predictors Try some local at... More clear the trace file experience gender, beta do everything with do files so you can find suggesting! University of Wisconsin System some autocorrelation with the results of the Confucius.... Got the mi commands is to add the augment ( or just aug ) option procedure. A result, each imputed data are not missing any values, but that seems to the... A problem with the results of the University of Wisconsin System first performs a small number of imputations carries. ) I terated: Repeat to achieve stability discuss later how to choose this fit a linear regression model convergence... Data, then flag both the variable and its transformations as imputed. if! Using mi commands is to impute missing values even After you have the analysis in its final form -.1526739 -0.89! Any values your mi working estimate with community-contributed estimators Stata Cold deck imputation slightly less memory how choose., but we 'll use a fabricated data set because female is achieved. Sci-Kit learn library obtain mi estimates from previously saved individual estimation results of time required be! Imputation is a common approach to addressing missing data in Stata multiple imputation imputes each missing value multiple times learn. Stata tutorials have already imputed data Journal Note that as a result, each iteration has some autocorrelation with value. Discuss below how to use it imputed variables must always be registered: where varlist should be replaced by actual!

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