Account for missing data in your sample using multiple imputation. Choose from univariate and multivariate methods to impute missing values in continuous, censored, truncated, binary, ordinal, categorical, and count variables. Then, in a single step, estimate parameters using the imputed datasets, and...
S458918 MIMPT: Stata module to impute missing values and persist in case of non-convergence by Daniel Klein S458917 QREGPLOT: Stata module for plotting coefficients of a Quantile Regression by Fernando Rios-Avila S458916 TVGC: Stata module to perform Time-Varying Granger Causality tests by Jesú...
Missing data occur frequently in practice. MI is one of the most flexible ways of handling missing data. Its three stages are multiply imputing missing values, estimating model parameters from each imputed dataset, and combining multiple estimation results in one final inference. In Stata, you ...
S458759 IVREG_SS: Stata module to compute confidence intervals, standard errors, and p-values in an IV regression in which the excluded instrumental variable has a shift-share structure byRodrigo Adão & Michal Kolesár & Eduardo Morales & Xiang Zhang S458758 REG_SS: Stata module to compute ...
Logistic Regression using imputed values. Coefficients and Standard Errors Corrected N = 1141 Log Likelihood for component regression no.1= -650.85724. Log Likelihood for component regression no.2= -650.95298. (output omitted ) Log Likelihood for component regression no.9= -650.91123. Log Likelihood...
By construction, the standard errors produced by are always larger than those produced by ; see Methods and formulas in [R] regress postestimation. calculates the expected value of the dependent variable conditional on the presence of the treatment: treatment 1. calculates the expected value of ...
Data management: How can I replace missing values with previous or following nonmissing values? (Added 23 August 2000) Data management: Why can't I compare two values that I know are equal? (Added 27 July 2000) Statistics: Must I use all of my exogenous variables as instruments when ...
How does theanovacommand handle collinearity? 2. Bayesian analysis How can I run multiple Markov chains in parallel? 3. Binary outcome qualitative dependent variable models How do I fit a bivariate probit model with partial observability and a single dependent variable?
imputed data ›› Importing existing multiply imputed data ›› Verifying multiply imputed data ›› Variable management (passive variables) ›› Merging, appending, and reshaping multiply imputed data ›› Exporting multiply imputed data to a non-Stata application • Estimation ›...