multiple imputation

Multiple Imputation (MI) is a method for dealing with missing data in a statistical analysis. The general idea of MI is to simulate values for missing data points using the data we have on hand, generating multiple new sets of complete data. We then run our proposed analysis on all the complete data sets and combine the results to obtain overall estimates. The end product is an analysis with proper standard errors and unbiased estimates.

Whenever we are dealing with a dataset, we almost always run into a problem that may decrease our confidence in the results that we are getting - missing data! Examples of missing data can be found in surveys - where respondents intentionally refrained from answering a question, didn’t answer a question because it is not applicable to them, or simply forgot to give an answer. Or our dataset on trade in agricultural products for country-pairs over years could suffer from missing data as some countries fail to report their accounts for certain years.