data-cleaning

Cleaning survey data for econometric analysis involves detecting and fixing quality issues, handling missing values, removing duplicates, and managing outliers. This creates analysis-ready panels with labeled variables and codebooks for replication.

Handling missing values and outliers in Python or R requires applying automated data cleaning pipelines to detect and resolve these issues. This creates derived variables and analysis-ready panels while documenting transformations for replication.

Ensuring reproducibility when transforming administrative data requires generating a codebook, labeling variables, and using versioned scripts. This documents all transformations and data quality fixes to support complete replication.

Yes, you can apply automated data cleaning pipelines with Stata to process administrative datasets. The pipeline detects and fixes common quality issues, creates derived variables, and generates codebooks for econometric cleanup and replication.

To document data transformations for a replication codebook, you generate labeled variables and versioned scripts alongside your cleaning pipeline. This records all handling of missing data, duplicates, and outliers to satisfy reproducibility requirements.