Data truncation is a process that involves the exclusion of certain data values from a dataset, resulting in a greater loss of information than censoring. It is similar to but different from the concept of statistical censorship, where a note is recorded documenting which limit (upper or lower) has been exceeded and the value of that limit. With truncation, no note is recorded and values are limited above or below, resulting in a truncated sample. Different databases use different truncation symbols, so it is important to check the information in the database “Help” or “Search Tips” for details on which symbol to use. In databases and computer networks, data truncation occurs when data or a data stream (such as a file) is stored in a location that is too short to maintain its full length.

In econometrics, truncated dependent variables are variables for which observations cannot be made for certain values in some range. The analysis of data in which observations are treated as if they were from truncated versions of standard distributions can be performed using the maximum probability, where the probability would be derived from the distribution or density of the truncated distribution. Modeling median income values would involve truncating income above and below specific amounts. The estimation of such a truncated regression model can be done in parametric or semiparametric and non-parametric frames. If you want to compare truncated regression models, you can run the static command to get the log probability, AIC, and BIC values. In conclusion, data truncation is an important concept to understand when dealing with datasets.

It involves excluding certain data values altogether, resulting in a greater loss of information than censoring. Different databases use different truncation symbols, so it is important to check the information in the database “Help” or “Search Tips” for details on which symbol to use.