Regression imputation for space-time datasets with missing values

Data consisting in repeated observation on a series of fixed units are very common in different context like biological, environmental and social sciences, and different terminology is often used to indicate this kind of data: panel data, longitudinal data, time series-cross section data (TSCS), spatio-temporal data.Missing information are inevitable in longitudinal studies, and can produce biased estimates and loss of powers. The aim of this paper is to propose a new regression (single) imputation method that, consideringthe particular structure and characteristics of the data set, creates a “complete” data set that can be analyzed by any researcher on different occasions and using different techniques. Simulated incomplete data from aPM10 dataset recorded in Palermo in 2003 have been generated, in order to evaluate the performance of the imputation method by using suitable performance indicators.