STATISTICS Seminars Series: Tilman Bretschneider

Topic Factor-based imputation of missing values using cross-section averages

Abstract There is a fast-growing literature developing factor-based imputation methods of missing values in panel datasets. These methods typically estimate factors by applying principal component analysis to an observed subset of the data. In this paper, we propose a new method that uses cross section averages as factor estimates, obtains loadings by linear projections, and imputes missing values with their inner product. The main motivation behind our approach is its straightforward implementation and flexibility to accommodate general missing patterns. We derive the asymptotic properties of estimated factors, loadings, and imputed values while allowing the true number of factors to be overspecified. We showcase the good small sample properties and performance in a Monte Carlo study and in an imputation exercise with macroeconomic data. Finally, we illustrate how our method can also be used to construct monthly state-level GDP indexes from monthly macroeconomic data for a panel of U.S. states.