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Economists are blessed with a wealth of data for analysis, but more oftenthan not, values in some entries of the data matrix are missing. Variousmethods have been proposed to handle missing observations in a few variables.We exploit the factor structure in panel data of large dimensions. Our\textsc{tall-project} algorithm first estimates the factors from a\textsc{tall} block in which data for all rows are observed, and projections ofvariable specific length are then used to estimate the factor loadings. Amissing value is imputed as the estimated common component which we show isconsistent and asymptotically normal without further iteration. Implicationsfor using imputed data in factor augmented regressions are then discussed.  To compensate for the downward bias in covariance matrices created by anomitted noise when the data point is not observed, we overlay the imputed datawith re-sampled idiosyncratic residuals many times and use the average of thecovariances to estimate the parameters of interest. Simulations show that theprocedures have desirable finite sample properties.

Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions