  Economists are blessed with a wealth of data for analysis, but more often
than not, values in some entries of the data matrix are missing. Various
methods have been proposed to impute missing observations with iterative
estimates of their unconditional or conditional means. We exploit the factor
structure in panel data of large dimensions. We first use a series of
projections of variable specific length to impute the common component
associated with missing observations and show that this immediately yields a
consistent estimate without further iteration. But setting the idiosyncratic
errors to zero will under-estimate variability. Hence in a second imputation,
we inject the missing idiosyncratic noise by resampling to obtain a consistent
estimator for the convariance matrix, which plays an important role in risk
management. Simulations calibrated to CRSP returns over the sample 1990-2018
are used to show that the double imputation methodology significantly improves
various performance measures over single imputation. Implications for using
