  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 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 of
variable specific length are then used to estimate the factor loadings. A
missing value is imputed as the estimated common component which we show is
consistent and asymptotically normal without further iteration. Implications
for using imputed data in factor augmented regressions are then discussed.
  To compensate for the downward bias in covariance matrices created by an
omitted noise when the data point is not observed, we overlay the imputed data
with re-sampled idiosyncratic residuals many times and use the average of the
covariances to estimate the parameters of interest. Simulations show that the
procedures have desirable finite sample properties.
