This paper suggests an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Under a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence. A complete characterization of the sampling error is obtained without requiring regularization or imposing the missing at random assumption. Under the assumption that potential outcome has a factor structure , we provide a distribution theory for the estimated average and individual treatment effects on the treated .
This paper proposes an imputation procedure that uses the factors estimated from a 'tall' block along with the re-rotated loadings estimated from a 'wide' block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks , it is shown that the common component can be consistently estimated at four different rates of convergence . An asymptotic analysis of the estimation error is obtained without requiring regularization or assuming missing at random . We also consider estimation of average and individual treatment effects on the treated under the assumption that potential outcome has a factor structure . A normal distribution theory is obtained that can be useful for hypothesis testing .
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