This paper suggests a factor-based 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 treatment effect on the treated . The relation between incoherence conditions used in matrix completions and the strong factor assumption is also discussed .
s 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 .
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This paper suggests a factor-based
s paper suggests an
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TALL
tall
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WIDE
wide
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treatment effect
average and individual treatment effects
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. The relation between incoherence conditions used in matrix completions and the strong factor assumption is also discussed