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Estimates of the approximate factor model are increasingly used in empiricalwork. Their theoretical properties, studied some twenty years ago, also laidthe ground work for analysis on large dimensional panel data models withcross-section dependence. This paper presents simplified proofs for theestimates by using alternative rotation matrices, exploiting properties of lowrank matrices, as well as the singular value decomposition of the data inaddition to its covariance structure. These simplifications facilitateinterpretation of results and provide a more friendly introduction toresearchers new to the field. New results are provided to allow linearrestrictions to be imposed on factor models.

Simpler Proofs for Approximate Factor Models of Large Dimensions