Generating counterfactuals through treating a variable as a function of its own past values or treating a variable as a function of other units, typically being referred to as horizontal or vertical regression, respectively, is widely used in the panel measurement of treatment effects. However, their inferences are often based on different assumptions for the data generating process. We consider unifying the underlying assumptions of the two approaches by a factor approach and compare their respective predictive power in terms of the sample configuration of the cross-section dimension N and the time dimension T.