Evaluating Restricted Common Factor models for non-stationary data
We propose to evaluate restrictions on the loadings of approximate Factor models comparing the estimated number of factors of the unconstrained and constrained models. A difference between the two estimates is evidence against the constraints, which should thus be rejected. To take into account possible finite sample bias of the model selection procedure, we develop a bootstrap algorithm for the estimation of the probability of rejecting cor- rect constraints. For non-stationary factor models we show analytically that the algorithm is asymptotically valid, and by simulation that the evaluation procedure has good small sample properties.
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