We propose to evaluate exclusion or homogeneity restrictions on the loadings in non-stationary factor models on the basis of the number of factors estimated for the partially data de-factored under the null hypothesis, with the probability of rejecting true null hypothesis estimated by the bootstrap. Simulation results suggest the procedure has good properties and may thus be a valuable tool for applied factor modelling of non-stationary data.
Evaluating Restricted Common Factor models for non-stationary data / DI IORIO, Francesca; Fachin, S.. - (2016). (Intervento presentato al convegno Third Annual Conference the International Association for Applied Econometrics IAAE 2016 tenutosi a University of Milano-Bicocca (Italy) nel June 22-25, 2016).
Evaluating Restricted Common Factor models for non-stationary data
DI IORIO, FRANCESCA;
2016
Abstract
We propose to evaluate exclusion or homogeneity restrictions on the loadings in non-stationary factor models on the basis of the number of factors estimated for the partially data de-factored under the null hypothesis, with the probability of rejecting true null hypothesis estimated by the bootstrap. Simulation results suggest the procedure has good properties and may thus be a valuable tool for applied factor modelling of non-stationary data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
