Factor Modeling for High-Dimensional Interval-Valued Data

Abstract The paper considers an approximate factor model for interval-valued panel data with both large numbers of cross-section units and time series observations. A ratio-type estimator is proposed for the number of interval-valued factors in the approximate factor model. A variant of the estimator is also suggested, which is robust to the case with dominant factors. Under certain conditions, the estimators can be proved to be consistent. Moreover, the estimators of interval-valued factors and the pooled loadings can be obtained by the principal component analysis method for point-valued data. Monte Carlo simulation studies show that the proposed estimators have the desired finite sample properties.