Interpreting (and testing) factor loadings

Dynamic Factor Models (DFMs) are popular to reduce dimensionality being customary in the empirical analysis of large systems of macroeconomic and/or financial variables.

In this context, the common underlying factors and their loadings are often extracted using Principal Components (PC), which are consistent and asymptotically normal under very general conditions.

Consequently, inference on the factor loadings, which is crucial for the correct interpretation of the underlying factors, is often based on their asymptotic distribution with the limit covariance matrix of the loadings consistently estimated using HAC estimators.

In this paper, we analyse the performance of the finite sample asymptotic approximation when constructing confidence intervals and testing about estimated PC loadings.

We show that this approximation is seriously affected when the cross-sectional dimension is not large enough.

We propose using HAR inference and a subsampling procedure to correct the MSE of the loadings to take into account the uncertainty associated with the estimation of the covariance matrix and of the factors, respectively.

The relevance of the results is illustrated in an empirical analysis of economic convergence among the US states.