Jan P. A. M. Jacobs

Cattel’s (Multivar Behav Res 1:245–276, 1966) heuristic determines the number of factors as the elbow point between ‘steep’ and ‘not steep’ in the scree plot. In contrast, an elbow is by definition absent in points on a hyberbole with corresponding equisized surfaces. We formalize this heuristic and propose a criterion to determine the number of factors by comparing surfaces under the scree plot. Monte Carlo simulations shows that the finite-sample properties of our proposed criterion outperform benchmarks in the dynamic factor model literature.

Multivariate analysis can help to focus on important phenomena, including trend and cyclical movements, but any economic information in seasonality is typically ignored. The present paper aims to more fully exploit time series information through a multivariate unobserved component model for quarterly data that exhibits seasonality together with cross‐variable component correlations. We show that economic restrictions, including common trends, common cycles and common seasonals can aid identification. The approach is illustrated using Italian GDP and consumption data.

We developed several measures to analyze the global financial cycle employing dynamic factor models and data for 25 advanced and emerging countries spanning 1980 to 2019. These measures were assessed using the similarity and synchronicity metrics proposed by Mink et al. (Oxford Economic Papers 64, 217–236, 2012). The findings indicate a strong similarity and synchronization of global cycles in asset prices and capital flows, particularly evident during crisis episodes. Furthermore, we observe significant co‐movement between our financial cycle measures and two literature‐based measures that utilize top‐down and bottom‐up approaches. However, the VIX index shows a lower level of co‐movement with our global financial cycle measures.

We examine the dynamic impact of household borrowing on the trade balance using data from 32 developing countries and 36 developed countries over the 1980-2020 period. Our findings suggest that the impact of household borrowing on the trade balance is negative, both in the short and long run, but the effects are more pronounced in developing countries. Moreover, we find that for developing countries the negative effect of household borrowing on the trade balance is achieved via boosting imports. In developed countries, household borrowing stimulates both imports and exports, where the effect on imports is larger.