Detecting heterogeneity within a population is crucial in many economic and financial applications. Econometrically, this requires a credible determination of multimodality in a given data distribution. We propose a straightforward yet effective technique for mode inference in discrete data...

This paper makes a twofold contribution, First, it develops the dynamic factor model of Barigozzi et al. (2016) by allowing for fractional integration instead of imposing the classical dichotomy between I(0) stationary and I(1) non-stationary series. This more general setup provides valuable...

This paper investigates the feasibility of using earlier provisional data to improve the now- and forecasting accuracy of final and official statistics. We propose the use of a multivariate structural time series model which includes common trends and seasonal components to combine official...

This paper proposes a long-memory model including multiple cycles in addition to the long-run component. Specifically, instead of a single pole or singularity in the spectrum, it allows for multiple poles and thus different cycles with different degrees of persistence. It also incorporates...

This paper examines persistence and nonlinearities in the US Federal Funds rate over the period from July 1954 to April 2025 by using fractional integration methods. More precisely, a general model including both deterministic and stochastic components is estimated under alternative assumptions...

This paper applies a fractional integration framework to investigate the behaviour of the stock returns of two sets of representative US companies with different environmental profiles, namely green versus polluting firms, as well as of the widely used CPU (Climate Policy Uncertainty) index over...