Macroeconomic practitioners frequently work with multivariate time series models such as
VARs, factor augmented VARs as well as time-varying parameter versions of these models …

Andrieu et al.(2010) prove that Markov chain Monte Carlo samplers still converge to the
correct posterior distribution of the model parameters when the likelihood estimated by the …

Trend–cycle decompositions for US real GDP such as the unobserved components models,
the Beveridge–Nelson decomposition, the Hodrick–Prescott filter and others yield very …

There are a large number of tests for instability or breaks in coefficients in regression models
designed for different possible departures from the stable model. We make two contributions …

Reliable demand forecasts are critical for effective supply chain management. Several
endogenous and exogenous variables can influence the dynamics of demand, and hence a …

Time series subject to parameter shifts of random magnitude and timing are commonly
modeled with a change-point approach using Chib's algorithm to draw the break dates. We …

The presence of the African Stock Markets (ASMs) in the global frontier markets indices
confirms their global portfolio diversification role. This study investigates the asymmetric and …

This article provides guidance on how to evaluate and improve the forecasting ability of
models in the presence of instabilities, which are widespread in economic time series …

Los avances recientes en el análisis de series de tiempo reflejan una creciente necesidad
de desarrollar modelos que permitan capturar sus diversas características. Tal es el caso de …

In this paper a robust approach to modeling electricity spot prices is introduced. Differently
from what has been recently done in the literature on electricity price forecasting, where the …