A regression-based algorithm is proposed that allows to construct arbitrarily many
comparable, multi-annual, consistent time series on monthly, weekly, daily, hourly, and …

US yield curve dynamics are subject to time‐variation, but there is ambiguity about its
precise form. This paper develops a vector autoregressive (VAR) model with time‐varying …

Time-varying parameter (TVP) regression models can involve a huge number of coefficients.
Careful prior elicitation is required to yield sensible posterior and predictive inferences. In …

Turning points in financial markets are often characterized by changes in the direction
and/or magnitude of market movements with short-to-long term impacts on investors' …

This chapter describes Bayesian methods for macroeconomic forecasting using VARs. It
covers various priors which have been proposed which achieve the shrinkage and …

The mixture innovation (MI) model places a spike-and-slab mixture distribution for the
innovations of time-varying regression coefficients and permits flexible time variation …

This article studies the time-varying parameter (TVP) regression model in which the
regression coefficients are random walk latent states with time-dependent conditional …

In order to solve the problems of low selection accuracy, high cost and low tourist
satisfaction in traditional methods, this paper proposes an individualised travel route …

US yield curve dynamics are subject to time-variation, but there is ambiguity on its precise
form. This paper develops a vector autoregressive model with time-varying parameters and …

Time-varying parameter (TVP) regressions commonly assume that time-variation in the
coefficients is determined by a simple stochastic process such as a random walk. While such …