We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...

This paper applies a fractional integration framework to analyse the stochastic behaviour of two Russian stock market volatility índices (namely the originally created RTSVX and the new RVI that has replaced it), using daily data over the period 2010-2018. The empirical findings are consistent...

International and interregional trade and transport are on the rise and hence, there is a clear needfor reliable estimates of transport flows. However, the available databases and estimation methodsare not yet satisfactory for analytical and predictive purposes.In this paper we explore the use...

This paper utilizes a very simple model to study the timing and determinants of speculationagainst a fixed exchange rate regime when investors are heterogeneous because of locationaldifferences. Location matters because resident players may incur smaller costs when takinga short-position, are...

A test for serial independence is proposed which is related to the BDS test but focuses on tail event probabilities rather than probabilities near the center of the distribution. The motivation behind this approach is to obtain a test more suitable for detecting structure in the tails, such as...

The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters related to the stochasticprocesses of the mean part...

We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...