In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by...

The linear Gaussian state space model for which the common variance is treated as a stochastic time-varying variable is considered for the modelling of economic time series. The focus of this paper is on the simultaneous estimation of parameters related to the stochastic processes of the mean...

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...

We analyze the distribution of economic activity across space for different types of activity and different levels of aggregation. Not only is this distribution highly uneven (independently of the type of activity and level of aggregation), it is also remarkably regular regarding its size...

We examine the setting of national competition policy in a two-country setting, emphasizing the relationship of trade to the goals of competition policy (such as the degree and nature of competition). The issues we address involve the general equilibrium distributional effects of competition...

What is a 'sustainable nation’ and how can we identify and rank ‘sustainable nations’? Are nations producing and consuming in a sustainable way? Aggregate indicators have been proposed to answer these questions. This paper quantitatively compares three aggregate indicators of...

Geographical economics analyzes the endogenous determination of the location of economic activity in a general equilibrium framework. We investigate the impact of pollution by focusing on the interaction between location advantages and negative pollution externalities associated with local...

We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. By combining existing numerical and Monte Carlo integration methods, we obtain a general and efficient likelihood evaluation method for this class of models. Our approach is based on the idea that only...

We apply the splitting method to three well-known counting problems, namely 3-SAT, random graphs with prescribed degrees, and binary contingency tables. We present an enhanced version of the splitting method based on the capture-recapture technique, and show by experiments the superiority of...

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by...