In this paper we have put forward a Bayesian framework for the analysis and testing of possible non-stationarities in extreme events. We use the extreme value theory to model temperature and precipitation data in the Dar es Salaam region, Tanzania. Temporal trends are modeled writing the...

We develop a method for finding optimal greenhouse gas reduction rates under ongoing uncertainty and re-evaluation of climate parameters over future decades. Uncertainty about climate change includes both overall climate sensitivity and the risk of extreme tipping point events. We incorporate...

The influence of maternal health problems on child’s worrying status is important in practice in terms of the intervention of maternal health problems early for the influence on child’s worrying status. Conventional methods apply symmetric prior distributions such as a normal...

This paper exploits the information collected from an ad hoc survey conducted on a sample of Macedonian firms to study the extent of nominal price and wage rigidities in the Republic of Macedonia. The research was motivated by the observation that sticky prices influence the responsiveness of...

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions...

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical...

We seek to quantify the impact on euro area GDP of the European Economic Recovery Plan (EERP) enacted in response to the financial crisis of 2008–2009. To do so, we estimate an extended version of the ECB's New Area-Wide Model with a richly specified fiscal sector. The estimation results point...

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 particle filter (with a finite number of particles) is used instead of the likelihood. A critical issue for...

GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved...

The objective of studying software reliability is to assist software engineers in understanding more of the probabilistic nature of software failures during the debugging stages and to construct reliability models. In this paper, we consider modeling of a multiplicative failure rate whose...