We report methods for tackling a challenging three-dimensional (3D) deconvolution problem arising in confocal microscopy. We fit a marked point process model for the set of cells in the sample using Bayesian methods; this produces automatic or semi-automatic segmentations showing the shape,...

Increasingly, spatial econometric methods are becoming part of the standard toolkit of applied researchers in agricultural, environmental and development economics. Nonetheless, applications in discrete-choice settings remain few and despite its appeal, applications of the Bayesian paradigm in...

This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country VAR models with cross unit interdependencies, unit specific dynamics and time variations in the coefficients. The framework of analysis is Bayesian:...

This paper integrates panel VARs and the index models into a unique framework where cross unit interdependencies and time variations in the coefficients are allowed for. The setup used is Bayesian and MCMC methods are used to estimate the posterior distribution of the features of interest and to...

New product launches are often accompanied by extensive marketing communication campaigns. Firms' allocation decisions for these marketing communication expenditures have two dimensions—across consumers and over time. This allocation problem is different relative to the problem of allocation...

This paper estimates a switching autoregressive conditional heteroskedastic time series model for returns on the daily German stock market index. Volatility clustering is captured by persistent periods of different volatility levels and by the dependence on past innovations. We introduce a...

This paper presents a method to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country VAR models with cross unit interdependencies, unit specific dynamics and time variations in the coefficients. The framework of analysis is Bayesian: a...

In this paper Markov Chain Monte Carlo algorithms(MCMC) are developed to facilitate the Bayesian analysis on nested designs when the error structure can be expressed as an autoregressive process of order one. Simulated and real data are also presented to confirm the efficiency and high accuracy...