This dissertation comprises a series of three essays that relate advances made to both theoretical and empirical issues in marketing. The first essay discusses the issue of endogeneity of market share and price in logit models and provides a theoretical procedure to solve this problem. The...

The vector innovations structural time series framework is proposed as a way of modelling a set of related time series. As with all multivariate approaches, the aim is to exploit potential interseries dependencies to improve the fit and forecasts. The model is based around an unobserved vector...

The vector innovations structural time series framework is proposed as a way of modelling a set of related time series. As with all multivariate approaches, the aim is to exploit potential interseries dependencies to improve the fit and forecasts. The model is based around an unobserved vector...

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of...

We propose a multivariate simultaneous unobserved components framework to determine the two-sided interactions between structural trend and cycle innovations. We relax the standard assumption in unobserved components models that trends are only driven by permanent shocks and cycles are only...

This article presents a computationally efficient approach to sample from Gaussian state space models. The method is an instance of precision-based sampling methods that operate on the inverse variance-covariance matrix of the states (also known as precision). The novelty is to handle cases...

Determining potential output and the output gap-two inherently unobservable variables-is a major challenge for macroeconomists. This paper presents the R package sectorgap, which features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying...

In this paper we model the dynamics of 100 years long monthly price series of eight non-ferrous and precious metals. Applying the state space framework we impose and identify two common factors related to non-ferrous and precious metals, respectively, which exhibit quite distinct autoregressive...

EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom-up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of...