Dynamic interactions among stock return, Research and Development (R&D) expenses, patent applications based on R&D investment, and the propensity to patent are studied in this work for a panel of firms from the United States. The panel includes technologically similar firms, neck-to-neck, mostly...

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

An efficient estimation procedure for conditionally linear and Gaussian state space models is developed. Efficient importance sampling together with a Rao-Blackwellization step are used to construct a highly efficient estimation method that produces continuous approximations to the likelihood...

We examine the dependence between the volatility of the prices of the carbon dioxide "CO2" emissions with the volatility of one of their fundamental components, the energy prices. The dependence between the returns will be approached by a particular class of copula, the Stochastic Autoregressive...

This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also consider a dynamic ordered probit model as...

Several empirical studies are concerned with measuring the effect of currency and current account crises on economic growth. Using different empirical models this paper serves two aspects. It provides an explicit assessment of country specific factors influencing the costs of crises in terms of...

We develop a panel intensity model, with a time varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. The model is designed to analyze individual trading behavior on the basis of trading activity datasets, which are...

A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In this paper, we propose a new long memory asymmetric volatility model which captures more flexible asymmetric patterns as compared with several existing models. We extend the new...

A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In this paper, we propose a new long memory asymmetric volatility model which captures more flexible asymmetric patterns as compared with existing models. We extend the new...