This paper introduces a new framework for the dynamic modelling of univariate and multivariate point processes. The so-called latent factor intensity (LFI) model is based on the assumption that the intensity function consists of univariate or multivariate observation driven dynamic components...

This paper derives exact expressions for the statistical curvature and related geometric quantities in the first order autoregressive models.

One way the social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. The social scientists should...

It is widely agreed that, in establishing whether variable X causes variable Y, a third variable Z may confound the relation and thus hinder causal assessment. The solution developed within the ‘traditional’ framework is to control for any third variable, susceptible of confounding the...

In this paper I develop an empirical framework to estimate the role of agglomeration externalities, especially those stemming from input-output linkages, in the location process of US manufacturing plants. Furthermore, drawing on the model of Holmesand Stevens (2004b), I propose a way to...

In this paper, we study social interactions between two populations of individuals living in a city. Agents consume land and benefit from intra- and inter-group social interactions. We show that in equilibrium segregation arises: populations get separated in distinct spatial neighborhoods. Two-...

In this paper, we apply a collection of parametric (Normal, Normal GARCH, Student GARCH, RiskMetrics and high-frequency duration models) and non-parametric (empirical quantile, extreme distributions models) Value-at-Risk (VaR) techniques to intraday data for three stocks traded on the New York...

We propose an estimation method that circumvents the path dependence problem existing in Change-Point (CP) and Markov Switching (MS) ARMA models. Our model embeds a sticky infinite hidden Markov-switching structure (sticky IHMM), which makes possible a self-determination of the number of regimes...

We present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in AR-GARCH models subject to an unknown number of structural breaks at unknown dates. Break dates are directly treated as parameters and the number of breaks is determined by the marginal likelihood...

Dynamic volatility and correlation models with fixed parameters are restrictive for time series subject to breaks. GARCH and DCC models with changing parameters are specified using the sticky infinite hidden Markov-chain framework. Estimation by Bayesian inference determines the adequate number...