This paper develops a multivariate statistical model for the analysis of credit default swap spreads. Given the large excess kurtosis of the univariate marginal distributions, it is proposed to model them by means of a mixture of distributions. However, the multivariate extension of this...

Maximum likelihood estimation of spatial models based on weight matrices typically requires a sizeable computational capacity, even in rel- atively small samples. The unilateral approximation approach to spatial models estimation has been suggested in Besag (1974) as a viable alternat- ive to...

Approximate Maximum Likelihood Estimation (AMLE) is a simple and general method recently proposed for approximating MLEs without evaluating the likelihood function. The only requirement is the ability to simulate the model to be estimated. Thus, the method is quite appealing for spatial models...

<title>Abstract</title> Stationarity in space presents two aspects: homogeneity and isotropy. They correspond respectively to stationarity under translations and stationarity under rotations. Testing the hypothesis of isotropy is a common practice in many fields of application of spatial statistics where...

Several economic phenomena are found to follow an approximate Pareto distribution, at least in the upper tail. The debate is well established for the distribution of wealth and business firms, and has recently been particularly animated with respect to city sizes. In this paper we contribute to...

This paper deals with the estimation of the lognormal-Pareto and the lognormal-Generalized Pareto mixture distributions. The log-likelihood function is discontinuous, so that Maximum Likelihood Estimation is not asymptotically optimal. For this reason, we develop an alternative method based on...