In this study we suggest a Bayesian approach to fuzzy clustering analysis – the Bayesian fuzzy regression. Bayesian Posterior Odds analysis is employed to select the correct number of clusters for the fuzzy regression analysis. In this study, we use a natural conjugate prior for the...

Crude oil markets are highly volatile and risky. Extreme value theory (EVT), an approach to modelling and measuring risks under rare events, has seen a more prominent role in risk management in recent years. This paper presents an application of EVT to the daily returns of crude oil prices in...

The multiproduct symmetric generalized McFadden cost function is increasingly prominent in empirical production analysis. Researchers should be aware that the scope for imposing and testing nonjointness in this model is limited. In the general version of the model, nonjointness requires the...

Previous empirical work that assessed the theoretical results of Lucas (1972) is updated by incorporating recent data, utilizing advancements in time series methods and bootstrapping results. Specifically, the two-stage method of Ball et al. (1988) and others is used, but the functional form...

We use the stochastic volatility model as a basis for investigating the statistical properties of absolute returns as a measure of latent volatility in financial markets. Our results are compared with existing results for squared returns.

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data...

This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of...

The aim of this work is to introduce a new nonparametric regression technique in the context of functional covariate and scalar response. We propose a local linear regression estimator and study its asymptotic behaviour. Its finite-sample performance is compared with a Nadayara-Watson type...

Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points. In this paper we show that if the sample covariance matrix is...

This paper discusses how to determine the order of a state-space model. To do so, we start by revising existing approaches and find in them three basic shortcomings: i) some of them have a poor performance in short samples, ii) most of them are not robust and iii) none of them can accommodate...