While the traditional R 2 value is useful to evaluate the quality of a t, it does not work when it comes to evaluating the predictive power of estimated nancial models in nite samples. In this paper we introduce a validated R 2 V value that is Taylor made for prediction. Based on data from the...

In this paper we consider the estimation of a Cobb-Douglas production function using a panel dataset of Spanish manufacturing firms. As it is stressed in the econometric literature, the use of standard GMM first differences estimators to eliminate the unobserved firm-specific effects may yield...

Time series generated by Stochastic Volatility (SV) processes are uncorrelated although not independent. This has consequences on the properties of the sample autocorrelations. In this paper, we analyse the asymptotic and finite sample properties of the correlogram of series generated by SV...

P-splines were introduced by Eilers and Marx (1996). We consider semiparametric models where the smooth part of the model can be described by P-splines. A mixed model representation is also considered. We set a simple strategy for the choice of P-spline parameters, ndx, bdeg and pord, and...

A dimension reduction method in kernel discriminant analysis is presented, based on the concept of dimension reduction subspace. Examples of application are discussed.

Logit models with random effects are now widely used in applied Statistics and Econometrics. They usually lead to intractable likelihood functions, as they involve integrals without closed form solution. Numerical integration can be used to compute the likelihood and software is available...

It is well-known that classical p-values sometimes behave incoherently for testing hypotheses in the sense that, when '0 0 T .T , the support given to 0 T is greater than or equal to the support given to '0 T . This problem is also found for posterior predictive p-values (a Bayesian-motivated...

In this paper, we provide a method for constructing confidence intervals for the variance that exhibit guaranteed coverage probability for any sample size, uniformly over a wide class of probability distributions. In contrast, standard methods achieve guaranteed coverage only in the limit for a...

In this paper, a new procedure for testing the number of linear components in a general regression problem is introduced. It is based on a nonparametric estimate of the covariance matrix of the inverse regression curve. A review of previous dimension tests is also presented.

This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical properties usually observed in high frequency financial time series: high kurtosis, small first order autocorrelation of squared observations and slow decay towards zero of the autocorrelation...