We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed-effect panel models. In its simplest form, the half-panel jackknife, the estimatorisjust 2θˆ−θ1/2,where θˆ!istheMLEfromthefullpaneland θ1/2 istheaverageofthe two...

Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of...

We investigate the existence of chart patterns in the Euro/Dollar intra-daily foreign exchange market. We use two identification methods of the different chart patterns: one built on close prices only, and one based on low and high prices. We look for twelve types of chart patterns and we study...

The EGARCH is a popular model for discrete time volatility since it allows for asymmetric effects and naturally ensures positivity even when including exogenous variables. Estimation and inference is usually done via maximum likelihood. Although some progress has been made recently, a complete...

In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way error components regression model suitable for incomplete panel and including parametrically specified variance functions for both individual-specific and general error disturbances.

This paper examines the empirical interplay between economic growth and greenhouse gas emissions using panel data. Relying on nonparametric methods, we find evidence supporting specifications which assume the constancy of the relationship between CO2 emissions and GDP per capita during the...

This paper examines the empirical interplay between economic growth and greenhouse gas emissions using panel data. Relying on nonparametric methods, we find evidence supporting specifications which assume the constancy of the relationship between CO2 emissions and GDP per capita during the...

This paper presents a method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method to...

We propose a practical and flexible solution to introduce skewness in multivariate symmetrical distributions. Applying this procedure to the multivariate Student density leads to a "multivariate skew-Student" density, for which each marginal has a different asymmetry coefficient. Similarly, when...

We present an indirect estimation approach for elliptical stable distributions which relies on the use of a multivariate t distribution as auxiliary model. This distribution is also elliptical and we show that its parameters have a one-to-one relationship with those of the elliptical stable,...