The paper presents a study of dependencies between the autocorrelation function and selected nonlinear transformations of time series. We examine parametric transformations and introduce an analysis of nonlinear canonical correlations. We also propose various methods of testing the...

This paper examines causality between the series of returns ans transaction volumes in high frequency data. The dynamics in both series is restricted to transitions between a finite umber of stated. Depending on the state selection criteria, this approach approximated the dynamics of varying...

We consider truncated processes, both in discrete and continuous time, and study their dynamic properties. When the underlying process is a diffusion process, we derive the infinitesimal generator of its truncated counterpart. This result is the basis for the estimation of the drift and...

In this note, we propose a general testing procedure for parametric models based on Bartlett Identities. A well-known example is the Information Matrix Test, which is based on the Bartlett Identity of order 1. The Identities are shown to induce a sequence of testable restrictions on the data...

We consider a kernel based approach to nonlinear canonical correlation analysis and its implementation for time series. We deduce various diagnostics for reversible processes and gaussian processes. The method is first applied to a stimulated series satisfying a diffusion equation allowing us to...

This paper is interested in small sample properties of the indirect inference procedure which has been previously studied only from an asymptotic point of view. First, we highlight the fact that the Andrews(1993) median-bias correction procedure for autoregresssive parameter of an AR(1) process...

We consider a kernel based approach to nonlinear canonical correlation analysis and its implementation for time series. We deduce a test procedure of the reversibility hypothesis. The method is applied to the analysis of stochastic differential equation from high frequency data on stock returns.

We introduce non-nested hypothesis tests using indirect simulation-based estimation procedures.