Time Series Models with an EGB2 Conditional Distribution

Time Series Analysis: Conditional Volatility Models

Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form

Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detec...

Conditional Point Distribution Models

In this paper, we propose an efficient method for drawing shape samples using a point distribution model (PDM) that is conditioned on given points. This technique is suited for sample-based segmentation methods that rely on a PDM, e.g. [6], [2] and [3]. It enables these algorithms to effectively constrain the solution space by considering a small number of user inputs – often one or two landmar...

Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model

This paper introduces and evaluates new models for time series count data. The Autoregressive Conditional Poisson model (ACP) makes it possible to deal with issues of discreteness, overdispersion (variance greater than the mean) and serial correlation. A fully parametric approach is taken and a marginal distribution for the counts is specified, where conditional on past observations the mean is...

Conditional-sum-of-squares estimation of models for stationary time series with long memory

Abstract: Employing recent results of Robinson (2005) we consider the asymptotic properties of conditional-sum-of-squares (CSS) estimates of parametric models for stationary time series with long memory. CSS estimation has been considered as a rival to Gaussian maximum likelihood and Whittle estimation of time series models. The latter kinds of estimate have been rigorously shown to be asymptot...