Nonlinear time series models, especially those with regime-switching and GARCH errors, have become increasingly popular in the economics and finance literature. However, much of the research has concentrated on the empirical applications of various models, with little theoretical or statistical...

Summary We apply the Threshold Autoregressive Conditional Duration Model (TACD) as proposed by Zhang, Russell, and Tsay (1999) to model the after market trading duration process associated with the initial public offering of the Deutsche Telekom AG share in November of 1996. Special emphasis is...

Threshold Autoregressive Models (TAR) along with other nonlinear time series models have attracted much attention in recent years in time series analysis. TAR models have been applied to a variety of time series. It has been reported that they have a good in sample fit but like many other...

This paper discusses the existence of spurious long memory in common nonlinear time series models, namely Markov switching and threshold models. We describe the asymptotic behavior of the process in terms of autocovariance and autocorrelation function and support the theoretical evidences by...

The paper develops an approach for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its one-step ahead conditional density. The approach entails examination of conditional moment profiles corresponding to certain shocks; a conditional moment...

The focus of this paper is an information theoretic-symbolic logic approach to extract information from complex economic systems and unlock its dynamic content. Permutation Entropy (PE) is used to capture the permutation patterns-ordinal relations among the individual values of a given time...

We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure...

In this paper a flexible GARCH-type model is developed with the aim of describing sign and size asymmetries in financial volatility as well as intermittent dynamics and excess of kurtosis. A sufficient condition for strict stationarity and ergodicity of the model is established and the existence...

Applying nonparametric variable selection criteria in nonlinear regression models generally requires a substantial computational effort if the data set is large. In this paper we present a selection technique that is computationally much less demanding and performs well in comparison with...

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling...