This paper builds on Kocenda (2001) and extends it in two ways. First, two new intervals of the proximity parameter ε (over which the correlation integral is calculated) are specified. For these ε-ranges new critical values for various lengths of the data sets are introduced and through Monte...

We model the joint process for real per capita U.S. federal government spending and tax revenues as a Threshold Vector Error Correction. We find evidence that fiscal authorities will intervene to reduce real per capita deficit only when it reaches a certain threshold. We also find that fiscal...

In this paper, I propose an extension to the class of semiparametric time series models by incorporating a weight function to allow for smooth transitions between a linear autoregressive and a nonparametric component. The weight function and nonparametric component may depend on auxiliary...

In this paper we attempt to establish unified sufficient conditions for geometric ergodicity of autoregressive models. It is shown that there is a close relationship between geometric ergodicity and mixing properties. The case of nonstationary time series is incorporated into the investigations....

In this paper, we present a new approach to trend/cycle decomposition under the assumption that the trend is the permanent component and the cycle is the transitory component of an integrated time series. The permanent component is defined as the steady-state level of the series, a definition...

Significant nonlinearities are found in several cyclical components macroeconomic time series across countries. Standard equilibrium models of business cycles successfully explain most first and second moments of these time series. Neverthelesss, this paper shows that a model of this class...

The problem of identifying the time location and estimating the amplitude of outliers in nonlinear time series is addressed. A model-based method is proposed for detecting the presence of additive or innovational outliers when the series is generated by a general nonlinear model. We use this...

We consider the standard spectral estimators based on a sample from a class of strictly stationary nonlinear processes which include, in particular, the bilinear and Volterra processes. It is shown that these estimators, under certain mild regularity conditions are both consistent and...

High breakdown-point regression estimators protect against large errors both in explanatory and dependent variables. The least trimmed squares (LTS) estimator is one of frequently used, easily understandable, and thoroughly studied (from the robustness point of view) high breakdown-point...

We extend the class of GARCH models to comprise asymmetric and nonlinear effects on volatility. In particular, we do not only explain future volatility of a time series on its own past, but allow for external influences and spillovers between capital markets. For this generalized class of...