This paper analyses the properties of jackknife estimators of the first-order autoregressive coefficient when the time series of interest contains a unit root. It is shown that, when the sub-samples do not overlap, the sub-sample estimators have different limiting distributions from the...

Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this. The threshold effect is introduced in the autoregressive and/or the fractional...

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used...

This paper introduces a representation of an integrated vectortime series in which the coefficient of multiple correlation computed fromthe long-run covariance matrix of the innovation sequences is a primitiveparameter of the model. Based on this representation, a notion of nearcointegration is...

Modelling the growth rate of economic time series with a Markov switching process in their mean and/or their variance allows to take account of two facts that are often encountered in such series, namely that the periods in which each mean is prevailing differ in their duration and that the...

The strong consistency and asymptotic normality of the maximum likelihood estimator in observation-driven models usually requires the study of the model both as a filter for the time-varying parameter and as a data generating process (DGP) for observed data. The probabilistic properties of the...

We study the strong consistency and asymptotic normality of the maximum likelihood estimator for a class of time series models driven by the score function of the predictive likelihood. This class of nonlinear dynamic models includes both new and existing observation driven time series models....

Two of the most important stylized facts well-known in finance relate to the non-Gaussian distribution and to the volatility clustering of stock returns. In this paper, we show that a new class of stochastic processes – called Multifractional Processes with Random Exponent (MPRE) – can...

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used...

This paper proposes methods for both the consistent estimation of so-called long run canonical correlations (LRCCs) and also testing the null hypothesis that a subset of LRCCs are zero. Two test statistics are proposed and their limiting distribution is derived under the null hypothesis. It is...