This article generalises the results of Sadi and Zakoian (2006) to a considerably larger class of nonlinear ARCH models with discontinuities, leverage e ects and robust news impact curves. We propose a new method of proof for the existence of a strictly stationary and phi-mixing solution....

We revisit Wintenberger (2013) on the continuous invertibility of the EGARCH(1,1) model. We note that the definition of continuous invertibility adopted in Wintenberger (2013) may not always be sufficient to deliver strong consistency of the QMLE. We also take the opportunity to provide other...

We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex...

We analyze the role of industrial and non-industrial production sectors in the US economy by adopting a novel multilevel factor model. The proposed model is suitable for high-dimensional panels of economic time series and allows for interdependence structures across multiple sectors. The...

This paper proposes a novel time-series model with a non-stationary stochastic trend, locally explosive mixed causal non-causal dynamics and fat-tailed innovations. The model allows for a description of financial time-series that is consistent with financial theory, for a decomposition of the...

This paper introduces a new model for spatial time series in which cross-sectional dependence varies nonlinearly over space by means of smooth transitions. We refer to our model as the Smooth Transition Spatial Autoregressive (ST-SAR). We establish consistency and asymptotic Gaussianity for the...

We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the...

We investigate the information theoretic optimality properties of the score function of the predictive likelihood as a device to update parameters in observation driven time-varying parameter models. The results provide a new theoretical justification for the class of generalized autoregressive...