There is increasing demand for models of time-varying and non-Gaussian dependencies for mul- tivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical...

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a...

For many low-income countries, the impact of structural reforms on economic growth and poverty alleviation crucially depends on the response of aggregate agricultural supply to changing incentives. Despite its policy relevance, the size of this parameter is still largely unknown. This paper...

The book provides deep insights into the signal extraction problem - especially at the boundary of a sample, where asymmetric filters must be used - and how to solve it optimally. The traditional model-based approach (TRAMO/SEATS or X-12-ARIMA) is an inefficient estimation method because it...

Estimation of signals at the current boundary of time series is an important task in many practical applications. In order to apply the symmetric filter at current time, model-based approaches typically rely on forecasts generated from a time series model in order to extend (stretch) the time...