A problem often faced by economic researchers is the interpolation or distribution of economic time series observed at low frequency into compatible higher frequency data. A method based on wavelet analysis is presented to temporal disaggregate time series. A standard `plausible' method is...

In this paper I describe and apply the methods of Symbolic Time Series Analysis (STSA) to an experimental framework. The idea behind Symbolic Time Series Analysis is simple: the values of a given time series data are transformed into a finite set of symbols obtaining a finite string. Then, we...

In this paper we describe and apply the methods of Symbolic Time Series Analysis to an experimental framework. We discuss data symbolization as a tool for identifying temporal patterns in experimental data and use symbol sequence statistics in a model strategy. In particular, we introduce a...

On the tracking and replication of hedge fund optimal investment portfolio strategies in global The hedge fund represents a unique investment opportunity for the institutional and private investors in the diffusion-type financial systems. The main objective of this condensed article is to...

Using recent advances in time-varying spectral methods, this research analyses the growth cycles of the core of the euro area in terms of frequency content and phasing of cycles. The methodology uses the continuous wavelet transform (CWT) and also Hilbert wavelet pairs in the setting of a...

The Lugannani-Rice formula is a saddlepoint approximation method for estimating the tail probability distribution function, which was originally studied for the sum of independent identically distributed random variables. Because of its tractability, the formula is now widely used in practical...

Convergence and synchronisation of business and growth cycles are important issues in the efficient formulation of euro area economic policies, and in particular European Central Bank (ECB) monetary policy. Although several studies in the economics literature address the issue of synchronicity...

In impulse response analysis estimation uncertainty is typically displayed by constructing bands around estimated impulse response functions. These bands may be based on frequentist or Bayesian methods. If they are based on the joint distribution in the Bayesian framework or the joint asymptotic...

The inherent assumption with most Monte Carlo techniques is that one may ignore autocorrelations, but doing so compromises the quality of the prediction from the data. Simulations that do not take account of autocorrelation will not properly model reality, as there is significant autocorrelation...