BFS 2002

Poster Presentation

Calibration of heavy-tailed economic time series

Zoltan Reppa, Laszlo Gerencser, Gyorgy Michaletzky

Economic time series with heavy-tailed marginal distributions will be described by ARMA- models driven by i.i.d. innovation process with normal-inverse Gaussian distributions. Following earlier works by Gerencser we develop a new method for analyzing the full information maximum likelihood estimates. This result is then applied to analyze partially adaptive estimates, suggested by Phillips in a different context. A very accurate description of the estimation error process is given in both cases, which can be applied to analyze the performance of adaptive predictors.