This paper investigates the possibility to analyse the structure of unconditional or conditional (and possibly nonlinear) dependence in financial returns without requiring the specification of mean-variance models or a theoretical probability distribution. <abstract/>The main goal of the paper is to show...

This paper estimates a DSGE model with learning to re-examine the evidence on time variation in post-war U.S. monetary policy. Several papers document a regime switch, by showing that policy changed from `passive' and destabilizing in the pre-1979 period to `active' and stabilizing in the...

This paper revisits the question if the user cost of capital plays an important role for investment decisions using Bayesian estimation techniques. These methods offer advantages over classical econometric tools in this area: The most important are that prior distributions offer a convincing way...

This study examines the forecastability of ASEAN-5 stock market returns using linear and non-linear time series models. Time series models with GARCH errors are also considered. Based on formal econometrics tests, this study shows that the behaviour of these returns do not follow random walk...

This paper gives an overview of some issues related to market aluation, focusing on the developments on the New York equity markets. The 42.4 p.c. fall in the S&P 500 price index between 24 March 2000 - when it reached its all-time high - and 31 December 2002 is situated in a very long term...

The aim of this paper is to develop and apply Neural Network (NN) models in order to forecast regional employment patterns in Germany. NNs are statistical tools based on learning algorithms with a distribution over a large amount of quantitative data. NNs are increasingly deployed in the social...

Many authors have documented that it is challenging to explain exchange rate fluctuations with macroeconomic fundamentals: a random walk forecasts future exchange rates better than existing macroeconomic models. This paper applies newly developed tests for nested model that are robust to the...