El objetivo de este artículo es la obtención de sendos modelos de predicción del fracaso empresarial en una muestra emparejada y otra aleatoria de pequeñas y medianas empresas con domicilio en Castilla y León (España), a fin de determinar si el poder predictivo de los modelos elaborados...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

We propose a new information criterion for impulse response function matching estimators (IRFMEs) of the structural parameters of dynamic stochastic general equilibrium (DSGE) macroeconomic models. An advantage of our procedure is that it allows researchers to select the impulse responses that...

A two-regime self-exciting threshold autoregressive process is estimated for quarterly aggregate GDP of the fifteen countries that compose the European Union, and the forecasts from this nonlinear model are compared, by means of a Monte Carlo simulation, with those from a simple autoregressive...

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection...

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and expected shortfall forecasts that exploits the...

A practice that has become widespread is that of comparing forecasts of financial return variability obtained from discrete time models against high frequency estimates based on continuous time theory. In explanatory financial return variability modelling this raises several methodological and...

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

This paper uses an empirical connection between real stock market indices of Germany and the USA for forecasting corresponding returns. We are starting from the random walk as the traditional forecasting model in stock market applications, extending it by co-integration. Since the cointegrating...