This article provides the in-sample estimation and evaluates the out of-sample conditional mean and volatility forecast performance of the conventional Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) and...

We analyze the predictive content of the mortgage spread for U.S. economic activity. We find that the spread contains predictive power for real GDP and industrial production. Furthermore, it outperforms the term spread and Gilchrist– Zakrajsek spread in a real-time forecasting exercise....

We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to...

The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and...

In the European Union, small and medium sized enterprises (SMEs) represent 99% of all businesses and contribute to more than half of the total value-added. In this paper, we develop distress prediction models for SMEs using a dataset from eight European countries over the period 2000-2009. We...

This paper shows through a Monte Carlo analysis the effect of neglecting seasonal deterministics on the seasonal KPSS test. We found that the test is most of the time heavily oversized and not convergent in this case. In addition, Bartlett-type non-parametric correction of error variances did...

Using new time-series data for the size of the Canadian underground economy, the relationship between unreported and measured GDP in that country is examined. Granger causality tests are conducted, with a proper allowance for the non-stationarity of the data. It is found that there is clear...

The purpose of this paper is to examine the forecasting ability of sixty-two vintages of revised real-time PCE and core PCE using nonparametric methodologies. The combined fields of real-time data and nonparametric forecasting have not been previously explored with rigor, which this paper...

This study models and forecasts the components of population growth in Arkansas through 2017. A structural econometric model is developed and used to generate ex-ante forecasts. The model includes equations for births, deaths, and net migration. These three variables, in combination with...