The choice of monetary policy is the most important concern of central banks, but this choice is always confronted with two relevant aspects of economic policy: parameter instability and model uncertainty. This paper deals with both types of uncertainty and shows that recursive thick modeling is...

This paper examines the extent to which the daily movements of three large emerging markets stock indices are predictable. Lagged technical indicators are used as explanatory variables. In the analysis we employed seven classification techniques and assessed the discriminatory power of the...

By following the spirit in Favero and Milani (2005), we use recursive thick modeling to take into account model uncertainty for the choice of optimal monetary policy. We consider an open economy model and generate multiple models for only the aggregate demand and aggregate supply. Models are...

This paper extends the existing literature on empirical research in the field of sovereign debt. To the authors' knowledge, only one study in the area of sovereign debt has used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance...

We examine the relation between monthly stock returns and lagged publicly available information. Our primary objective is to determine whether the variables proposed in the literature to predict the equity premium contain incremental information to an investor. We find that certain variables do...

We use a machine learning algorithm called Adaboost to find direction-of-change patterns for the Samp;P 500 index using daily prices from 1962 to 2004. The patterns are able to identify periods to take long and short positions in the index. This result, however, can largely be explained by...

Previous empirical studies have shown that predictive regressions in which model uncertainty is assessed and propagated generate desirable properties when predicting out-of-sample. However, it is still not clear (a) what the important conditioning variables for predicting stock returns...