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...

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...

This article is concerned with frequency-domain analysis of dynamic linear models under the hypothesis of rational expectations. We develop a unified framework for conveniently solving and estimating these models. Unlike existing strategies, our starting point is to obtain the model solution...

A general statistical modeling problem is that given a class of competing models and new data, how one can improve the overall model performance. In general, there exist two solutions for this problem, namely model selection and model combination. Model selection is to select a single best model...

Penalized quantile regressions are proposed for the combination of Value-at-Risk forecasts. The primary reason for regularization of the quantile regression estimator with the elastic net, lasso and ridge penalties is multicollinearity among the standalone forecasts, which results in poor...

Based on the 2012 Version of the Romanian Macromodel, the first section of this paper discusses the evolution of the Romanian economy in 2014. The previous simulations were revised taking into account the changes in the internal and external socio-economic conjuncture or in the available...

The central banks introduce and implement the monetary and financial stabilities policies, going from the accurate estimations of national macro-financial indicators such as the Gross Domestic Product (GDP). Analyzing the dependence of the GDP on the time, the central banks accurately estimate...

The objective of this study is to examine the performance of default prediction model: the Z-score model using discriminant analysis, and to propose a new prediction model on a data set of 30 defaulted and 30 solvent companies. Financial ratios obtained from corporate balance sheets are used as...

Over the last few years, Bitcoin and other cryptocurrencies have attracted the interest of many investors, practitioners and researchers. However, little attention has been paid to the predictability of their risk measures. In this paper, we compare the predictability of the one-step-ahead...

We use multivariate random forests to compute out-of-sample forecasts of a vector of returns of four precious metal prices (gold, silver, platinum, and palladium). We compare the multivariate forecasts with univariate out-of-sample forecasts implied by random forests independently fitted to...