We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allows us to shed some light on the practical...

Factor models can cope with many variables without running into scarce degrees of freedom problems often faced in a regression-based analysis. In this article we review recent work on dynamic factor models that have become popular in macroeconomic policy analysis and forecasting. By means of an...

High dimensional general unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables. Automatic model selection procedures can handle more candidate variables than observations, allowing substantial dimension reduction from GUMs...

We study the workings of the factor analysis of high-dimensional data using arti…cial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allow us to shed some light on the practical...

This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data...

In this paper we examine the out-of-sample forecast performance of high-yield credit spreads regarding employment and industrial production in the US, using both a point forecast and a probability forecast exercise. Our main findings suggest the use of few factors obtained by pooling information...

The Google Insights data are a collection of recorded Internet searches for a huge number of the keywords, which are available since January 2004. These searches represent a kind of revealed perceptions of Internet users, which are a (possibly not entirely representative) sample of the general...

This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data...

In this paper, the application of two different unobserved factor models to a data set from Estonia is presented. The small-scale state-space model used by Stock and Watson (1991) and the large-scale static principal components model used by Stock and Watson (2002) are employed to derive common...