We provide a survey of the literature on panel vector autoregression (pVAR) models and of their main characteristics. We also assess the possible gains pVAR models might yield for flash estimation, now-casting and economic short-term (point and density) forecasting, and discuss some yet unexploi...
Parallel advances in IT and in the social use of Internet-related applications, provide the general public with access to a vast amount of information. The associated Big Data are potentially very useful for a variety of applications, ranging from marketing to tapering fiscal evasion. From the p...
This work is concerned with the analysis of outliers detection, signal extraction and decomposition techniques related to big data. In the first part, also with the use of a numerical example, we investigate how the presence of outliers in the big unstructured data might affect the aggregated ti...
This paper assesses the forecasting performance of various variable reduction and variable selection methods. A small and a large set of wisely chosen variables are used in forecasting the industrial production growth for four Euro Area economies. The results indicate that the Automatic Leading ...
In this report we describe various methods suited for the analysis of linear models with a very large number of explanatory variables, with a special emphasis on Bayesian approaches. We next consider some non-parametric and/or non-linear methods suited for applications with big data, such as ran...
Big data have high potential for nowcasting and forecasting economic variables. However, they are often unstructured so that there is a need to transform them into a limited number of time series which efficiently summarise the relevant information for nowcasting or short term forecasting the ec...
