This paper aims at providing a primer on the use of big data in macroeconomic nowcasting and early estimation. We discuss: (i) a typology of big data characteristics relevant for macroeconomic nowcasting and early estimates, (ii) methods for features extraction from unstructured big data to...

This paper analyses the forecasting ability of economic summary indicators in EU economies. We employ the use of Partial Least Squares and Bayesian Shrinkage Regression methods and we predict the growth rates of quarterly GDP and Consumption and monthly Industrial Production. We find evidence...

This paper is concerned with the forecasting performance of variable reduction and variable selection methods using medium and large datasets. The variable reduction methods include Principal Components, Partial Least Squares and Bayesian Shrinkage Regression. The variable selection methods...

We consider forecasting key macroeconomic variables using many predictors extracted from the Eurostat PEEIs dataset. To avoid the curse of dimensionality, we rely on model selection and model reduction. For model selection we use heuristic optimisation of information criteria, including...

This paper investigates the performance of Financial Condition Indexes (FCIs) in forecasting four key macroeconomic variables of EU economies. A wide range of carefully selected financial indicators include Rates and Spreads, Stock Market Indicators and Macroeconomic Quantities. The results...

This paper investigates the estimation of turning points in the business cycles of six major EU economies and the EA. The core dating algorithm as implemented by King and Plosser (1994) is used here and it sheds some more light in the recent financial crisis. We are particularly concerned with...

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 paper we investigate the applied performance of covariance shrinkage in the portfolio optimisation problem. We suggest that the optimal shrinkage coefficient should be obtained from a numerical optimisation of a function with financial interpretation. Such a function could be the...

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...

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...