Economists are shifting attention and resources from work on survey data to work on “big data.” This analysis is an empirical exploration of the trade-offs this transition requires. Parallel models are estimated using the Federal Reserve Bank of New York Consumer Credit Panel/Equifax and the...

Governments around the world want to develop their ICT and digital industries. Policymakers thus need a clear sense of the size and characteristics of digital businesses, but this is hard to do with conventional datasets and industry codes. This paper uses innovative "big data" resources to...

Google occupies a powerful position within the United States economy, a position which many have begun to consider too powerful. Google’s power is derived almost entirely from how it uses the billions of pieces of information it collects on its users—a collection of information known as big...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

We generate measures for search intensity of employers and job seekers and - as a novel feature - for placement intensity of employment agencies. For this purpose, we tap big data on online activity from the job exchange of the German Federal Employment Agency and its internal...

Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an increase of 4%–10% in...

Big Data has become a buzz word. It is the next big thing that every corporation should invest in. It is said to bring hundreds of billions dollars in benefits to consumers. In this concept paper I show that Big Data is a tool that will allow corporations to take away consumer surplus from...

Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised...

The paper tries to resolve the complexity encountered in processing of Big Data through various methodology which includes Reverse Geometric Data Perturbation to estimate the spectral flow of data through new learning methods for underlying neural networks. It also has insights into the...