Data Science in Economics and Finance for Decision Makers
First published:
ISBN: 978-1-78272-394-3
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Quality matters: for insightful quality advice, get to know your big data
Foreword
Introduction
Digitalisation and transformation in economics and finance
Big data for policymaking in economics and finance: the potential and challenges
Quality matters: for insightful quality advice, get to know your big data
Statistics and machine learning: variations on a theme
Advanced statistical analysis of large-scale Web-based data
Text analysis
Prudential stress testing in financial networks
Data visualization: developing capabilities to make decisions and communicate
Data science in economics and finance: tools, infrastructure and challenges
Data science and machine learning for a data-driven central bank
Large-scale commercial data for economic analysis
Artificial intelligence and data are transforming the modern newsroom: a Bloomberg case study
Implementing big data solutions
A borderless market for digital data
Legal/ethical aspects and privacy: enabling free data flows
Assessing the trustworthiness of artificial intelligence
“Big tech”, journalism and the future of knowledge
This chapter outlines where data scientists often go wrong when trying to use big data sets to describe relationships between variables. We discuss these problems and explain why data quality analysis should always be done up front before any analysis takes place.
INTRODUCTION
Big data is a wonderful playground for statisticians and economists and provides ample opportunities to test existing theories and discover new causations among large data sets to trace down new insights which, if proven to be consistent over time, may well generate new theories, particularly in the social and behavioural sciences.
It is a wonderland both for those researchers who are curious at the micro level and for those with a macro-level oversight, as big data approaches offer the prospect of supplementing existing predictions and stimulating both academic and political debate. Indeed, using alternative data sets may create the opportunity to adjust our model-based theory and recognise the fragility of the assumptions imposed on our models in order to make our predictions more realistic, while describing the uncertainties surrounding our models and results. It may therefore give us the
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