Tag Archives: Data visualisation

Bitesize: Exploring UK sectoral productivity

Ian Billett and Patrick Schneider.

As time goes to infinity, the probability that a productivity analyst will wonder ‘which sectors are driving these trends?’ goes to one. We present an interactive sectoral productivity tool to help you explore this question without any fuss.

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Filed under Bitesize, Macroeconomics, New Methodologies

Bitesize: Periodicity of GBP/USD trading activities

Jihyoung Yi.

FX assets are traded continuously across the globe.  The majority of GBP/USD trades, however, are executed during typical trading hours in London and New York (NY). Saravelos and Grover (2016) find that: (i) FX moves during these hours are most highly correlated to the overall daily move; and (ii) there is statistically significant periodicity where GBP tends to depreciate in the London morning and appreciate in the NY afternoon against the US dollar.

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Filed under Bitesize, Currency, Financial Markets, Financial Stability

Izzy Whizzy let’s get Vizzy – The magic of using visualisation to analyse and understand data.

Lyndsey Pereira-Brereton.

Like Sooty, the BBC’s yellow bear loved by generations of British children, central banks should wave the ‘magic wand’ of data visualisation over their large, granular or complex data sets, in order to gain further insight into the patterns and relationships contained within them. This blog draws on some examples to highlight how different visualisation techniques help not only the communication of data, but more importantly, how it can aid data exploration, analysis and understanding.

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Filed under New Methodologies

Chance favours the prepared mind: What linked micro data can tell us about the housing market

Perttu Korhonen

Good analysis requires new discoveries, creativity, even luck. But innovation is not just a matter of chance  — it favours those who are ready for it, which in this case means having the right data. Utilising micro-data to answer new and different questions is a good start, but the next step is to link such item-level information from various sources together. That way we can create analytical opportunities beyond the sum of the parts. In this post I show how a unique linked dataset on the UK housing market reveals that buy-to-let buyers secure a  greater discount from the asking price than other buyers.

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Filed under Banking, New Methodologies