Silvia Lui is a senior data scientist at the Data Science Campus.
She obtained a PhD in Economics from Queen Mary, University of London. Her PhD focused on using time series models to produce forecasts for large datasets.
Prior to joining the Campus, she has worked at the National Institute of Economic and Social Research (NIESR), and the University of Groningen.
Silvia has over 10 years of post-PhD experience in applying advanced quantitative methods to research on a range of economic questions and using programming languages for in-depth data analysis.
She has worked on a variety of research projects throughout her professional career, and have developed interests and expertise in micro and macro data analysis, administrative data analysis, time series analysis, forecasting and nowcasting, data linkage, longitudinal analysis, econometric modelling and the application of data science methods in economic analysis.
Her research has been published as academic papers, book chapters and official reports.
Apart from her main role at the Data Science Campus, she is also a research associate of the Economic Statistics Centre of Excellence (ESCoE), working on ONS-ESCoE joint research project.
The LBD is at its core a re-usable longitudinal data spine with each of its component providing the longitudinal link between business references. Data spine is a new concept.
Read more on The Longitudinal Business Database: Capturing the UK economy with new business microdata
This report is part of a programme of work that the ONS has been doing with the Alan Turing Institute to explore the usefulness of various economic nowcasting methods, particularly the signature method.
Read more on Technical report: nowcasting UK household income using the new “signature” method
Nowcasting refers to generating estimates of the current (“now”) state of the economy. We investigate how signature methods can be useful in the context of economic nowcasting
Read more on Helping decision makers understand the economy quickly through new methods We go behind our analysis on the use of microdata for the examination of preference tariff utilisation and take a deep dive into challenges of drawing together new administrative data sources to answer relevant policy questions.
Read more on The use of microdata for firm-level analysis of preference tariff utilisation in the UK: technical report
Preference utilisation rates (PURs) measure the extent to which UK businesses make use of the zero or reduced tariffs available via free trade agreements (FTAs). In this work, we study the take-up of preferential tariffs by UK businesses between 2009 and 2019 and examine their trends and patterns.
Read more on Employing data science to analyse the use of preferential tariffs in free trade agreements