Qualification Type: | PhD |
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Location: | Glasgow |
Funding for: | UK Students |
Funding amount: | Not Specified |
Hours: | Full Time |
Placed On: | 23rd February 2024 |
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Closes: | 3rd April 2024 |
Opens: Now
Funding details: Fully-funded scholarship for 3 years covers all university tuition fees (at UK level) and an annual tax-free stipend. International students are also eligible to apply, but they will need to find other funding sources to cover the difference between the home and international tuition fees. Exceptional international candidates may be provided funding for this difference.
Number of places: 1
Number of places extra: There will be a shortlisting and interview process.
RCUK eligibility: No
Eligibility:
Eligibility criteria will be tested both through CV screening and interview.
Study modes eligibility: Full-time
Project Details: Consumer confidence indicators (CCIs) gauge expectations about future economic developments influencing household spending and saving decisions. However, these indicators are based on surveys that have several limitations: small sample sizes (2,000 monthly in the case of the UK survey conducted by market research company GfK) with questionable representativeness and high costs. Moreover, the publication lags reduce their value during crises or as economic activity approaches turning points in the cycle.
The PhD project will see the appointed student work with the supervisory team to create a more representative and cost-effective alternative to the UK-CCI by applying machine learning and NLP techniques to articles published in leading UK newspapers.
The linguistic analysis of text in Economics and Finance has become very popular in the last few years. However, there are many challenges when modelling text data (e.g. cleaning text, variable shrinkage, interpretation of results). An important contribution of this project is to focus on the interpretation of words and the associated human emotions. Rigorous econometric methods will be used. The student will specifically evaluate the text based CCI’s responsiveness to key UK economic, financial, and political events and its performance in forecasting. In the second stage of the project daily and weekly text-based real time indicators will be created and used in nowcasting macroeconomic indicators including GDP, inflation, and the unemployment rate – this is not possible with the current low frequency and slow release of CCI data.
Primary Supervisor: Dr. Luigi Gifuni.
Additional Supervisor/s: Prof Julia Darby and Prof Dimitris Korobilis.
Contact Details: luigi.gifuni@strath.ac.uk
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