University of Glasgow – Adam Smith Business School
Key contact name: Prof Dimitris Korobilis
Key contact email: Dimitris.Korobilis@glasgow.ac.uk
Project Details
An increasing share of economic decisions is recorded as digital text, audio and video. At the same time, advances in computation and statistical inference have allowed for the exploitation of these unstructured data sources in scientific research. Such data can be used to extract useful information that is not available in traditional aggregate indicators of economic activity (e.g. stock prices inflation, or output). For example, text data from social media can provide incremental information that goes beyond traditional quantitative data (e.g. provide new measures political risk, political influence, economic sentiment), and they are timely, in that they are available at much higher frequencies than traditional economic data (daily instead of monthly/quarterly).
However, the challenges of modelling with text data are many. This research project focuses on two important contributions, relevant for prediction. First, the numerical representation of text data is inevitably ultra-high dimensional. While forecasting with large information sets is desirable, when more information translates into more parameters, this can be very hurtful (over-parametrization problem). Therefore, one dimension of the proposed research will examine statistical estimators (so-called “shrinkage estimators”) that prevent overparametrization, combined with computational algorithms that are of low complexity and are easy to use by practitioners. Second, the proposed research will focus on the interpretation of text data for prediction purposes. The current typical approach is to insert all keywords from a text document into a model that converts this big term-document matrix into a manageable indicator. However, such approaches are so-called “black-box” and little is known if the extracted indicator will be relevant for the economic variable that we want to predict. Therefore, our intention is to examine statistical procedures where indicators based on textual data are extracted in a way that there is always direct reference to the variable to be predicted.
About the University of Glasgow
Founded in 1451, the University of Glasgow is the fourth oldest university in the English-speaking world and has been named Scottish University of the Year 2018. Glasgow is a place that inspires ambitious people to succeed. A place where inquiring minds can develop their ideas. A place where people make discoveries that change the world. The University of Glasgow includes among its alumni, the father of economics, Adam Smith. The Adam Smith Business School is named in his honour. We aim to follow his legacy by developing enlightened, engaged and enterprising graduates, who are internationally recognised and make a positive impact on culture and society. Our business is about creating inspiring leaders, researchers and professionals whose research and relations with industry have real impact, influencing organisations as they develop and grow globally. Our range of accredited degree programmes in Accounting & Finance, Economics and Management will help to prepare you for a promising future career and the ability to contribute to organisations at the highest level. We have the triple crown of accreditation and are accredited by the Association to Advance Collegiate Schools of Business (AACSB International), the European Quality Improvement System (EQUIS) and the Association of MBAs (AMBA) for our Glasgow MBA programme. We are also home to research, of international and national excellence, that contributes to theoretical advancement and is relevant to practice.
Eligibility
Applicants must meet the following eligibility criteria
• A good first degree (at least 2:1), preferably in economics, statistics, or computing science.
• Demonstrate an interest in, and knowledge of, natural language processing, high-dimensional estimation, and computational methods.
• Have a good grounding in economics, econometrics and finance.
Students must meet ESRC eligibility criteria. ESRC eligibility information can be found here*: esrc.ukri.org/skills-and-careers/doctoral-training/prospective-students
Award details
The scholarship is available as a +3 (PhD only) or a 1+3 (MSc and PhD) programme depending on prior research training. This will be assessed as part of the recruitment process. The programme will commence in September 2020.
The award includes:
• An annual maintenance grant at the RCUK rate (in 2019/20 this is £15,009)
• Fees at the standard Home rate
Other Information
This PhD project will be jointly supervised by Dimitris Korobilis (Professor of Econometrics) and Cathy Y. Chen (Professor of Finance), and the student will be associated with the Department of Economics of the Adam Smith Business School, University of Glasgow.
Application and Selection
How to apply
1. Register on GradHub, gradhub.sgsss.ac.uk, and fill out EO data (this is a requirement of the application process)
2. Complete and upload the prescribed list of required documentation to include:
• Application form
• Academic transcripts
• Two References
• CV (maximum of two pages)
• A cover letter (maximum of one page) explaining your interest in the project and your suitability for it. This letter should be uploaded in a standalone document with a naming convention as follows *name/supervisor/institution/competition/date*
Application deadline is Friday April 10, 2020.
Selection process
Successful applicants will be notified by Monday 13 April 2020. Interviews will take place on Monday 27 April 2020.