PhD Studentship – Economic Predictions with Text Data at the University of Glasgow – Adam Smith Business School

PhD Studentship – Economic Predictions with Text  Data

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, i.e. 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.
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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 canbe found here*: esrc.ukri.org/skills-and-careers/doctoral-training/prospectivestudents

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
  
• Students can also draw on a pooled Research Training Support Grant, usually up to a maximum of £750 per year
  

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 SelectionHow to apply

1. Register on GradHub: https://gradhub.sgsss.ac.uk/Form.aspx?id=7067 and fill out the application form
   
   
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.