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ES50155: Data mining, machine learning and econometrics

[Page last updated: 26 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Economics
Further information on credits Credits: 10      [equivalent to 20 CATS credits]
Further information on notional study hours Notional Study Hours: 200
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Assessment detail for this unit will be available shortly. (CW 100%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Description: Aims:
This unit aims to provide students with econometric methods and knowledge of mathematical computing and econometric software necessary to conduct empirical analysis over a range of economic, business and financial problems. It also introduces students to contemporary statistical and algorithmic methods for cleaning, processing and extracting hidden information and knowledge out of raw data. Finally, it also covers topics on the intersection of data mining, machine learning, and econometrics and introduces students to machine learning methods used for empirical economic analysis. There will be particular emphasis on the use of machine learning methods for estimating causal effects.

Learning Outcomes:
At the end of the unit students should be able to:

* Choose appropriate algorithms to detect previously unknown rules and patterns within data and infer their business implications;
* Create econometric models appropriate for the problems studied;
* Estimate and evaluate econometric models, and interpret and critically evaluate the results;
* Apply mathematical computing and econometric software;
* Create custom code either in econometric software or a suitable programming language;
* Apply key concepts, methods, and tools of machine learning;
* Evaluate the applicability of machine learning methods for empirical economic, business and policy analysis.

Skills:

Ability to think algorithmically to extract rules and detect exceptions.

Ability to apply analytical and numerical techniques

Ability to gather and synthesize information

Ability to use state-of-the-art data mining, econometric and machine learning software.

Ability to assess the value of data, information, and knowledge.

Content:
Data mining methods: exploratory data analysis, naïve Bayes model, clustering methods

Machine learning methods: decision trees, support vector machines, neural networks, LASSO

Estimation methods:
* Linear and generalized linear regression models
* General method of moments
* Instrumental variables

Panel data models

Time series models
Hypothesis testing and inference

Mathematical computing and econometrics software

Cross-validation

Policy evaluation

Counterfactual prediction.
Further information on programme availabilityProgramme availability:

ES50155 is a Designated Essential Unit on the following programmes:

Department of Economics
  • THES-AFM30 : MSc Economics for Business Intelligence and Systems

Notes: