One year Master’s in Research 

The Business School Master’s in Research (MRes), provides an introduction to theory and research methods in Finance, Economics and Management, providing you with a solid foundation for your doctoral studies.

Depending on the research area you choose to specialise in, you will embark on a one or two year MRes programme.

If you choose to specialise in the following research areas, you will embark on a one-year MRes:

  • Analytics & Operations
  • Marketing

If you choose to specialise in the following research areas, you will embark on a two-year MRes:

  • Economics & Public Policy
  • Finance
  • Innovation and Entrepreneurship
  • Strategy and Organisational Behaviour

Find out more about the two-year MRes programme structure. 

Administrative label
Phd - Core modules

Compulsory introductory modules

Core introductory modules, designed to provide a foundation in research tools:

This course provides students with a basic foundation in mathematics and statistics required to undertake further quantitative research methods courses. This course consists of two parts, statistics and mathematics. Topics covered include: matrix algebra; optimization; differential equations; random variables and probability distributions; moments of a random variable; probability distributions; joint, marginal and conditional distributions; functions and transformation of a random variable; hypothesis testing; univariate regression.

When starting new research, the first step is usually a literature review: scanning what is already known about a given topic and figuring out where the gaps are. However, novice researchers tend to be anything but systematic in their literature review: they have no method for scanning the literature, and they usually have little idea of what is relevant and what is not. The Systematic Review method opens a way to create research syntheses that add real value and novel insight – in a way that is potentially publishable in its own right.

Research methods modules

You will study a selection of research methods modules from:

This module will provide an introduction to the practice of applied microeconometrics. Students will learn the standard empirical methods in current use by applied researchers and be exposed to a handful of frontier approaches. The focus will be on implementation beyond simply estimating a parameter of interest: getting the standard errors right, validation and conducting appropriate robustness exercises, and adapting methods to fit new contexts.

This module will be an introduction to some of the most important themes for students wishing to conduct their own research in Empirical Corporate Finance. For other students, this module will help students gain a better understanding of research related to your own field. Topics covered include: regression refresher, causality and randomized experiments, instrumental variables, difference-in-difference, regression discontinuity, standard errors, event studies, discrete response models, matching methods, and non-parametric methods

The module has the objective to provide the students with econometric tools necessary to conduct their empirical research and discuss fundamentals of econometric theory behind them. Students will learn how to conduct - and how to critique - empirical studies in finance, economics and related fields.

The module is intended for students with a prior knowledge of asset pricing theory, capital markets and econometrics, and will concentrate on discrete-time methods and use a use a variety of econometric techniques. The module will cover these econometric tools in order to empirically address meaningful economic questions.

The main objective in this module is to give students a thorough grounding in optimization models, theory, algorithms and software. The module level is introductory but it is rigorous with emphasis on proof techniques of the basic results of optimization theory, so only the most important and representative models and algorithms will be covered. Students will be expected to program as well as use the most popular industrial software for optimization.

This module covers research methods required in qualitative research. You will develop skills in all aspects of the research process, including research design, data collection, data analysis, theory building, writing up as well as reviewing papers and responding to referees. The module is essential for those who wish to author qualitative research but will also be useful for quantitative researchers.

This module provides an overview of the primary quantitative methods employed in management research. It will enable you to develop the ability to interpret the results of your own research as well as to critically assess the findings presented in other studies. The emphasis will be on the practical application of different estimation models using STATA rather than on the econometrics and mathematical specification.

The main objective in this module is to give students an overview of different areas or research in Operations Management and Analytics with the aim of preparing them for their research. Students will read papers and present as well as start posing research questions. The class will be led by multiple instructors who each will cover their research area.

Administrative label
Phd - Specialist modules

Specialist modules

You will also select modules focusing on theory and application within your chosen area of research from:

The primary aim is to help develop students into successful consumer behaviour researchers. To achieve this, a thorough understanding of the relevant literature is essential and a number of key articles will be discussed in each session. In order to help prepare students for an academic career, the module includes identifying gaps in the literature, seeing both contributions and shortcomings in published work, coming up with rigid research ideas, and selling research ideas convincingly.

This module explores competitive dynamics between firms that shape individual firms and entire industries. Using game theory as our main analytical tool, we will explore some of the most important strategic decisions that firms make such as market entry, channel design, pricing, product design and positioning. The module is designed for graduate-level students who have basic knowledge of calculus and microeconomics. No prior knowledge of game theory is required - the module will start by covering key concepts in game theory and optimization theory that will be used in the rest of the module.

This module covers applications of machine learning in economics.  The main focus areas will be supervised learning and causal inference, and unsupervised learning and information retrieval from unstructured data. 

The module covers the main tools of microeconomic theory and focuses on preferences, consumer theory, choice under uncertainty, producer theory, and game theory. Time permitting, it introduces general equilibrium in competitive markets. The emphasis is on economic intuition as well as techniques. The fundamental concepts of microeconomic theory are discussed.

This module covers competitive equilibrium, markets with imperfect, competition and asymmetric information, general equilibrium, Social choice and mechanism design.

The topics covered in each module are based on the current academic year and may be subject to change. The information is provided as a guide only.

Other electives modules

In addition to the modules offered within the Business School as part of the MRes programme, we have added elective modules from other faculties within Imperial College London, so that Operations Management students can benefit from specialised training across the University that is relevant to their research. Students will take the Research Methods modules within the Business School and can then take elective modules in relevant modules which may include:

  • Probability and Stochastic Processes

  • Traffic Theory and Queuing Systems

  • Optimisation

  • Game Theory

  • Information Theory

  • Advanced Machine Learning

  • Advanced Machine Learning (online)

  • Retail and Marketing Analysis

  • Digital Marketing Analytics

  • Logistics and Supply Chain Analytics

  • Workforce Analytics

  • Contemporary Topics in Health Policy

  • Healthcare and Medical Analytics

  • Optimisation and Decision Models

  • Business Models and Intellectual Property 

*These modules are available subject to capacity and timetabling constraints in other faculties and are differently weighted to the MRes Business electives

Research project and assessment

You will develop your own research project, which is assessed via submission of a written thesis and an oral examination.

Administrative label
Doctoral CTAs