Doctoral programme: MRes one-year programme

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.

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.

This module will offer a thorough theoretical understanding of the key themes of innovation research, combined with practical insights into the challenges of innovation management in organisations. You will address topics ranging from technological change, creativity, the role of networks in innovation, and appropriability/value capture from innovation.

This module covers some of the basic problems and algorithms for machine learning. We will cover supervised and unsupervised learning problems, and causal inference will also be one of the main themes of the course. Most of the applications will be in marketing, operations research and economics.

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.

In this module you will be introduced to a selection of most seminal papers in organisational behaviour with a particular focus on classic and contemporary theories, ongoing controversies, and ground-breaking empirical studies. The emphasis is on providing a foundational overview of the field.

This module will expose you to the major theoretical perspectives and issues studied in organisation theory research. You will also be exposed to a set of approaches to understanding how and why organisations form, survive and grow.

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.

Those who are wanting to pursue organisational behaviour should take at least one more advanced module on a topic within organisational behaviour. This might be focused on micro organisational behaviour that focuses more on intra-personal processes, such as emotions and cognitions, or meso organisational behaviour focusing more on inter-personal processes. This could also be special topics in organisational behaviour, such as gender and diversity in organisations, or moral psychology applied to organisations.

This module covers some of the basic problems and algorithms for machine learning. We will cover supervised and unsupervised learning problems, and causal inference will also be one of the main themes of the course. Most of the applications will be in marketing, operations research and economics.

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 will allow you to develop a good command of the core concepts and debates in the corporate sustainability literature. The module is organised with the initial stage dedicated to the debate on the theory of the firm. The module will then progress to a part related to the environmental and social impacts of firm activities, and engage students about alternative forms of capitalism that might be able to remedy and prevent the negative spillovers created by economic activity. The third stage will examine governance and strategic decision-making as critical elements of the firm that will require innovative solutions backed up by experimental evidence. The final stage will examine how other functional and organisational elements of the firm have been and can be redesigned to develop a stakeholder-centred form of enterprise.

You will study the application of microeconomic theory to the healthcare sector, analysing principles of microeconomics and their application. You will also consider the issues of efficiency, equity and their application to the healthcare sector.

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.

This module focuses on the execution of qualitative research with the aim being to write a qualitative paper over the course of the term, with ample constructive feedback throughout the process.

By the end of this module you will be able to comfortably navigate open data; curate, combine and clean data sets to tackle new problems; and contribute to your proprietary data to open data repositories. You will also develop skills to use Python in Jupyter notebooks to transform, analyse, and visualise data.

In this module you will learn to critically assess existing concepts that explain the structure and dynamics of networks and the position of individuals and organisations within them. You will learn to apply the toolkit of network theories, concepts and methods to design effective networks to research in your field of interest. By the end of the module you will be able to perform social network analysis to examine processes of tie formation, network dynamics and their impact on individual and organisational outcomes.