BSc International Business, Finance and Economics with Industrial/Professional Experience

You will receive a significant tuition fee discount for the placement year. UK students with a household income of up to £35,000 are also eligible to receive a  cash bursary  worth up to £2,000.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme.

The Manchester Bursary is available to UK students registered on an undergraduate degree course at Alliance MBS who have had a full financial assessment carried out by Student Finance England. 

In addition, Alliance MBS will award a range of Social Responsibility Scholarships to UK and international/EU students. These awards are worth £2,000 per year across three years of study. You must achieve AAA at A-level (or equivalent qualification) and be able to demonstrate a significant contribution and commitment to social responsibility.

The School will also award a number of International Stellar Scholarships to international students achieving AAA at A-level (or equivalent qualification). Applicants who exceed AAA and/or have supplementary qualifications (such as EPQ) will receive additional consideration.

Additional eligibility criteria apply - please see our scholarship pages for full details.

To provide students with an understanding of the quantitative methods and tools that economists use and how they can be appropriately applied and interpreted. These methods and tools are used in practical and academic settings to test economic theories and measure magnitudes that are relevant for economic policy analysis and other decisions. These methods are a key element of the professional training an economist; they will provide a foundation for subsequent study of applied and quantitative topics and are useful in many careers in economics. The course aims to equip students with a number of core competencies including: (i) an awareness of the main empirical approach to economics, (ii) experience in the analysis and use of data and software packages as tools of quantitative and statistical analysis to answer topical economic questions, (iii) an understanding of the nature of uncertainty and methods of making inference in the presence of uncertainty.

The objectives of this course are that students will be able to:

• understand the main techniques of quantitative economics and econometrics, including their strengths and limitations, at a level appropriate for an economics graduate
• understand how these techniques can be applied to test economic theories and measure economic magnitudes, and have some knowledge of methods and results in selected areas of the applied economics literature
• have some practical experience of the application of econometric methods based on practical exercises
• have acquired the necessary skills and knowledge to be able to critically appraise work in the area of applied economics.
• have a good intuitive and theoretical grasp of the dangers, pitfalls and problems encountered in doing applied modelling.
• have the necessary background material so that they are able to go on to study more advanced and technical material in the area of econometrics.
• use the R software package to obtain basic descriptive statistics using real world data and perform introductory econometric analysis. 

ECON20071 AND ECON20072 NOT AVAILABLE TO STUDENTS WHO TOOK ECON10071A OR ECON10071B IN YEAR 1 ECON20110/ECON30370/ECON20222 cannot be taken together

ECON20071 Adv Maths and ECON20072 Adv Stats

 

The aims of this course are:

1. To provide students with an understanding of the quantitative methods and tools that economists use and how they can be appropriately applied and interpreted. These methods and tools are used in practical and academic settings to test economic theories and measure magnitudes that are relevant for economic policy analysis and other decisions. These methods are a key element of the professional training an economist; they will provide a foundation for subsequent study of applied and quantitative topics and are useful in many careers in economics.

2. The course aims to equip students with a number of core competencies including: (i) an awareness of the main empirical approach to economics, (ii) experience in the analysis and use of data and software packages as tools of quantitative and statistical analysis to answer topical economic questions, (iii) an understanding of the nature of uncertainty and methods of making inference in the presence of uncertainty.

The objectives of this course are that students will be able to:

• understand the main techniques of quantitative economics and econometrics, including their strengths and limitations, at a level appropriate for an economics graduate
• understand how these techniques can be applied to test economic theories and
• measure economic magnitudes, and have some knowledge of methods and results in selected areas of the applied economics literature
• have some practical experience of the application of econometric methods based on practical exercises
• have acquired the necessary skills and knowledge to be able to critically appraise work in the area of applied economics.
• have a good intuitive and theoretical grasp of the dangers, pitfalls and problems encountered in doing applied modelling.
• have the necessary background material so that they are able to go on to study more advanced and technical material in the area of econometrics.
• use the R software package to obtain basic descriptive statistics using real world data and perform introductory econometric analysis.

Provisional

This course will introduce students to the theory and practice of econometric analysis. Each week part of the lectures will focus on the theoretical underpinnings of econometric analysis, and the remaining part of the lectures and associated example and computer sessions will focus on the practice and application of these ideas, with the aim of providing students with practical and intuitive real world applications of the theory.

• simple linear regression models
• multiple regression analysis
• inference
• functional form and dummy variables
• parameter properties
• asymptotic inference
• omitted variable bias
• instrumental variables
• introduction to panel data
• time-series data and time-series modelling
• heteroscedasticity
• autocorrelation
• structural breaks
• binary dependent variable models
• maximum likelihood
• Bayesian inference

Synchronous activities (such as Lectures or Review and Q&A sessions, and tutorials), and guided self-study

Semester 1:

5% Problem Sets x 4 (1.25% each)

10% Mid-term test

35% Exam

Semester 2:

5% Problem Sets x 4 (1.25% each)

10% Mid-term test

35% Exam

• Weekly online quizzes
• Tutorial feedback.
• PASS groups.
• Office hours.
• Revision sessions.
• Discussion boards.