Quantitative Methods
Code: 40094 ECTS Credits: 15| Degree | Type | Year |
|---|---|---|
| Economic Analysis | OB | 1 |
Contact
- Name:
- Amedeo Stefano Edoardo Piolatto
- Email:
- amedeo.piolatto@uab.cat
Teachers
- Jordi Caballe Vilella
- Fernando Payro Chew
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
There are no specific prerequisits.
Objectives and Contextualisation
This module provides students advanced quantitative tools for economic analysis. The module covers optimization, probability and statistics.
The module is organized in two sections. The first one covers the foundations of optimization theory. The second section provides students
with the theoretical foundations of probability and statistics necessary for econometric and financial analysis.
Competences
- Capacity to articulate basic economic theory, analytically deriving them from mathematical reasoning
- Capacity to identify basic statistical analysis and econometric techniques deriving them from the laws of probability and statistics
- Conceptually analyse a specific economic problem using advanced analytical tools
- Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context
- Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
Learning Outcomes
- Describe statistical topics on which stochastic economic analysis and empirical analysis is based
- Distinguish the element to be included and the necessary assumptions for proposing a decision-making problem with very simple strategic interactions
- Framing an economic question of decision within a strategic context in simple math problem and derive its response through mathematical logic
- Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context
- Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
- Use of mathematics to analyse economic problems
Content
I. Optimization
1. Sets and Metric Spaces:
2. Functions and Correspondences:
3. Linear Spaces and Linear Algebra:
4. Smooth functions, Optimization and Comparative Statics:
5. Difference and Differential Equations:
II. Probability and Statistics
1. Probability
2. Measure Theory
3. Random Variables and Distributions
4. Expectation
5. Special Distributions
6. Functions of Random Variables7. Stochastic Processes and Limiting Distributions
8. Sampling
9. Estimation
10. Hypothesis Testing
For a detailed description of the content of this module go to https://sites.google.com/view/idea-program/master-program
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Theory classes | 112.5 | 4.5 | 1, 2, 3, 5, 4, 6 |
| Type: Supervised | |||
| Problem solving and tutorials | 75 | 3 | 1, 2, 3, 5, 4, 6 |
| Type: Autonomous | |||
| Personal study, study groups, textbook readings, article readings | 187.5 | 7.5 | 1, 2, 3, 5, 4, 6 |
The course will consist of sessions where the instructor presents the material, and sessions specifically dedicated to problem solving. Students are encouraged to form study groups to discuss assignments and readings.
The proposed teaching methodology may undergo some modifications according to the restrictions imposed by the health authorities on on-campus courses.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Class Attendance and Problem sets and assignments | 20% | 0 | 0 | 1, 2, 3, 5, 4, 6 |
| Exam Part I | 40% | 0 | 0 | 1, 2, 3, 5, 4, 6 |
| Exam Part II | 40% | 0 | 0 | 1, 2, 3, 5, 4, 6 |
This modul does not contemplate an evaluation from a single comprehensive exam
| Exam Part I |
40% |
|
Exam Part II |
40% |
|
Problem sets and assignments + Class attendance and active participation |
20% |
The proposed evaluation activities may undergo some changes according to the restrictions imposed by the health authorities on on-campus courses.
In this course, the use of Artificial Intelligence (AI) technologies is not permitted in any of its phases. Any work that includes fragments generated with AI will be considered a breach of academic honesty and may result in a partial or total penalty to the activity's grade, or more severe sanctions in serious cases.
Bibliography
Optimization:
Axler, S.J., Linear algebra done right (Vol. 2). New York: Springer.
Carter, M., Foundations of mathematical economics. MIT Press.
Sydsæter, K., Hammond, P., Seierstad, A. and Strom, A., Further mathematics for economic analysis. Pearson education
Probability and Statistics:
Ash, R.B., Real Analysis and Probability, Academic Press.
Bierens, H.J., Introduction to the Mathematical and Statistical Foundations of Econometrics, Cambridge University Press.
Billingsley, P., Probability and Measure, Wiley.
DeGroot, M.H. and Schervish, M.J., Probability and Statistics, Pearson.
Hogg, R.V., McKean, J. and Craig, A.T., Introduction to Mathematical Statistics, Pearson.
Lindgren, B.V., Statistical Theory, Chapman and Hall/CRC.
Rice, J.A., Mathematical Statistics and Data Analysis, Cengage Learning.
Additional references will be provided during the course.
Software
- Matlab
- R
- Phyton
- Stata
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLABm) Practical laboratories (master) | 30 | English | first semester | morning-mixed |
| (TEm) Theory (master) | 30 | English | first semester | morning-mixed |