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2021/2022

Quantitative Methods

Code: 40094 ECTS Credits: 15
Degree Type Year Semester
4313805 Economic Analysis OB 1 1
The proposed teaching and assessment methodology that appear in the guide may be subject to changes as a result of the restrictions to face-to-face class attendance imposed by the health authorities.

Contact

Name:
Maria Teresa Cabeza Gutes
Email:
Maite.Cabeza@uab.cat

Use of Languages

Principal working language:
english (eng)

Teachers

Jordi Caballé Vilella
Katerina Chara Papioti

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

  1. Describe statistical topics on which stochastic economic analysis and empirical analysis is based
  2. Distinguish the element to be included and the necessary assumptions for proposing a decision-making problem with very simple strategic interactions
  3. Framing an economic question of decision within a strategic context in simple math problem and derive its response through mathematical logic
  4. 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
  5. Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
  6. 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 http://idea.uab.cat/master_program.php .

 

Methodology

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.

Activities

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

Assessment

 

Final  Exams   

50%  

Class  attendance    and    active    participation    

20%  

Problem  sets    and    assignments    

30%  

The proposed evaluation activities may undergo some changes according to the restrictions imposed by the health authorities on on-campus courses. 

 

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Class Attendance and Problem sets and assignments 50% 0 0 1, 2, 3, 5, 4, 6
Final Exams 50% 0 0 1, 2, 3, 5, 4, 6

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