Quantitative Methods for Business Management Research II

Index

General Information

Estimated learning time

Competences / Learning outcomes

Learning objectives

Teaching blocks

Teaching methods and general organization

Official assessment of learning outcomes

Reading and study resources

General Information

Course unit nameQuantitative Methods for Business Management Research II
Course unit code568290
Academic year2024/2025
CoordinatorVicente Royuela Mora
DepartmentDepartment of Econometrics, Statistics and Applied Economics
Credits2.5
Single programS

Estimated learning time

Total number of hours : 62.5 Hours
ActivitiesType of trainingHoursObservations
Face-to-face and/or online activities22.5
- Lecture with practical component Face-to-face 15
- IT-based class Face-to-face 7.5
Supervised project20
Independent learning20

Competences / Learning outcomes to be gained during study

  • Basic competences 

    — Knowledge forming the basis of original thinking in the development or application of ideas, typically in a research context.

    — Skills to enable lifelong self-directed and independent learning.


    General competences

    — Ability to establish research questions or original hypotheses based on a relevant theoretical framework and then evaluate or verify them empirically.


    Specific competences

    — Ability to search for and select relevant qualitative and quantitative data, analyse and interpret data, and transform data into useful information to conduct research on business issues.

    — Mastery of the implementation stages of business research activities, including selecting the methodological approach appropriate to tackle the problem and framing the potential contributions of solving the problem to the context of current knowledge and debate.

    — Ability to apply the most appropriate qualitative methodologies for high-quality research on how organizations function and implement these methods rigorously.

Learning objectives

Referring to knowledge

This course covers two advanced topics in econometrics:

— In relation to multivariate analysis, students must understand the objective and the mathematical and statistical underpinnings of multivariate techniques, such as factor analysis, the analysis of principal components and clustering methods. At the end of the course students should be able to know when to choose each type of analysis, which is the correct technique to use in each case and what the results of the analysis mean.

— In relation to panel data, this course covers econometric modelling and estimation methods for panel data in economics and business. The objective of the course is that students learn how to conduct empirical research in economics and business. Accordingly, the course shall focus on empirical applications. Asymptotic theory is introduced when needed and not the primary focus of interest.

Learning objectives

Referring to knowledge

This course covers two advanced topics in econometrics:

— In relation to multivariate analysis, students must understand the objective and the mathematical and statistical underpinnings of multivariate techniques, such as factor analysis, the analysis of principal components and clustering methods. At the end of the course students should be able to know when to choose each type of analysis, which is the correct technique to use in each case and what the results of the analysis mean.

— In relation to panel data, this course covers econometric modelling and estimation methods for panel data in economics and business. The objective of the course is that students learn how to conduct empirical research in economics and business. Accordingly, the course shall focus on empirical applications. Asymptotic theory is introduced when needed and not the primary focus of interest.

Teaching blocks

  • 1 Multivariate analysis

  • 2 Panel data

Teaching methods and general organization

Students acquire the ability to estimate and interpret panel data models. They should be able to implement multivariate analysis to conduct research in Business Studies.

 

Official assessment of learning outcomes

Students are expected to present an essay on each of the course’s teaching blocks. Each essay is worth 20% of the final grade.

There is also a final examination worth 60% of the final grade. To be eligible to weight their marks, students must achieve a minimum mark of 3.5 out of 10 in the final examination. Students are allowed to access all materials (books, notes, etc.) during the final examination.

Basic competences are assessed by means of the essays and the examination.

The ability to establish research questions or original hypotheses that can be empirically tested (generic competence) is assessed by means of the essays, as students have to propose a question or hypothesis and empirically test it. Similarly, in both essays students are required to search for relevant data, analyse it and interpret the results (specific competence). Students must find the appropriate technique and apply it rigorously (specific competences).

The repeat assessment examination follows the same procedure as that of single assessment.

Examination-based assessment

A final examination, worth 100% of the final grade. Students are not allowed to access any material (books, notes, etc.) during the final examination. Students have to demonstrate having acquired all competences described above.

Reading and study resources

Book

Baltagi, B. (2013) Econometric Analysis of Panel Data. 5th ed. Chichester, UK [etc.] : John Wiley & Sons

Comment:

Book

Greene, WiH. (2012) 7th ed. Econometric analysis. Boston [etc.] : Pearson Education

Comment:

Book

Härdle, W; Simar, L. (2015): Applied Multivariate Statistical Analysis. Springer.

Comment:

Book

Timm, N.L. (2002): Applied Multivariate Analysis. Springer.

Book

Wooldridge, F.M. (2010) Econometric Analysis of Cross Section and Panel Data. 2nd ed. MIT Press 

Comment:

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