21205. Econometrics . Group 34

Subject

Name21205 - Econometrics
Group Grup 34 ( Campus Digital )
Academic Year 2018-19
Credits6 credits
Period First semester
Language Catalan
Titulació
  • Degree in Business Administration - Second year
  • Double Degree in Business Administration and Law - Third year
  • Double Degree in Business Administration and Tourism - Second year

Lecturers

LecturerOffice hours for students
Start timeEnd timeDay of the weekStart dateEnd dateLocation/Building
Francisco Javier Franconetti Manchado
javier.franconettijavier.franconetti@uib.esuib.es
08:30h09:00h Thursday 10/09/201808/02/2019 Despatx 8 - edif. Arxiduc - Cita prèvia per e-mail
12:30h13:00h Tuesday 10/09/201808/02/2019 Despatx DB200 - edif. Jovellanos - Cita prèvia per e-mail

Context

This subject starts out from the contents already studied in "Analysis of Economic Data". In order to be able to grasp the contents of "Econometrics" more easily, students are strongly advised to review the contents of "Analysis of Economic Data" as soon as the academic year starts.

The main objective of "Econometrics" is the detailed study of some econometric techniques commonly used in applied research in the context of Economics and Business. The first part of the course covers the principles of statistical inference, the concepts of estimator and confidence interval, as well as hypothesis testing. The second part of the course introduces the study of the simple linear regression model, considering the relevant methods of hypothesis testing, and its generalization to multiple regression, incorporating qualitative explanatory variables (known as "dummies") into the linear regression model.

The language of instruction for this group is English, which implies that during the course students should learn specific economic/business/tourism-related vocabulary and develop their communication skills in English, therefore acquiring further specialized knowledge of the English language.

Requirements

A good knowledge of the contents of "Analysis of Economic Data" and "Mathematics" will facilitate the understanding of the contents of this subject, as well as a good knowledge of English.

Recommended

A good knowledge of the contents of "Analysis of Economic Data" and "Mathematics" will facilitate the understanding of the contents of this subject, as well as a good knowledge of English.

Skills

The main objective of the subject is the understanding of some econometric techniques commonly used in applied research in the Economics and Business context. It will provide basic training in handling econometric techniques as tools of analysis of Economics and Business data, using the theoretical frameworks taught in various Economics and Business courses, and in interpreting and explaining the results obtained in the light of those theories, as well as in carrying out predictions. The methods and techniques explained in the "Econometrics" subject are transferable to most Economics and Business datasets that students may come across in their future professional careers.

Specific

  • CE2.1.7 A partir de datos de interés económico-empresarial, ser capaz de aplicar las herramientas estadísticas y econométricas adecuadas para el análisis de la empresa y su entorno
  • CE2.3.7 Conocer las fuentes de datos estadísticos y económicos relevantes así como las herramientas de análisis adecuadas para preparar la toma de decisiones en empresas y organizaciones, especialmente en los niveles operativo y táctico
  • CE2.4 Defender las soluciones propuestas de una manera articulada a partir de los conocimientos teóricos y técnicos adquiridos

General

  • CG3 Capacidad para comunicarse en inglés
  • CG4 Capacidad para usar habitualmente una variada gama de instrumentos de tecnología de la información y las comunicaciones
  • CG5 (CB3) Tener la capacidad de reunir e interpretar datos relevantes para emitir juicios que incluyan una reflexión sobre temas relevantes de índole social, científica o ética

Basic

You may consult the basic competencies students will have to achieve by the end of the degree at the following address: http://www.uib.eu/study/grau/Basic-Competences-In-Bachelors-Degree-Studies/

Content

Topic 1. Statistical inference: estimation

Topic 2. Statistical inference: hypothesis testing

Topic 3. The simple linear regression model

Topic 4. The multiple linear regression model

Topic 5. Qualitative explanatory variables (dummies)

Thematic content

Topic 1 Statistical inference: estimation

1. Main concepts
2. Parameter estimation: point estimator and estimator properties
3. Parameter estimation: interval estimator (confidence interval)
4. Main estimators

Topic 2 Statistical inference: hypothesis testing

1. Hypothesis specification
2. Test statistic and decision criteria
3. Test quality: error types, power and p-value
4. Main parametric tests
5. Using EXCEL and GRETL in inference

Topic 3 The simple linear regression model

1. Econometric modelling
2. Linear correlation and regression
3. Specification of the simple linear regression model
4. Statistical hypotheses on the classical regression model
5. Estimation by Ordinary Least Squares (OLS)
6. Model testing, validation and selection
7. Prediction
8. Using GRETL and exercises

Topic 4 The multiple linear regression model

1. Specification of the multiple linear regression model
2. Estimation by Ordinary Least Squares (OLS)
3. Interpretation of results and parameter testing (individual, joint and restrictions)
4. Model testing, validation and selection
5. Prediction
6. Using GRETL and exercises

Topic 5 Qualitative explanatory variables (dummies)

1. Specification and OLS estimation with one dummy and its category groups
2. Specification and OLS estimation with two or more dummies and their category groups
3. Interactions
4. Dummies and structural breaks
5. Dummies and seasonality
6. Using GRETL and exercises

Teaching methodology

Attended activities (2,4 credits, 60 hours)

TypeNameG. typeDescriptionHours
Theory classes Lectures Large group (G)

Lectures allow a detailed exposition of the most important aspects of each topic, especially the new concepts. They also allow a special focus on the most difficult issues, where students need more learning support. Finally, they also facilitate the understanding of the context in which each topic is placed, including the relationships between the different topics. Lectures will take up an average of 40 hours per student.

40
Practical classes Computer classes Medium group (M)

At the end of topics 2 to 5 there will be computer classes to deepen the understanding of the theory and to alllow the student to apply the theoretical concepts to statistical data. The data analysis package EXCEL and the econometric package GRETL will be used to this end. Computer sessions will take up an average of 10 hours per student.

13
Assessment Final exam Large group (G)

For those students who fail the course during the semester in-class assessment, and for Pathway B students, there will be two final exams in the examination periods defined by the University, the first one in January and the second one in February. The final exam is worth 100% of the final mark and the pass mark is a 5. The length of the final exam will be 2 hours. Note that students who pass through in-term assessment either Bloc 1(statistical inference, worth 40% of the final mark) or Bloc 2(regression analysis and dummies, worth 60% of the final mark) will not need to repeat that bloc in the final exam.

2.5
Assessment In-class tests Medium group (M)

In two selected classes during the semester the student will be required to take two in-class tests, the first one on Bloc 1 (statistical inference) and the second one on Bloc 2 (linear regression model and dummies). Each test is worth 40% of the final mark and a minimum mark of 3 in each test is required for the student to pass the course by continuous assessment.

3
Assessment Computer-based tasks Medium group (M)

In one selected computer class during the semester the student will be required to carry out a set of computer tasks in GRETL for assessment. This set of tasks will be based on Bloc 2 (topics 3 to 5 - linear regression model and dummies) and is worth 20% of the final mark. The questions and the data will be provided at the start of the class and the answers must be uploaded before the end of the class. A minimum mark of 3 in the computer-based assessment is required for the student to pass the course.

1.5

At the beginning of the semester the subject schedule will be available to students through the UIBdigital platform. This schedule will at least include the dates for the continuous assessment exams and assignment deadlines. Furthermore, the lecturer will inform students as to whether the subject syllabus will be carried out according to the schedule or otherwise, including Campus Extens.

Non-attended activities (3,6 credits, 90 hours)

TypeNameDescriptionHours
Individual self-study Self-study

Students should study the lecture material before each lecture and also review the lecture content after each lecture in order to ensure that they have grasped the basics of the subject. They should also solve the exercises proposed to them. Similarly, to deepen the understanding of lecture contents and place them in context it is important to study the bibliography of the course.

90

Specific risks and protective measures

The learning activities of this course do not entail specific health or safety risks for the students and therefore no special protective measures are needed.

Student learning assessment

There are two itineraries to pass the course. The itinerary A is based on continuous assessment, and it is the one to be followed by students who are not working part-time and who are enrolled for the first time in the course. The itinerary B is based on a single final exam and is optional both for part-time-working students and for the repeaters of the course who have takenat least 50% of the continuous assessment in a past course.

The evaluation of the itinerary A consists of three evaluation tests. The three evaluation tests are recoverable. To pass the course in the Continuous Assessment (AC), itis necessary to obtain a weighted-average grade of 5 on the two blocks and, at least, a grade of 3 on each one of the 3 assessment tests. Then, the student pass the course in the AC and do not have to take the final exam.

Block 1 . (40% of the final grade) If a grade equal to or greater than 5 is obtained in the first partial exam, the subject content of block 1 is released from the final exam. If the grade is smaller than 3, this block must be recovered in the final exam. If the grade is between 3 and 5, it can be compensated with that of the second block.
Block 2 . (60% of the final grade) It consists of two tests, a partial exam that weighs 40% in the final grade and a practical test in Gretl that weighs 20%. Therefore, within this block, the partial exam represents 66.7% of the grade and the practical test accounts for 33.3%. To pass the second block, it is necessary to obtain a minimum grade of 3 for each one of the two evaluation tests of this block. If a student does not obtain a grade of 3 in any of the two tests, then he must take the final exam and recover ALL the second part of the course. If the student obtains a grade equal to or greater than 3 in one of the two tests and the weighted average of the block is equal to or greater than 5, then the subject content of this block is released from the final exam. If the weighted-average grade of the block is between 3 and 5, it can be compensated with that of the first block.
The final exam is a written exam that takes place during the period of complementary evaluation and in the period of extraordinary evaluation. It is a recovery exam for the students of the itinerary A, and it is a final exam for those of the itinerary B.
The student will get a numerical grade between 0 and 10 for each one of the assessment activities. The overall score is calculated considering different weights of the different forms of evaluation. The student who achieves a minimum final grade of 5 pass the course. A student will be considered not presented if he takes a number of exams corresponding to a percentage equal to or less than 35% of the final grade.

In accordance with article 33 of the Academic Regulations, "regardless of the disciplinary procedure that may be followed against the offending student , the demonstrably fraudulent performance of any of the evaluation elements included in the teaching guides of the subjects will lead, at the discretion of the teacher, to an undervaluation in the qualification that may suppose the qualification of "suspense 0" in the annual evaluation of the subject".

Final exam
Type Assessment
Technique Short-answer tests ( retrievable )
Description

For those students who fail the course during the semester in-class assessment, and for Pathway B students, there will be two final exams in the examination periods defined by the University, the first one in January and the second one in February. The final exam is worth 100% of the final mark and the pass mark is a 5. The length of the final exam will be 2 hours. Note that students who pass through in-term assessment either Bloc 1(statistical inference, worth 40% of the final mark) or Bloc 2(regression analysis and dummies, worth 60% of the final mark) will not need to repeat that bloc in the final exam.

Assessment criteria

Set according to the competences described.

Final grade percentage: 0% for the training plan A
Final grade percentage: 100% for the training plan B

In-class tests
Type Assessment
Technique Objective tests ( retrievable )
Description

In two selected classes during the semester the student will be required to take two in-class tests, the first one on Bloc 1 (statistical inference) and the second one on Bloc 2 (linear regression model and dummies). Each test is worth 40% of the final mark and a minimum mark of 3 in each test is required for the student to pass the course by continuous assessment.

Assessment criteria

Set according to the competences described.

Final grade percentage: 80% for the training plan A
Final grade percentage: 0% for the training plan B

Computer-based tasks
Type Assessment
Technique Real or simulated task performance tests ( retrievable )
Description

In one selected computer class during the semester the student will be required to carry out a set of computer tasks in GRETL for assessment. This set of tasks will be based on Bloc 2 (topics 3 to 5 - linear regression model and dummies) and is worth 20% of the final mark. The questions and the data will be provided at the start of the class and the answers must be uploaded before the end of the class. A minimum mark of 3 in the computer-based assessment is required for the student to pass the course.

Assessment criteria

Set according to the competences described.

Final grade percentage: 20% for the training plan A
Final grade percentage: 0% for the training plan B

Resources, bibliography and additional documentation

Basic bibliography

HILL, R. C., GRIFFITHS, W.E. and LIM, G. C. (2012), "Principles of Econometrics", Wiley, 4th edition.

STOCK, J.H. and WATSON, M.M. (2012): Introduction to Econometrics?. Pearson..

WOOLDRIDGE, J. M. (2006), "Introductory Econometrics: a modern approach", South-Western, 2nd edition.

Additional bibliography

ARCARONS, J. and CALONGE, S. (2008), "Microeconometría: introducción y aplicaciones con software econométrico para Excel", Delta Publicaciones.

ASHENFELTER, O., LEVINE, P. B. and ZIMMERMAN, D. J. (2006),"Statistics and Econometrics: methods and applications", Wiley.

GREENE, W. H. (2007), "Econometric analysis", Addison-Wesley / Prentice Hall, 6th edition.

GUJARATI, D. (2009), "Econometrics", 5th edition, McGraw-Hill.

KENNEDY, P. (2003), "A Guide to Econometrics",MIT Press.

MADDALA, G. S. (1992), "Introduction to econometrics", Prentice Hall, 2nd edition.

NEWBOLD P.,CARLSON, W. andTHORNE, B.(2009), "Statistics for business and economics", Addison-Wesley / Prentice Hall, 7th edition.

NOVALES, A. (1996), "Estadística y Econometría", McGraw-Hill.