Econometrics is a compulsory subject taught in the first term of year 4 on the Double Degree in Business Administration and Management and Law. It is part of a module called Advances in Business Administration and Management.



On this subject students are trained to use the linear regression model to analyse the behaviour of certain economic variables based on the data available, e.g. to explain the evolution of sales at a company based on different factors such as spending on advertising, whether or not there is an online portal, location, sector of the economy to which the firm belongs, etc. On completing the subject students should be able to answer questions such as these: what factors influence sales at a firm and what factors do not?; by how much can sales be expected to increase if spending on advertising increases by a given amount?; what sales is a firm with certain characteristics likely to have? Obtaining and understanding such information is highly useful in management and decision-making processes at firms.



The subject, of a markedly instrumental nature, prepares the students to take decisions in the planning of the company regarding, for example, the level of production, the future sales of a product, the selling price, the level of salary of its staff, the assumable expenditure on production factors, its policy of contribution to the improvement of the environment or its technological policy.



To work properly with econometrics, students need the knowledge of descriptive statistics, probability theory and statistical inference provided by the earlier subjects of Statistics & Data Analysis and Statistics Applied to Business. They also need to know how to use the basic linear and matrix algebra; knowledge that they should have acquired in upper secondary school and in the Mathematics I and II subjects taught in year one of the Business Administration and Management

degree.



The linear regression model is useful and necessary for learning other advanced subjects in the degree that require the analysis of economic and business models for decision-making purposes.

Specific competences:



C1. To analyse critically the basic elements of the linear regression model with a view to understanding the logic of econometric modelling and being able to specify causal relationships between economic variables.

C2. To apply basic econometric methods to estimate and validate economic relationships based on the statistical information available and using suitable IT tools.

C3. To provide reasoned interpretations for the results of estimating and validating the econometric model with a view to drawing up economic reports.

C4. To present in a clear and concise way, both orally and in writing, the conclusions obtained in an empirical application.





During the year the following transversal competences of the module are worked on:



1. Ability to make reasoned judgements based on results obtained (M03CM02)*.

2. Development of learning abilities with a view to acquiring a high degree of autonomy for future studies and self-education (M03CM05).

3. Ability to communicate fluently in writing and orally (M03CM09).

4. Capability for analytical thinking and critical reflection (M03CM11).

5. Ability to communicate in a foreign language, preferably English, French or German (M03CM13).



* These are the codes assigned to the transversal competences in the module Advances in Business Administration and Management in the official description of the degree course (www.ehu.eus).



From the Cross-Curricular Competences catalogue of the UPV/EHU: Social commitment (critical understanding of global socio-environmental issues).







Learning outcomes:



- To understand the specification of the linear regression model, and in particular the meaning and implications of its basic assumptions (C1).

- To know how to incorporate quantitative and qualitative explanatory variables into regression models (C1).

- To interpret the coefficients of regression models, including non-linear specifications in variables (C1, C3).

- To organise and systemise significant statistical information (C3, C4).

- To use an econometric software package (Gretl) to analyse economic databases and interpret the results (C2, C3).

- To estimate regression models by Ordinary Least Squares (OLS)(C2).

- To test hypotheses in regression models on economic relationships (C3).

- To predict the variable of interest with an econometric model (C3).

- To check the validity of certain basic assumptions of the regression model and learn to modify the analysis if assumptions are not met (C3).

- To select between alternative specifications based on the properties of the estimators (C3).

- To interpret interpreting the results of estimating econometric models (C3, C4).

- To make well-informed judgements on relevant socio-economic and environmental issues using available data and appropriate econometric models (C3, C4).



The faculty who teach this subject promote academic ethics by encouraging the use of good practices both in their attitude in the classroom and in the performance of exams, assignments, practices, individual and group work throughout the course.



This subject aims to provide a comprehensive, flexible training adapted to the needs of society in line with the IKD model and the EHU's 2030 agenda. To achieve this goal, the framework of the United Nations Sustainable Development Goals (2015) has been incorporated into quantitative analysis of real economic problems through a research-based learning approach. In this way, the aim is to strengthen the relationship between classroom teaching, the SDG framework and the research and professional orientation that the Econometrics teaching team has been giving to this subject in recent years.

1. Introduction to Econometrics.

What is Econometrics? Concept of Model. Economic Model vs Econometric Model. The Structure of the economic data. The Econometric Model. The error term. Stages in the elaboration of an Econometric Model.



2. The Linear Regression Model (I). Specification.

Linear Regression Model specification: assumptions. Population regression function. Interpretation of coefficients. Qualitative explanatory variables.



3. The Linear Regression Model (II). Estimation.

Method of Ordinary Least Squares (OLS). Sample regression function: important results. Goodness of fit: coefficient of determination. Residual analysis. Changes in the units of measurement. Restricted Least Squares Estimator.



4. The Linear Regression Model (III). Inference and Prediction.

Properties and distribution of the OLS estimator. Estimator of its covariance matrix. Confidence intervals for the population coefficients. Testing hypothesis about a single population parameter. Testing multiple linear restrictions. Multicollinearity. Prediction. Confidence intervals.



5. Some specification issues.

Selection of regressors, functional form and heteroskedasticity.

The teaching methodology is based on two types of sessions: master classes and practical classes. The calendar with the distribution of the different sessions (schedule) as well as the material necessary for learning this subject are available in the virtual classroom of the subject created in the eGela teaching support platform.

The master classes mainly develop the different topics of the program. The theoretical concepts are explained and various illustrative examples of them are shown to make them easier to understand. Problems, questionnaires and exercises are also provided so that students can solve them individually or collectively and thus understand the applicability of the theory to different case studies.

The practical classes are developed in three modalities: classroom practices (6 sessions), practices in the computer rooms (2 sessions) and seminars (4 sessions). In the practical classes, several tasks will be carried out, discussed and presented: some with the aim that the students acquire new knowledge on their own and others, in order to strengthen the concepts presented in the master classes.

The general organization of all practical classes is very similar. First of all, the students at eGela are provided with the statement of the task they must work on during non-attendance hours and bring prepared to the practical class. Secondly, this task is worked on individually or in groups in the classroom. Usually, for each assignment, the students will provide the teacher with evidence or results of the activities carried out (oral and/or deliverable presentation) either in the practical class itself or in the subsequent master classes. Thus, the follow-up of the learning proposed in these tasks is prolonged throughout several face-to-face sessions providing feedback on the work done and the evidence presented, suggesting improvements when necessary. In this way, students have the opportunity to become aware of their learning and, if necessary, to redirect their learning tactics to improve their results. The first part of this methodology focuses on preparing and consolidating knowledge autonomously, the second part allows for the resolution of doubts and the third part complements the progress achieved.

The econometric software used in this course to solve the tasks is Gretl. The use of this programme is proposed because it presents the advantages of being open source and free to use. The aim of these sessions is for the students to acquire a basic skill level with this program to be able to use it in the resolution of tasks.

• Continuous Assessment System
• Final Assessment System
• Tools and qualification percentages:
  • Written test to be taken (%): 70
  • Realization of Practical Work (exercises, cases or problems) (%): 30

The ongoing evaluation system is used in the ordinary assessment session. It is compulsory and it is structured as follows:



30% of the grade for the subject is obtained by the evidences derived from the tasks submitted in the classroom sessions.



70% of the grade for the subject is obtained by means of an individual written exam in which students must answer theoretical questions and solve problems related to the theoretical and practical contents taught on the subject.



The final grade is the weighted mean of the scores obtained in the classroom evidences and in the individual exam, though a minimum grade of 4/10 is required in the said exam for an overall grade to be awarded (otherwise students will be awarded the grade corresponding to their score in the exam without the continuous assessment part).



In the tests and examinations of this subject you can use calculator and when appropriate the PC available in the computer rooms that the UPV/EHU makes available. Electronic devices such as telephones or tablets may not be used (not even as a calculator). Students in this subject are subject to the Protocol on Academic Ethics and Prevention of Dishonest or Fraudulent Practices in Assessment Tests and Academic Work at the UPV/EHU. This document can be accessed on line at: https://www.ehu.eus/documents/2100129/0/6.-+b%29+Protocolo+plagio+cas+-.pdf/11f13960-d46a-cf5a-ac13-ebfb5ad10acd



Students who wish to be declared exempt from ongoing evaluation as provided for in Article 8 of Chapter II of the Regulations for Students' evaluation at the University of the Basque Country, must complete the form provided in the eGela platform. This form, duly signed, must be delivered to the professor before the finish of the nine weeks of class. The list of students exempt from ongoing evaluation will be published in the eGela platform within a maximum period of 3 days.



For those students who are awarded exemption from the mixed evaluation system, evaluation in the ordinary assessment session consists of an individual exam (which accounts for the entire grade awarded) covering all the learning outcomes for the subject.



In accordance with what is stated in art. 12 of chapter II of the Regulations Governing the Evaluation of Students in Official Degree Programmes at the UPV/EHU, in order to waive the ordinary call to examinations, it will be sufficient not to sit the exam set out in the official examination calendar, which is 70% of the mark in the case of continuous evaluation and 100% in the case of final evaluation. Their grade will then be recorded as No Presentada / No presentado.



The faculty who teach this subject reserve the right to modify the evaluation system because of causes of major force. Any change will be announced on the e-gela platform in good time and in an appropriate manner.

In the extraordinary assessment session assessment consists of an individual exam (which accounts for the entire grade awarded) covering all the learning outcomes for the subject. It will take place in the computer rooms.





Students may decline to take part in the extraordinary assessment session merely by not sitting the individual exam. Their grade will then be recorded as No presentada / No presentado.



The faculty who teach this subject reserve the right to modify the evaluation system because of causes of major force. Any change will be announced on the e-gela platform in good time and in an appropriate manner.

Econometric software: GRETL

eGela platform

Basic bibliography

Main Bibliographic References:



1. Dougherty, Christopher (2011). Introduction to Econometrics. 4º ed., Oxford University Press.



2. Stock, James H. and Mark Watson (2011). Introduction to Econometrics, 3º ed. Prentice Hall.



3. Wooldridge, Jeffrey M. (2003), Introductory Econometrics: A modern approach, 2ª ed., Thomson.





Online material.



Gonzalez, Pilar and Susan Orbe (2014). Using Gretl for applied econometrics, EHU OpenCourseWare, Creative Commons, (hyperlink: ocw.ehu.es, [20014/01][Eng]).



Fernández, Javier (2009). Introductory Econometrics, EHU OpenCourseWare, Creative Commons, (hyperlink: ocw.ehu.es, [2009/03][Eng]).

In-depth bibliography

Textbooks:

1. Hill, R. Carter, William E. Griffiths and Guay C. Lim (2003). Principles of Econometrics, 4ª ed., Wiley.

2. Ramanathan, Ramu (2002). Introductory Econometrics with Applications, 5th ed., SouthWestern.

Exercises:

1. Adkins, L.C. (2011), Using gretl for Principles of Econometrics, 4th Edition
Oklahoma State University,
http://www.learneconometrics.com/gretl/using_gretl_for_POE4.pdf

2. Ramanathan, Ramu (2002). Introductory Econometrics with Applications, 5th ed., SouthWestern.

3. Wooldridge, Jeffrey M., (2003). Introductory Econometrics: A modern approach, 2ª ed., Thomson.

Journals

Econometric journals (English)

1. Econometric Reviews
2. Empirical Economics Journal
3. International Journal of Forecasting
4. Journal of Applied Econometrics
5. Journal of Business and Economic Statistics
6. Journal of Econometrics
7. Journal of Economic Dynamics and Control
8. Journal of Forecasting
9. Oxford Bulletin of Economics and Statistics
10. Review of Economics and Statistics1
11. Review of Economic Studies
12. SERIEs, Journal of the Spanish Economic Association

Econometric journals (Spanish)

1. Revista de Economía Aplicada
2. Revista de Estudios Regionales
3. Investigaciones Económicas
4. Ekonomiaz

More information on how to access electronic journals available in the UPV/EHU library at:
https://www.ehu.eus/es/web/biblioteka/aldizkari-elektronikoak

Web addresses

Institutions and Databases

1. EUSTAT (Basque Statistics Institute) http://www.eustat.es
2. INE (Spanish Statistics Institute) http://www.ine.es
3. Bank of Spain http://www.bde.es
4. EUROSTAT http://ec.europa.eu/eurostat
5. OECD http://www.oecd.org
6. International Monetary Fund http://www.imf.org
7. World Bank http://www.worldbank.org
8. Madrid Stock Market http://www.bolsamadrid.es
9. National Bureau of Economic Research http://www.nber.org/data_index.html
10. Journal of Applied Econometrics data archive http://econ.queensu.ca/jae/
11. Panel Study of Income Dynamics http://www.psidonline.isr.umich.edu/data/
12. US Census Bureau http://www.census.gov/
13. World Bank https://datatopics.worldbank.org/world-development-indicators/
14. WHO The global health observatory https://www.who.int/data/gho
15. World Values Survey https://www.worldvaluessurvey.org/wvs.jsp
16. ATLAS of Sustainable Development Goals https://datatopics.worldbank.org/sdgatlas/
17. United Nations SDG indicators https://unstats.un.org/sdgs/indicators/database/
18. Sustainable Development Goals index and dashboards https://www.sdgindex.org/
19. World Happiness Report https://worldhappiness.report/ed/2020/#appendices-and-data
20. Econstats http://www.econstats.com/
21. Our World in data https://ourworldindata.org/
22. The Global Economy https://www.theglobaleconomy.com/download-data.php
23. Worlddometer https://www.worldometers.info/

More information on databases and e-resources available at the UPV/EHU library:
https://www.ehu.eus/es/web/biblioteka/datu-baseak-eta-e-baliabideak