Objectives:



This course is a follow-up of the course Introduction to Econometrics. It is designed to provide students with advanced econometrics skills which are needed to complete quantitative research at the Degree level. It deals with the effects that the violation of some basic assumptions in the Classical Linear Regression Model has on the specification and estimation of the model: heteroskedasticity, autocorrelation, stochastic regressors and dynamic models. Different testing procedures are proposed to detect these problems and adequate estimation methods are derived for every situation, as an alternative to Ordinary Least Squares.

Specific competences:



1. Comprehend the meaning and relevance of the basic assumptions imposed in the specification of an econometric model in order to be able to propose and use more realistic assumptions.



2. Distinguish different and alternative estimation methods and evaluate their use according to the economic variables of interest in order to get reliable results.



3. Identify statistical data sources in order to be capable of searching and organizing the economic data that are relevant for the explanation of a particular economic phenomenon.



4. Be acquainted with the use of econometric analysis software to analyze relationships among economic variables.



5. Be capable of interpreting the results obtained with an econometric analysis in order to make coherent and meaningful informs on the behavior of economic data.



General Competences :



• Ability to search, analyse and synthesize the information extracted from different sources being capable of making self-criticism, self-learning and reasoned judgements on relevant economic, social or scientific issues. (G004 of the DEGREE).



• Write informs and transmit ideas on any economic issue, with clarity and coherence, to both specialized and unspecialized audience, (G006 of the DEGREE).



• Capability of being part of a multidisciplinary research group, developing the abilities required in every situation: work in group, leadership, initiative, creativity and decision making. (G005 of the DEGREE).

Program



1. Generalization of the Classical Linear Regression Model



Review of basic statistical concepts and asymptotic theory. The regression model with non-spherical disturbances.

Properties of the OLS estimators. Generalized Least Squares (GLS). Feasible Generalized Least Squares (FGLS).



2. Heteroskedasticity.



Definition and causes. Structures of heteroskedasticity. Heteroskedasticity tests. GLS or WLS: Generalized or Weighted Least Squares. FGLS: Specification of a model for heteroskedasticity. OLS: Robust estimator of the variance and covariance matrix of the OLS estimator.



3. Autocorrelation.



Structures of autocorrelation. Autocorrelation tests. GLS: Generalized Least Squares. FGLS: Feasible Generalized Least Squares. Cochrane-Orcutt method. OLS: Robust estimator of the variance and covariance matrix of OLS estimator.



4. Stochastic Regressors and Dynamic Models.



Errors in variables and latent variables: Properties of the OLS estimator. Lagged endogenous variable: Properties of the OLS estimator. Instrumentan Variables (IV) method of estimation. Hausman test. . Models including lags of the endogenous variable.

Teaching will be based on three different types of sessions: formal lectures where the different topics of the course will be

developed, applied coursework lectures, which concentrate on exercises and practices, and seminars.



Teaching Web platform: Egela. The student will find there different material used along the course.



Teaching materials: Slides, lectures notes and documents available for the student in the web page of the course.



Written essays: The exercises will be discussed in the classroom. Students may be required to hand in written responses to specific problems either via Moodle or directly to the teacher. On the other hand, the participation in a joint work made in group might be evaluated

• Sistema de Evaluación Final
• Herramientas y porcentajes de calificación:
  • Prueba escrita a desarrollar (%): 70
  • Realización de prácticas (ejercicios, casos o problemas) (%): 30

The degree of achievement of the competences will be evaluated in a continuous process and the final mark will be calculated according to the following proportions:



Written exam: 70%;



Solutions and presentation of problems (individually and/or in group): 30%. One of the exercises will be done in the computer classroom. The students may have in this case the help of the class notes.



The score in the written exam must be larger than 4 out of 10 to be considered in the continuous evaluation.



In addition, the positive and active participation of the student, including class attendance can be evaluated.



Those students who are not continuously evaluated will be graded according to a final exam of the degree of achievement of the competences of the course.



The student who wishes to be evaluated through the final evaluation system must submit in writing to the teacher responsible of the course the waiver of continuous assessment in the first 9 weeks of the course.



In the second round of exams, 100% of the mark will be determined based on a final written exam.



If the student does not sit for final exam, this will entail automatic waiver of the corresponding call and the qualification of "non presented".



The evaluation will preferably be face-to-face. If this is not possible, the final exam will be taken using the services available at Egela. The student will have a limited time to download the final exam form from Egela and upload the solution to that platform (preferably in pdf and in any case in a perfectly legible format to enable evaluation). This exam is individual, so in order to guarantee it the teaching team of the course might request an oral test of verification of the answers after the correction of the exam with an individualized conversation with the student.

Written final exam.



The evaluation will preferably be in the classroom. If this is not possible, the final exam will be taken using the services available at Egela. The student will have a limited time to download the final exam form from Egela and upload the solution to that platform (preferably in pdf and in any case in a perfectly legible format to enable evaluation). This exam is individual, so in order to guarantee it the teaching team of the course might request an oral interview of verification of the answers after the correction of the exam with an individualized conversation with the student.

- Notes of the course
- Software: GRETL
http://gretl.sourceforge.net/

Bibliografía básica

Basic Readings:



Gujarati, D. (2009), Basic Econometrics, ed. McGraw-Hill, 5ª edition.



Ramanathan, R. (2002), Introductory Econometrics with applications, South-Western, 5th edition.



Wooldridge, J.M. (2003), Introductory Econometrics: A modern Approach, South-Western, 2nd edition.



Dougherty, C. (1992) Introduction to Econoetrics. Oxford University Press.





Exercises:



Alonso, A., Fernández, J., Gallastegui, I., (2004), Econometría, Appendices, Prentice-Hall (Pearson).



Fernández, A., González, P., Regúlez, M., Moral, P., Esteban, V. (2005), Ejercicios de Econometría, ed.McGraw-Hill, 2 ª edición.



Ramanathan, R. (2002) Instructor’s Manual to accompany, del libro Introductory Econometrics with applications, ed. South-Western, 5th edition, Harcourt College Publishers.



Wooldridge, J.M. (2003), Student Solutions Manual, del libro Introductory Econometrics: A modern Approach, ed.South-Western, 2nd edition.



Recommended Problem Set. Disponible Available in Moodle

Bibliografía de profundización

Greene, W. (1997), Econometric Analysis. Ed. Prentice Hall, 3ª edition.
Johnston, J. (1983), Econometric Methods, ed. Vicens Vivens, 4ª edition.
Maddala (1996), Introduction to Econometrics, ed. Pearson: Prentice Hall.
Pindyck, R.S. y Rubinfeld, D.L. (1998), Econometric Models and Economic Forecast, ed. McGraw-Hill, 4ª
edición.

Revistas

Computational Economics
Econometrica
Econometric Reviews
Econometric Theory
Empirical Economics Journal
International Journal of Forecasting
Journal of Applied Econometrics
Journal of Business and Economic Statistics
Journal of Econometrics
Journal of Economic Dynamics and Control
Journal of Forecasting
Oxford Bulletin of Economics and Statistics
Review of Economics and Statistics
Review of Economic Studies
Studies in Nonlinear Dynamics and Econometrics

Direcciones web

Course page:
o http://moodle4.ehu.es/course/category.php?id=1158

Software:
o http://gretl.sourceforge.net. Gretl: Free (Open Source) Econometric Software.
o http://www.learneconometrics.com/gretl.html, Lee C. Adkins gretl web page.
o
Institutions:
o http://www.eustat.es. EUSTAT
o http://www.ine.es. INE
o http://www.bde.es. Banco de España
o http://ec.europa.eu/eurostat. EUROSTAT
o http://www.oecd.org OCDE
o http://www.imf.org. International Monetary Fund
o http://www.worldbank.org. World Bank
o http://www.bolsamadrid.es Madrid Stock Market
Data:
o http://www.nber.org/data_index.html
o http://www.estadief.minhac.es/
o http://fisher.osu.edu/fin/osudown.htm
o http://econ.queensu.ca/jae/
o http://www.psidonline.isr.umich.edu/data/
o http://www.census.gov/