SPECIFIC COURSE INFORMATION

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1 COURSE SYLLABUS EUROPEAN CREDIT TRANSFER SYSTEM (ECTS) PILOT PROGRAMME UNIVERSITIES IN ANDALUSIA, SPAIN ACADEMIC YEAR: 2009/2010 DEGREES: Business Administration and Management (BAM) / Double Degree in Law, and Business Administration and Management (LBAM) BASIC COURSE INFORMATION COURSE: ECONOMETRICS (Econometría English group) CODE: 4084 STUDY PLAN: 1998 TYPE (core/obligatory/elective): Core TOTAL CREDITS (LRU/ECTS): 9 THEORY CREDITS: 6 PRAXIS CREDITS: 3 YEAR: 4 th BAM / 5 th LBAM TERM(S): 1 st and 2 nd (Year-long) LEVEL: 2 nd Cycle TEACHING TEAM INFORMATION Course coordinator: NAME: JOSÉ ANTONIO ORDAZ SANZ FACULTY/DEPARTMENT: Faculty of Business Administration / Department of Economics, Quantitative Methods and Economic History ACADEMIC AREA: Quantitative Methods CATEGORY: Profesor Contratado Doctor POD GROUPS CREDITS TOTAL Large Lecture Group Regular Lecture Group Seminar Group Several groups OFFICE HOURS: To be announced OFFICE No.: jaordsan@upo.es TEL.: WEBPAGE: Other teachers: NAME: JOSÉ MANUEL RUEDA CANTUCHE FACULTY/DEPARTMENT: Faculty of Business Administration / Department of Economics, Quantitative Methods and Economic History ACADEMIC AREA: Quantitative Methods CATEGORY: Profesor Asociado Doctor POD GROUPS CREDITS TOTAL Large Lecture Group M-1 Regular Lecture Group M-1 Seminar Group Several groups OFFICE HOURS: To be announced OFFICE No.: jmruecan@upo.es TEL: WEBPAGE: 1

2 NAME: RAÚL BREY SÁNCHEZ FACULTY/DEPARTMENT: Faculty of Business Administration / Department of Economics, Quantitative Methods and Economic History ACADEMIC AREA: Quantitative Methods CATEGORY: Profesor Contratado Doctor POD GROUPS CREDITS TOTAL Large Lecture Group Regular Lecture Group Seminar Group Several groups OFFICE HOURS: To be announced OFFICE No.: rbresan@upo.es TEL.: WEBPAGE: NAME: MARÍA DEL CARMEN MELGAR HIRALDO FACULTY/DEPARTMENT: Faculty of Business Administration / Department of Economics, Quantitative Methods and Economic History ACADEMIC AREA: Quantitative Methods CATEGORY: Profesora Contratada Doctora POD GROUPS CREDITS TOTAL Large Lecture Group Regular Lecture Group Seminar Group Several groups OFFICE HOURS: To be announced OFFICE No.: mcmelhir@upo.es TEL.: WEBPAGE: 2

3 SPECIFIC COURSE INFORMATION 1. COURSE DESCRIPTION. Multiple regression model: estimate validity and dynamic formulation. Simultaneous equation model (Official Gazette of Spain No. 173 for 21 July, 1998, on the description of the Official Study Plan of the Degree in Business Administration and Management (BAM) at Universidad Pablo de Olavide). 2. ACADEMIC CONTEXT PREREQUISITES: Although there are no official prerequisites for this course, students are however expected to have basic knowledge of Economic Theory, Mathematics, Descriptive Statistics and Statistical Inference CONTEXT WITHIN THE DEGREE: Econometrics is a core curriculum course offered in the Degree in Business Administration and Management (BAM) and the Double Degree in Law, Business Administration and Management (LBAM). In both cases, the course is open to 2 nd -Cycle students (4 th Year BAM / 5 th Year LBAM) and taught by professors from the Academic Area of Quantitative Methods, included in the Department of Economics, Economic History and Quantitative Methods. Econometrics draws on knowledge acquired in areas such as Economic Theory, Mathematics and Statistics. On completing the course, students are expected to be able to use available economic variables to develop, estimate, interpret and forecast the performance of economic and business models. For Business Administration and Management majors, Econometrics is the top rung on the learning ladder leading to a comprehensive education in the fields of Statistics and Economics. The knowledge acquired in the course enables students to assess the empirical validity of a variety of economic and/or business theories. The course is designed to be primarily praxis-centered while paying proper attention to the role of theory as scaffolding. Within this broader context, special emphasis is placed on learning to use specialized software such as Econometric Views RECOMMENDATIONS: Students wishing to successfully complete this course should have a solid command of material learned previously in courses on matrix algebra and optimization (i.e. Mathematics: 1 st Year BAM / 1 st Year LBAM), as well as probability distributions and statistical inference (i.e. Statistics and Introduction to Econometrics: 2 nd Year BAM / 3 rd Year LBAM; Statistics II: 3 rd Year BAM / 4 th Year LBAM). In addition, students should be familiar with basic notions relating to Economic Theory studied previously in a variety of different courses: Introduction to Economics (1 st Year BAM) / Political Economy and Public Finance (1 st Year LBAM); Microeconomics (2 nd Year BAM / 2 nd Year LBAM); and Macroeconomics (3 rd Year BAM / 3 rd Year LBAM). 3

4 3. TARGET APTITUDES GENERAL SKILLS: Capacity of analysis and synthesis. Logical and critical reasoning skills. Autonomous learning & research skills/self-sufficiency. Creativity. Teamwork skills & participatory values. Capacity to resolve interpersonal conflicts that may arise within working teams. Ability to put theory into practice. Ability to find and select relevant information. Information Technology (IT) skills SPECIFIC SKILLS: Cognitive (Know ): - Knowledge of the fundamental theoretical and technical building blocks of econometric analysis. - Acquirement of a specific terminology in Econometrics, a good grasp of basic theories, applications and fundamental principles, along with a broad, comprehensive understanding of course material. Procedural/Instrumental (Know how to ): - Development of capacity to analyze economic / business scenarios. - Application on appropriate techniques in order to assess the empirical validity of a variety of economic and/or business theories relating to consumption, savings, income or consumer preferences, for example. - Assessment of new problems using the acquired tools and available statistics; and rigorous and systematic justification of the most relevant results derived from the available statistical data. - Capacity to link knowledge of mathematics, statistics and economic theory obtained in different courses in the Degree. - Enhancing work in groups. - Make appropriate use of EViews software. - Identify relevant sources of economic / business-related information. Attitudinal (Be ): - Analytical, rigorous & methodical. - A critical thinker, especially with regard to how to use available resources to solve real problems. 4

5 4. OBJECTIVES. In his textbook Econometric Analysis, Professor W.H. Greene states that Econometrics is the field in Economics that has to do with the application of the Mathematical Statistics and the tools of the Statistical Inference, to empirical measures of those relationships postulated by the Economic Theory. That is, Econometrics may be defined as a set of quantitative methods for the evaluation, analysis and forecasting in Economics, using Statistics, Mathematics and Economic Theory as their key features. However, the variety of econometric applications has been increased in such a way that other fields are increasingly incorporating econometric models in their studies, e.g. finances, marketing, insurance, organizational economics and others. Teaching Econometrics in the Business Administration and Management Degree is absolutely essential for the students to obtain a high level of quantitative analysis to be developed in a huge variety of professional situations when leaving the University. For instance, in a course of Econometrics a student will be able to analyze the level of competitiveness in an industry, the efficiency between different sales policies, the efficiency in terms of profitability between different marketing campaigns, the relevant variables that may influence in labour absenteeism or any marketing study to introduce a new product into the market. Hence, the general approach of the course will provide not only the statistical and econometrical framework but the way they should be correctly applied with real business and consumer cases. Consequently, it is absolutely necessary for the student to refresh their previous statistical and mathematical knowledge in order to guarantee the maximum apprenticeship provided by this course on Econometrics. Basic knowledge on probability theory, univariate probability distributions (Binomial, Poisson, Normal, χ 2, F- Snedecor and t-student), statistical inference about means and variances in case of normal populations, point and intervals estimation methods, hypothesis testing, the multivariate normal distribution, normally linear and quadratic forms distributions and simple and multiple regression analysis are needed. Particularly, the asymptotic theory and the maximum likelihood estimates, without which the properties of small and large samples estimates would hardly be thoroughly analyzed, should be known as well. With regard to mathematical knowledge, the student should be skilled in the use of matrix algebra and mathematical optimization. Finally, the use of computers in Econometrics is unavoidable and essential nowadays. In this sense, there will be several computer practices throughout the course which will provide the student with the knowledge of the presently most frequently used and up-to-date econometric software in the market, i.e. Econometric Views (EViews). Some basic knowledge of this useful software will be required to the student in the end of the course. In short, the basic objective is to provide students who pass the course with a set of theoretical and practical fundamentals for their professional development in economic and business-related context. DISTRIBUTION OF THE IN-CLASS LEARNING. Large Lecture Groups Regular Lecture Groups Seminar Groups No. of groups No. of sessions

6 5. METHODOLOGY. Econometrics is designed as an attendance-based course with a modular structure as follows: General/background sessions (Large Lecture Group): This module is comprised of eight 1.5-hour sessions per year (4/term). Sessions consist in lectures given by the professor on general theory and background relating to different course topics. Large Lecture Group sessions provide the theoretical basis needed for success in the other modules. Regular attendance is highly recommended. Practical/developmental knowledge-building sessions (Regular Lecture Group): This module is comprised of twenty-eight 1.5-hour sessions per year (14/term). Sessions focus on expounding on theory and putting it into practice. Problems are solved on the blackboard and students practice IT skills in small groups using Econometric Views software, among other activities. Practical/developmental knowledge-building sessions make up the core of the course as it is in these classes that most of the coursework will be carried out. Regular attendance is highly recommended. Guided academic activity sessions (Seminar Group): Guided academic activities are carried out in small groups in four 1.5-hour seminar sessions (2/term) in the presence of the professor. The aim of these sessions is for students to acquire specific skills through applying what they have learnt in lectures and practical knowledge-building activities, including IT workshops on EViews software. Given the percentage of the final grade allotted to module, regular attendance and satisfactory completion of guided academic activities are crucial to success in the course. Other fundamental aspects of the learning process are: Personalized tutorials (Office hours): Tutorial sessions with the professor in office hours are optional. Sessions serve to aid students in organizing their autonomous learning time, clear up specific doubts regarding course topics, correct misunderstood concepts and misguided study habits, guide students with insufficient academic preparation for the course and recommend additional reading to help them get up to speed. The aim here is to boost students confidence in their ability to make the most of the course as well as encourage their desire to learn. Students can also take advantage of tutorial sessions to go over group work with the professor in preparation for seminar sessions. Each professor will announce his/her office hours at the beginning of the 1 st Term. Tutorial schedules will be posted on official bulletin boards and websites as well). Autonomous learning time (Student s prerogative): Autonomous learning on the part of students is yet another pillar in the learning process, and is best carried out individually or small groups. Autonomous learning time should be employed to assimilate topics and hone skills learned in theory and practical knowledge-building sessions. This time might also be well-employed for completing guided and autonomous learning assignments, such as research papers and practical problemsolving activities using EViews software. 6

7 The class Webspace is an invaluable tool and crucial to succeed in the course: Please find below an estimate of the total number of learning hours students are expected to invest in this course (by module): TOTAL NUMBER OF STUDENT LEARNING HOURS: 225 FIRST TERM: 118 learning hours No. of Hours: General/background (Large Lecture Group): 6 Practical/developmental knowledge-building (Regular Lecture Group): 22.5 Guided academic activities/ Theory-into-practice (Seminar Group): 3 Specialised tutorials (attended/online): A) Collective: B) Individual: Individual autonomous learning: 83 A) Study of general/background notes and reading: 15 B) Study/preparation of practical/developmental material: 56 C) Individual/group work on guided academic activities: 12 Other activities (visits, field trips, etc.): Exams & quizzes: 3.5 A) Written exams and/or quizzes: 3.5 B) Oral exams and/or quizzes (on individual autonomous learning): SECOND TERM: 107 learning hours No. of Hours: General/background (Large Lecture Group): 6 Practical/developmental knowledge-building (Regular Lecture Group): 19.5 Guided academic activities/ Theory-into-practice (Seminar Group): 3 Specialised tutorials (attended/online): A) Collective: B) Individual: Individual autonomous learning: 75 A) Study of general/background notes and reading: 15 B) Study/preparation of practical/developmental material: 48 C) Individual/group work on guided academic activities: 12 Other activities (visits, field trips, etc.): Exams & quizzes: 3.5 A) Written exams and/or quizzes: 3.5 B) Oral exams and/or quizzes (on individual autonomous learning): 7

8 6. TEACHING TECHNIQUES. Academic Theory (general / background): X Presentations / Debates: X Specialized Tutorials: Theory into Practice (practical / developmental): X Visits / Field trips: Required Reading Quizzes: Other: Practical IT workshops. COURSE DESIGN / RATIONALE: Practical IT workshops (EViews software) are an essential complement to other learning activities as they provide students with an opportunity to become familiar with new technologies designed to aid in solving reallife econometric problems commonly found in economic and business-related contexts. The importance of such tools is reflected in the number of learning hours/sessions allotted to IT throughout the year and the high percentage of the final grade derived from these workshops. 7. LEARNING MODULES. Introduction to Econometrics (Topic 1) The classic linear regression model: specification, estimation, inference & forecasting (Topics 2 & 3) Failed assumptions of the classic linear regression model: origins, consequences & solutions (Topics 4 & 5) Introducing qualitative variables (endogenous and/or exogenous) in regression models (Topic 6) Simultaneous equation models (Topic 7) Time series models (Topic 8) 8

9 8. BIBLIOGRAPHY GENERAL READING: - Gujarati, D.N. (2003): Basic Econometrics. 4 th ed. McGraw-Hill. - Gujarati, D.N. (2003): Student Solutions Manual for Use with Basic Econometrics. 4 th ed. McGraw-Hill. - Johnston, J. and Dinardo, J. (1997): Econometric Methods. 4 th ed. McGraw-Hill. - Studemund, A.H. (2001): Using Econometrics. A Practical Guide. 4 th ed. Adison-Wesley-Longman SPECIFIC READING: - Asteriou, D. and Hall, S.G. (2007): A Modern Approach Using EViews and Microsoft. New York, Palgrave MacMillan. - Baltagi, B.H. (1999): Econometrics. 2 nd ed. Berlín, Springer-Verlag. - Baltagi, B.H. (1998): Solutions Manual for Econometrics. 1 st ed. Berlín, Springer-Verlag. - Box, G.E.P.; Jenkins, G.M. and Reinsel, G.C. (1994): Time Series Analysis: Forecasting and Control. Upper Saddle River (New Jersey), Prentice-Hall. - Cramer, J.S. (2003): Logit Models from Economics and Other Fields. New York, Cambridge University Press. - Davidson, R. and Mackinnon, J.G. (2004): Econometric Theory and Methods. New York-Oxford, Oxford University Press. - Goldberger, A.S. (2000): A Course in Econometrics. 1 st ed. Harvard University Press. - Gourieroux, C. (2000): Econometrics of Qualitative Dependent Variables. Cambridge, Cambridge University Press. - Greene, W.H. (1993): Econometric Analysis. 3 rd ed. Prentice Hall. - Hamilton, J.D. (1994): Time Series Analysis. New Jersey, Princeton University Press. - Sterling, M.J. (2005): Algebra Workbook for Dummies. Hoboken (New Jersey), Wiley. - Stewart, J. and Gill, L. (1998): Econometrics. 2 nd ed. Prentice Hall. - Wooldridge, J.M. (2003): Introductory Econometrics: A Modern Approach. Mason (Ohio), Thomson South-Western. 9

10 9. ASSESSMENT & GRADING. Each term students will be assessed in the following areas: Theory and praxis covered in General/background sessions (Large Lecture Group) and Practical/developmental knowledge-building sessions (Regular Lecture Group) 70% of the final grade. Activities carried out in the Guided academic activities sessions (Seminar Group), or assigned as Autonomous learning 30% of the final grade. More specifically, students will be evaluated on the following: Assessment of Theory & Praxis (Large Lecture / Regular Lecture Groups): Students will be evaluated on a written exam worth 70% of the final grade for the term (max. 7 pts. on a 10-point scale). This exam will consist in a test on theoretical concepts worth 20% (2 pts.) and a section with problems and exercises testing both theory and praxis; sometimes they can be based directly on the acquired knowledge derived from the computer practices with Eviews, worth 50% (5 pts.). Assessment of Guided Academic Activities (Seminar Group): This section is worth 30% of the final grade for the term (max. 3 pts. on a ten-point scale). Students will be evaluated on a series of activities, with special emphasis on having acquired a solid working knowledge of EViews software. Activities will be carried out individually and/or in working teams. The latter activities will generally consist in preparing ad-hoc assignments: preparation of a task and oral presentation. Approximately, the individual work counts 10% of the final grade while the work in groups makes around 20%. Assessment / Grading Criteria: The evaluation of theory and praxis is designed to assess knowledge students have acquired in Large Lecture / Regular Lecture Group sessions throughout the course. Activities carried out in the Seminar Group and relating to the use of EViews software are designed to assess: (1) the acquisition of practical skills as applied to solving real-life empirical problem scenarios using modern IT tools, and (2) students ability to put theory into practice and demonstrate specific teamwork and/or oral presentation skills. A minimum score of 5 points out of the total of 10 pts. possible (7pts. written exam on theory & praxis + 3 pt. guided activities and IT skills) will be required to pass a given term. A minimum score of 4/10 for each term is required in order to pass the course. The final grade for the course will be either the average of the scores for each term or in the case of students who pass only one term the average of the score for that term and the score for the final exam in June (covering only the material for the term which the student did not pass). A minimum total score of 5/10 must be acquired in order to receive a final passing grade for the course. If none of terms have been passed, students should study the entire course for the final exam in June. Students who do not pass the course in one of the ways mentioned above will be expected to sit official exams in September and/or December. Students sitting official September and December exams will be held 10

11 responsible for material covered throughout the entire course. As in June, the written exam will count for 70% of the final mark; the remaining 30% will correspond to the evaluation of the guided activities and other assignments from the previous academic year. PLEASE NOTE: Scores obtained on activities carried out and/or assigned in Seminar Group sessions will be valid for official exams in June, September and December. Students who have not attended class sessions and/or not completed activities & assignments successfully will be evaluated out of a maximum of 70%, thus forfeiting any chance to obtain the remaining 30%. The extraordinary official exam in February-March will generally be evaluated on the 100% of the total grade. Nevertheless, if the student wishes and so he/she lets the coordinator know it in advance, the exam could be evaluated on a 70% of the total grade and the student can thus make use of his/her partial grade (with a maximum of 30% of the total grade) obtained as a result of the work done in the corresponding seminars of the previous academic courses (Agreement of Junta de Facultad de Ciencias Empresariales of 18 February, 2009). The subject matter to be evaluated will depend on the choice of the student and the corresponding system of evaluation. All minimum requirements for the June exam are applicable to the September and December exams. The use of calculators and/or other resources is at the discretion of the professor and will not be allowed unless expressly indicated otherwise. Students will not be admitted to exams without their national identity card or other official form of picture ID. UPO students under official mobility programs Those students who cannot attend the seminars due to being abroad under official mobility programs (Sócrates-Erasmus, Séneca, Atlanticus...) will do an additional exam, or work that will be conveniently defined, in order to obtain that 30% of the grade. (Agreement of Junta de Facultad de Ciencias Empresariales of 22 May, 2007). Students in this situation should let their professor know before October 31 st. Note: Title II. Chapter II. Article 12.2 and 14.3 of Normativa de Régimen Académico y de Evaluación del Alumnado (signed in at Consejo de Gobierno of UPO on July 18, 2006): When doing essays or other homework, plagiarism and the use of material that is not original, included that obtained in the Internet, without having indicated explicitly the source of that information, and if that is the case, without the permission of the author, such act can lead the student to fail the module, in addition to any other academic penalisation brought about by such dishonest behaviour. The Director of the Department responsible of that module, once informed by the staff involved, the students affected and any other academic part required by the Direction of the Department, decide over the possibility of opening up a formal expedient of penalisation. 11

12 10. WEEKLY COURSE SCHEDULE. WEEK General / Background (Large Lecture Group) Theory / Knowledge- Building (Regular Lecture Group) Guided Learning Activities (Seminar Group) Visits and Excursions Specialised Tutorials Required Reading Quizzes Exams 1 st Term WEEK 1: 29/09-30/ WEEK 2: 06/10 07/ WEEK 3: 13/10 14/ WEEK 4: 20/10 21/ WEEK 5: 27/10 28/ WEEK 6: 03/ (IT: EViews A,B,C,D,E,F) 2 WEEK 7: 10/ WEEK 8: 17/11 18/ WEEK 9: 24/11 25/ WEEK 10: 01/12 02/ WEEK 11: 09/ WEEK 12: 15/ (IT: EViews A,B,C,D,E,F) WEEK 13: WEEK 14: 12/ (A,B,C,D,E,F) 2 & 3 WEEK 15: 19/ (A,B,C,D,E,F) 4 WEEK 16: Final Assessment Period WEEK 17: Final Assessment Period WEEK 18: Final Assessment Period 3.5 (February 8) Topics 3 12

13 WEEK 2 nd Term General / Background (Large Lecture Group) Theory / Knowledge- Building (Regular Lecture Group) Guided Learning Activities (Seminar Group) Visits and Excursions Specialised Tutorials Required Reading Quizzes WEEK 1: 15/02 17/ WEEK 2: 22/ (IT: EViews A,B,C,D,E,F) 5 WEEK 3: 03/ (IT: EViews A,B,C,D,E,F) 5 WEEK 4: 08/03 10/ WEEK 5: 15/ (IT: EViews A,B,C,D,E,F) 5 WEEK 6: 22/ (IT: EViews A,B,C,D,E,F) 5 WEEK 7: 05/04 07/ WEEK 8: 12/04 14/ WEEK 9: 26/ (IT: EViews A,B,C,D,E,F) 7 WEEK 10: 03/ (IT: EViews A,B,C,D,E,F) 7 WEEK 11: 10/ (A,B,C,D,E,F) 6 WEEK 12: 17/ (A,B,C,D,E,F) 6 WEEK 13: 24/ WEEK 14: 31/ WEEK 15: 07/ Review classes WEEK 16: Final Assessment Period WEEK 17: Final Assessment Period 3,5 (June 25) WEEK 18: Final Assessment Period WEEK 19: Final Assessment Period 3,5 (July 9) WEEK 20: Final Assessment Period Exams Topics 13

14 11. COURSE TOPICS & TARGET APTITUDES. TOPIC 1 INTRODUCTION TO ECONOMETRICS: DEFINITIONS, OBJECTIVES AND BASIC INFERENCE THEORY 1.1. Econometrics and its objectives Classification of economic variables and types of data Probability and probability distributions of random variables Statistical inference: estimation, confidence intervals and hypothesis testing. Target aptitudes: What is Econometrics? What is the general framework for posing and solving problems in this subfield of Economics? How are economic variables analyzed? Brush up on your knowledge of Statistics in order to assimilate the fundamental building blocks of econometric analysis. TOPIC 2 THE CLASSIC MODEL OF LINEAR REGRESSION: SPECIFICATION AND ESTIMATION 2.1. The linear regression model: simple/multiple specifications. Matrix formulation. Classic model assumptions Estimation using Ordinary Least Squares (OLS); properties of OLS estimators. The Gauss-Markov Theorem. Interpreting regression model coefficients Goodness of fit. The coefficient of determination, degrees of freedom and the adjusted coefficient of determination; Partial coefficients of determination. The ANOVA Table The Maximum Likelihood Estimator (MLE) Alternative functional forms: potential, semi-log (log-lin or exponential, and lin-log), and reciprocal models. Target aptitudes: Identify and have a solid grasp on the central premise of Econometrics: the econometric model and the intrinsic consideration of the concept of random disturbance error. Know how to work with linear regression the simplest mathematic specification found in models using the fundamental building blocks acquired in Matrix Algebra and Descriptive Statistics. Be familiar with the definition and formulation of the econometric model, as well as with classic model assumptions and estimation. Be able to obtain BLUEtype estimators and test for goodness of fit. Know about other, more complex alternative functional forms. TOPIC 3 THE CLASSIC MODEL OF LINEAR REGRESSION: INFERENCE AND FORECAST 3.1. Normality of random disturbance errors. The Jarque-Bera test Confidence intervals in the classic model of linear regression Hypothesis contrast testing in the classic model of linear regression: significance test for individual explanatory variables, test of the overall significance of the model, and general significance test of a set of linear restrictions. The Restricted model Testing for structural change / parameter stability Point and expected value forecasts. Target aptitudes: Gain a more in-depth understanding of the classic model of linear regression by introducing Statistical Inference: confidence intervals & hypothesis testing. Carry out specific applications statistical testing such as tests for structural change. Acquire the ability to forecast scenarios common to reallife economic and business contexts. 14

15 TOPIC 4 PROBLEMS WITH MODEL SPECIFICATION AND MULTIPLE COLINEARITY 4.1. Omitted relevant variables Included redundant variables Adopting the wrong functional form Measurement errors Tests for specification errors Perfect and imperfect multicolinearity. Detection and treatment for multicolinearity. Target aptitudes: Be familiar with, and know how to identify, different problems associated with model specification and their consequences for estimator properties and inferential aspects of the model. Understand the consequences of the main limitations derived from the existence of a linear relationship between the explanatory variables of a model; know how to detect and treat multicolinearity. TOPIC 5 GENERALIZED LEAST SQUARES (GLS): HETEROSKEDASTICITY AND AUTOCORRELATION 5.1. Non-spherical disturbances: nature and causes Properties of Ordinary Least Squares (OLS) estimators with non-spherical disturbances Generalized Least Squares (GLS) estimators Heteroskedasticity: concept, detection methods and estimation Autocorrelation: concept, detection methods and estimation. Target aptitudes: What is heteroskedasticity? What is autocorrelation? Identify the most common methods used to detect the presence of heteroskedasticity and autocorrelation; understand how these concepts affect estimation efficiency and empirical application using corrections which allow estimates to recover desirable properties. TOPIC 6 DUMMY VARIABLE AND DISCRETE DEPENDENT VARIABLE MODELS 6.1. Dummy variables Discrete choice models; Binary choice models: The Linear Probability, Logit and Probit models. Target aptitudes: Understand how qualitative and discrete variables are introduced in econometric models; distinguish between models with dummy variables (where the explanatory variables are qualitative) and binary choice models (where the explained variable is either 1 or 0, thus reflecting the presence of a given attribute). Know the fundamental differences between diverse model specifications and be able to explain model estimation, regression coefficients and suitable non-standard goodness-of-fit measures. TOPIC 7 SIMULTANEOUS EQUATION MODELS 7.1. Principal characteristics of simultaneous equation models: nature, assumptions, presentation and examples Identifying simultaneous equation models Single equation estimation methods: Ordinary Least Squares (OLS), Indirect Least Squares (ILS) & Two-Stage Least Squares (2SLS) Estimators System estimation methods: Three-Stage Least Squares (3SLS). Target aptitudes: Grasp the notion of multi-equation models with simultaneous effects in an economic context. Understand the identification problems triggered by the variables in the model, associated with certain behavioral relations. Know about different parameter estimation methods for this type of model and how they are applied to real-life economic scenarios. 15

16 TOPIC 8 TIME SERIES MODELS 8.1. Introduction Stationary stochastic processes Non-stationary stochastic processes and unit root tests Autoregressive vectors Co-integration. Target aptitudes: Use econometric principles to approach problems and pose solutions for time series models. Define basic features of stochastic processes which explain why forecasting models are particularly valuable when compared with simpler statistical methods. Learn methods allowing for adequate forecasting using these models. Calculate optimum predictors and asses forecasting errors. 12. ON-GOING ASSESSMENT & FOLLOW-UP. In addition to exam results and other grading criteria outlined in the corresponding section above, the following factors will be taken into account for final assessment: student attitude, participation and effort (consistent completion of activities assigned by the professor throughout the year). All students enrolled in the course must provide the corresponding card to his/her professor before October 31 st. It must be correctly filled and with a stuck photo. 16