Teaching guide ECONOMETRICS INDEX CARD Subject Data Código Titulación Nombre Carácter Ciclo 1313.- Grado en Administración y Dirección de Empresas, Mención Creación y Dirección de Empresas, Itinerario Emprendedores Econometrics Formación Básica Grado Curso 3º Créditos ECTS 6.0 Materia Centro Econometría Centro Universitario EDEM Curso académico 2015 2016 Professor Name Conchado Peiró, Andrea Departament 10 Análisis Económico Office hours: Wednesday, from 15 to 17 Asignatura: Grado en ADE para Emprendedores Página 1
SUMMARY Econometrics is a compulsory basic course scheduled during the first semester of year 3 of the Grade in Management and Business Administration with a total workload of 6 ECTS credits (150 hours). This course completes the topics covered by the Quantitative Methods Module in this degree. The aim of the course is to provide students with the basic knowledge of an academic discipline as Econometrics, which combines concepts from Economic Theory, Mathematics and Statistics. The basic topics of study in this subject include the estimating regression models, the formulation and testing of hypotheses for these models, prediction and the effects and problems that follow from the possible breach of any of the cases dealt. About these estimates, the basic models of qualitative and quantitative dependent variable are explained. Likewise, models that include independent variables of both types are considered. As a discipline of mathematical-statistical contents, the students should use the previous knowledge acquired of calculus and statistics. It is, therefore, a training course with a wide range of theoretical and practical content, which is based on previously acquired skills and designed to allow, with the support of appropriate informatics tools, a comprehensive view of the instruments of quantitative analysis used in the study and prediction of economic and business reality. The main content of the course focuses primarily on the development of the linear regression model, its assumptions and associated problems. PRIOR KNOWLEDGE Relación con otras asignaturas de la misma titulación Restrictions of registration with other subjects of the degree have not been specified. Other requisites Although there are no prerequisites, it is recommended to have studied previously the subjects of Mathematics I, Mathematics II, Basic Statistics and Statistical Inference. Asignatura: Grado en ADE para Emprendedores Página 2
COMPETENCES BASICS: Analysis and synthesis (GI.1) Organization and planning (GI.2) Oral and written communication in the native language (GI.3) Ability to use English in professional environments (GI.4) Ability to use ICT in the field of study (GI.5) The ability to find, collect and analyze information from different sources (GI.6) Ability to solve problems (GI.7) Decision making (GI.8) Ability to negotiate and reconcile interests effectively (GI.9) Ability to transmit and communicate complex ideas and approaches to both specialist and non-specialist stakeholders (GI. 10) Ability to work in team (GP.1) Critic and auto critic thinking (GP.3) Manage time effectively (GP.5) Self-study Adaptation Creativity Coordination SPECIFIC: Know and use appropriately the various quantitative and qualitative methods to argue in a correct and analytical way evaluate results and predict economic and financial magnitudes (EG.7). Knowing how to perform strategic diagnosis in complex and uncertain environments, using appropriate methodologies in this process (EA.4). Ability to make decisions in certainty and uncertainty environments (EA.5). Ability to apply mathematical and analytical methods for the analysis of economic and business contexts (EA.6). Ability to define, expose and solve complex problems in a systemic way (EA.8). Ability to argue in formal, graphical and symbolic languages (EA.10). Ability to plan, to organize and to control and evaluate the implementation of business strategies (EA.30). The capacity to evaluate the context in which entrepreneurial ideas and initiatives can be introduced (EA.30) To develop a critical capacity on the Spanish and international economic news (EA.73) RESULTADOS DEL APRENDIZAJE Evaluate the state of an economic context or business environment from the observation of the economic reality. Asignatura: Grado en ADE para Emprendedores Página 3
To promote the ability to use logical / strategic thinking to address real world economic situations. Manage the basic quantitative tools (especially linear regression analyses) and apply them to the analysis of economic and business environments. Use statistical software to estimate different types of regression models. Interpret results derived from the application of regression techniques and methods. Identify problems associated to regression analyses and acquire knowledge about their treatment. Students will get the ability to select a theoretical frame of reference to develop different types of analyses. Students will get the knowledge and understanding of the basic tools of quantitative nature for the analysis, diagnosis and economic prospection. Identify, classify, reason, argue and interpret the relationships between economic variables. Be capable of applying different regression methods and analysis techniques under uncertainty using statistical software. COURSE CONTENTS Chapter 1. Introduction: econometrics and econometric models 1.1. Econometrics: concept 1.2. Elements of an econometric model 1.3. Stages in the econometric modeling: process of econometric research. 1.4. The role of Econometrics: economic data and usefulness of econometrics. Chapter 2. The classical linear regression model 2.1. The simple linear regression model (model specification, estimation by Ordinary Least Squares [OLS]). 2.2. The multiple regression model. 2.3. Coefficients interpretation. 2.4. Units of measurement and functional forms. Chapter 3. Properties and hypotheses associated to the regression model 3.1. Descriptive properties of the regression 3.2. Statistical hypotheses in the simple linear and multiple regression models 3.3. Probabilistic properties of the regression model 3.4. Goodness of fit: measures, the determination coefficient and models selection criteria (AIC). Asignatura: Grado en ADE para Emprendedores Página 4
Chapter 4. Hypotheses tests in the multiple regression model 4.1. Assumptions of the classical linear model and introduction to hypothesis testing 4.2. Hypothesis testing on a single parameter: the t statistic. 4.3. Hypothesis testing on a set of parameters (multiple linear constraints): the F statistic. 4.4. Testing for structural stability 4.5. Prediction Chapter 5. Multiple regression analysis with qualitative information 5.1. The dummy variables. 5.2. Interpretation of coefficients of dummy variables 5.3. Multiple categories 5.4. Interactions 5.5. The logistic regression model: an overview. Chapter 6. Breach of the basic assumptions of the regression model 6.1. Multicollinearity 6.2. Normality 6.3. Heteroscedasticity 6.4. Autocorrelation VOLUMEN DE TRABAJO CLASS ACTIVITIES/ ACTIVIDADES PRESENCIALES HORAS Lectures 28.0 Complementary activities 2.0 Practical sessions 28.0 Partial Exams 2.0 Total class activities 60.0 SELF-STUDY / ACTIVIDADES NO PRESENCIALES Individual tasks and works 10,0 Self-study 20,0 Preparation of evaluation activities 20,0 Preparation of lectures 15,0 Preparation of practical classes, problems and group work 20,0 Resolution of practical classes 5,0 Total self-study 90,0 TOTAL 150,0 TIMELINE Asignatura: Grado en ADE para Emprendedores Página 5
Chapter / Practical activities SESSIONS/WEEKS Chapter 1: Introduction: econometrics and econometric models. 1 Chapter 2: The classical linear regression model. 2.1 The simple linear regression model 2 Chapter 2: The classical linear regression model. 2.2. The multiple regression model 3 Software for econometrics (Session 1) 4 Chapter 2: The classical linear regression model. 2.3. Coefficients interpretation 5 Software for econometrics (Session 2) 6 Chapter 2: The classical linear regression model. 2.4. Units of measurement and functional forms 7 Software for econometrics (Session 3) 8 Chapter 2: Summary and key points 9 Chapter 3. Properties and hypotheses associated to the regression model. 3.1. Descriptive properties of the regression 10 Chapter 3. Properties and hypotheses associated to the regression model. 3.2 Statistical hypotheses in the simple linear and multiple 11 regression models Chapter 3. Properties and hypotheses associated to the regression model. 3.3. Probabilistic properties of the regression model 12 Chapter 3. Properties and hypotheses associated to the regression model. 3.4. Goodness of fit: measures, the determination coefficient 13 and models selection criteria (AIC). Chapter 3: Summary and key points 14 Partial exam of Chapters 1, 2, 3 15 Chapter 4. Hypotheses tests in the multiple regression model. 4.1. Assumptions of the classical linear model and introduction to 16 hypothesis testing Chapter 4. Hypotheses tests in the multiple regression model. 4.2. Hypothesis testing on a single parameter: the t statistic. 17 Chapter 4. Hypotheses tests in the multiple regression model. 4.3. Hypothesis testing on a set of parameters (multiple linear constraints): 18 the F statistic. Chapter 4. Hypotheses tests in the multiple regression model. 4.4. Testing for structural stability 19 Chapter 4. Hypotheses tests in the multiple regression model. 4.5. Prediction 20 Chapter 4: Summary and key points 21 Chapter 5. Multiple regression analysis with qualitative information. 5.1. The dummy variables. 5.2. Interpretation of coefficients of dummy 22 variables Chapter 5. Multiple regression analysis with qualitative information. 5.3. Multiple categories 23 Chapter 5. Multiple regression analysis with qualitative information. 5.4. Interactions 24 Chapter 5. Multiple regression analysis with qualitative information. 5.5. The logistic regression model: an overview 25 Chapter 5: Summary and key points 26 Chapter 6. Breach of the basic assumptions of the regression model. 6.1. Multicollinearity and specification errors. 6.2. Normality 27 Asignatura: Grado en ADE para Emprendedores Página 6
Chapter 6. Breach of the basic assumptions of the regression model. 6.3. Heteroscedasticity 28 6.4. Autocorrelation Chapter 6: Summary and key points 29 External programmed visit 30 TEACHING METHODOLOGY 1. Interactive master classes The master class will serve as an introduction to the main contents of the subject. The professor will describe the theoretical concepts underlying the field of econometrics, on the basis of different sources of scientific knowledge. Besides, the professor will provide students with examples and applications of this subject oriented to the development of their entrepreneurship skills. Master classes will not be limited to oral presentations by the professor. In order to increase the commitment of the students during the process of teaching and learning, other resources will be used during the course, such as audiovisual media and publications specialized in this area. The professor will foster the active participation of students during the master class, through the formulation of questions and the proposal of discussions, with the aim to set new contents in the context of previous knowledge. Summaries, lists of key points and conceptual maps will be provided by the professor to sum up the contents of each chapter. 2. Problem based learning Problem based learning will be introduced by the professor through guided examples, focused on the contents previously exposed in the interactive master class. Subsequently, the professor will set up several problems to students in such a manner that this activity will facilitate learning the theoretical contents of the subject. The resolution of these problems will require a deep understanding of the main concepts as well as the relationships between them. In addition, computer skills will be needed to solve the problems posed by the professor, particularly statistical software for the estimation of regression models and basic statistics, such as Excel, PSPP and R. 3. Case studies Case studies will be analyzed by students working in pairs. Each group of students will be assigned a particular problem or situation in an innovative business. Students will assume the responsibility to make an in depth examination of the context, formulation of hypothesis, specification of regression models, estimation of parameters, discussion of the results and assessment of the applicability of the model in the context of study. Besides, students will report periodically about the advances of their case study, Asignatura: Grado en ADE para Emprendedores Página 7
through the application of the knowledge and skills developed in the interactive master class and problem based learning activities. EVALUATION CRITERIA Evaluation of students will take place through continuous assessment and exams. Class attendance is mandatory for optimal monitoring of the subject. When the number of unjustified absences exceeds the 10% of the sessions, i.e. more than 3 sessions of 30, continuous assessment will not be considered in the final mark. As a result, the maximum score which can be achieved by student is limited to 6 points in the exams. 1. Continuous assessment (40%) Continuous assessment will be based on: - Written assignments (20%) Students working in pairs have to report the latest advances on their case studies on a regular basis. These advances will consist on the application of the contents of the subject to analyze the situation assigned to each group. To accomplish this task, students are expected to produce high quality written reports. These reports usually will explain the particular aspect of the subject they are covering, they will show the main results of the analysis and finally they will discuss the results, using the econometrics jargon. On the other hand, the resolution of problems using statistical software will be also described through written assignments. In these reports, students will emphasize in their decisions while setting the options of the software, the code or menu options used and the discussion of the results in the context of the problem. - Oral presentations (10%) All groups will expose briefly the situation or problem posed in their case study, the difficulties and challenges they found and the solutions they proposed. The schedule of these presentations will be organized in such a manner that different stages of the analysis and discussion may be presented to the rest of students. To put it differently, oral presentations of case studies will be spaced over the course so that students can present different stages of the development of their work. - Multiple choice tests and One minute papers (10%) Asignatura: Grado en ADE para Emprendedores Página 8
Theoretical knowledge will be assessed through multiple choice tests. In spite of the practical approach of this subject, students have to demonstrate their understanding and familiarity with the main concepts of the subject. It is recommended to devote some effort to the comprehension of the contents of the subject, as this background is essential for the appropriate application in the resolution of problems and case studies. Additionally, one minute papers will be used by the professor to identify strong and weak points at the end of the sessions. This information, obtained from the responses of students, will be highly valuable for the professor for improving her teaching. 2. Exams (60%) Each registration entitles three examination sessions: A partial exam in the middle of the course, the final exam in January 2016 and an additional exam for recovery in April 2016. Contents will be divided in two partial sections for the assessment. The average mark in both partial sections must be higher to 5 to pass the subject. Exams will consist on a set of problems to be solved by applying theoretical concepts and the use of statistical software. Results have to be discussed using econometric jargon in the context of the problem. Students with failing grades in the first partial exam will have the opportunity to pass it during the final exam in January 2016. Meanwhile, students who failed the final exam (1 st or 2 nd part of the subject, or both) will have to repeat the exam in April 2016. REFERENCIAS Main reference books: -Gujarati, D.N., y D.C. Porter (2010). Econometría. 5ª edición. McGraw-Hill. -Wooldridge, J. M. (2012). Introductory econometrics: a modern approach. South-Western College Publishers (2012), 5 th edition -Contreras, D. y Belaire, J. (2000). Introducció a l Econometria. Universitat de València. -Uriel, E. (2013). Introducción a la Econometría. Manual electrónico, Valencia. (http://www.uv.es/~uriel/libroes.htm) -Uriel, E. (2013). Introduction to econometrics. Electronic textbook, Valencia. (http://www.uv.es/~uriel/libroes.htm) Asignatura: Grado en ADE para Emprendedores Página 9
Complementary bibliography: -Stock and Watson. (2010). Introduction to econometrics. Addison-Wesley. -Uriel, E. y Gea, I. (1997). Econometría Aplicada, Editorial AC. -Uriel, E., D. Contreras, M.L. Moltó y A. Peiró (1990). Econometría: El modelo lineal. Editorial AC. -Dougherty, C. (2011). Introduction to Econometrics. 4th edition. Oxford University Press. Asignatura: Grado en ADE para Emprendedores Página 10