Econometrics I
Code: 102308 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| 2501572 Business Administration and Management | OB | 2 |
| 2501573 Economics | OB | 2 |
Contact
- Name:
- Luca Salvadori
- Email:
- luca.salvadori@uab.cat
Teachers
- Michael David Creel
- María Dolores Márquez Cebrián
- Luca Gambetti
- Marina Fuentes Rojas
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
It is highly recommended that the student has successfully completed Mathematics I, II and Statistics I, II. Having full command of the materials presented in these courses is essential to succeed in Econometrics I.
Objectives and Contextualisation
Econometrics I presents basic tools for the empirical analysis of relationships between economic variables. The course begins with the simple regression model, already introduced in Statistics II, and continues with multiple regression, including both quantitative and qualitative regressors.
The goal of this course is for students to learn to extract information from economic data using basic regression analysis, being able to rigorously assess the advantages and limitations of this tool. Major emphasis shall be placed on understanding the intuition behind the general theoretical aspects of econometric analysis. Throughout the course numerous applications using real data and econometric software will be presented to help students learn to value the empirical applications of the tools introduced.
This course provides the fundamentals for the analysis of economic data that continues with the courses of Econometrics II.
Competences
- Business Administration and Management
- Apply the basic statistics for improving processes of analysis and systematisation of business information and learn rigorously and scientifically about the company chain of value.
- Capacity for adapting to changing environments.
- Capacity for independent learning in the future, gaining more profound knowledge of previous areas or learning new topics.
- Capacity for oral and written communication in Catalan, Spanish and English, which enables synthesis and oral and written presentation of the work carried out.
- Demonstrate initiative and work individually when the situation requires it.
- Identify and apply econometric methodology to respond to the problems that appear in the empirical study of some economic data.
- Organise the work in terms of good time management, organisation and planning.
- Select and generate the information necessary for each problem, analyse it and take decisions based on that information.
- Take decisions in situations of uncertainty, demonstrating an entrepreneurial and innovative attitude.
- Use of the available information technology and adaptation to new technological environments.
- Economics
- Apply the basic statistics for improving processes of analysis and systematisation of business information and learn rigorously and scientifically about the company chain of value.
- Capacity for adapting to changing environments.
- Capacity for independent learning in the future, gaining more profound knowledge of previous areas or learning new topics.
- Demonstrate initiative and work individually when the situation requires it.
- Identify and apply econometric methodology to respond to the problems that appear in the empirical study of some economic data.
- Organise the work in terms of good time management, organisation and planning.
- Select and generate the information necessary for each problem, analyse it and take decisions based on that information.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills in order to undertake further training with a high degree of autonomy.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Take decisions in situations of uncertainty, demonstrating an entrepreneurial and innovative attitude.
- Use of the available information technology and adaptation to new technological environments.
Learning Outcomes
- A capacity of oral and written communication in Catalan, Spanish and English, which allows them to summarise and present the work conducted both orally and in writing.
- Capacity to adapt to changing environments.
- Capacity to continue future learning independently, acquiring further knowledge and exploring new areas of knowledge.
- Demonstrate initiative and work independently when required.
- Identify and apply the appropriate econometric methodology to respond to the problems appearing in the empirical study of some economic data.
- Look for economic information from different sources: databases, Internet, etc.
- Make decisions in situations of uncertainty and show an enterprising and innovative spirit.
- Organise work, in terms of good time management and organisation and planning.
- Prepare the data obtained from the sources for subsequent quantitative analysis.
- Select and generate the information needed for each problem, analyse it and make decisions based on this information.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills in order to undertake further training with a high degree of autonomy.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Use available information technology and be able to adapt to new technological settings.
- Use information technology programmes to perform a quantitative analysis of the data.
Content
Unit 1: Introduction to econometric analysis
- What is econometrics? Objectives
- Causation versus correlation
- The nature of economic data: experimental data versus observational data
- The structure of economic data
Unit 2: The simple regression model: estimation
- The simple regression model. The regression line
- Least squares estimation. The fitted line. Goodness of fit
- Interpretation of the coefficients. Special cases: dependent variable in logs. Qualitative regressor
- Distribution of the estimator under classical assumptions. Statistical properties
- Applications
Unit 3: The simple regression model: inference
- Inference in a regression model
- Hypothesis testing with the t-statistic
- Confidence intervals for a regression parameter
- Applications
Unit 4: The multiple regression model: estimation
- The multiple regression model. The population regression function
- Least squares estimation. The sample regression function
- Goodness of fit. Coefficient of determination. Adjusted coefficient.
- Distribution of the estimator under ideal conditions. Statistical properties
- The components of the variance of the estimator
- Applications.
Unit 5: Linear regression analysis: inference and extensions
- Hypothesis testing with the t statistic. Confidence intervals
- Hypothesis testing using the F statistic
- Inference under the presence of collinearity
- Regression models with variables in log. Polynomial forms. Interaction terms
- Test of structural change
- Applications
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lab sessions | 17 | 0.68 | 6, 5, 9, 17 |
| Lectures | 32.5 | 1.3 | 2, 5, 7, 10, 16 |
| Type: Supervised | |||
| Tutoring | 6 | 0.24 | 2, 5, 4, 8, 7, 9, 10, 16, 17 |
| Type: Autonomous | |||
| Studying and problem solving | 88.5 | 3.54 | 6, 2, 1, 3, 4, 5, 8, 7, 9, 10, 16, 17 |
The course will be structured as follows:
1. Lectures
During lectures, key concepts and methods will be presented using many examples to facilitate a clear understanding of the materials presented. An exercise list will be provided for each unit. Students will be asked to work on them, as an independent activity, in small groups or on their own. The instructor will select some exercises from the lists to be discussed in class and can use some of them as an evaluation activity.
2. Lab sessions
In order to better grasp the different econometric concepts and methods, some of the sessions will take place in the computer room, or in the classroom using personal computers. In these sessions econometric software (RStudio) will be used. The main goal fo these sessions will be for the student to learn to rigorously apply to tools presented.
3. Tutoring
Students can use instructor's office hours to get help on specific questions. Office hours will be announced in either the intranet (Campus Virtual) or in the instructor's webpage.
4. Studying
It is expected that the activities described above, take about one a fraction of the time that the student is supposed to dedicate to Econometrics I. The rest of the time should be filled with students' independent work (studying, reading the course textbook, problem solving,...). This activity is crucial to assimilate the theoretical aspects and the applications of the tools presented.
Note: The proposed teaching methodology may undergo some modifications according to the restrictions imposed by the health authorities on on-campus courses.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Exercise submission | 25% | 2.5 | 0.1 | 6, 2, 1, 3, 4, 5, 8, 9, 10, 16, 17 |
| Final exam | 50% | 2 | 0.08 | 1, 4, 5, 8, 7, 15, 14, 13, 11, 12, 10, 17 |
| Midterm | 25% | 1.5 | 0.06 | 1, 4, 5, 8, 7, 10, 17 |
This subject does not offer the option for comprehensive evaluation. Student's evaluation will be based on the following activities:
1. Midterm exam
There will be a written test covering the material from Units 1,2 and 3. It will be a closed book exam.
2. Final exam
There will be a written test covering the material from Units 1,2,3,4 and 5. It will be a closed book exam.
3. Assignments
Students will be asked to turn two sets of exercises that will be done during lab sessions. The first set, with a weight of 10%, will be done in a lab session before the midterm. The second set, with a weight of 15%, will be done in a lab session before the final.
Grading Policy
a. Course grade is calculated according to the following expression:
COURSE GRADE=0.25*ASSIGNMENTS + 0.25* MIDTERM + 0.50*FINAL
b. To pass the course, the course grade needs to be equal or greater than 5. If the course grade is between 3.5 and 4.9, the student can sit in the retake exam, as established in section Retake process included below. The student will fail the course if the grade is below 3.5.
c. A student who has not participated in any of the assessment activities will be consideredas 'Not evaluable'.
Calendar of evaluation activities
The dates of the evaluation activities (midterm exams, exercises in the classroom, assignments, ...) will be announced well in advance during the semester.
The date of the final exam is scheduled in the assessment calendar of the Faculty.
"The dates of evaluation activities cannot be modified, unless there is an exceptional and duly justified reason why an evaluation activity cannot be carried out. In thiscase, the degree coordinator will contact both the teaching staff and the affected student, and a new date will be scheduled within the same academic period to make up for the missed evaluation activity." Section 1 of Article 264. Calendar of evaluation activities (Academic Regulations UAB).
Students of the Faculty of Economics and Business, who in accordance with the previous paragraph need to change an evaluation activity date must process the request by filling out an Application for exams' reschedule: e-Formulari per a la reprogramació de proves.
Grade revision process
After all grading activities have ended, students will be informed of the date and way in which the course grades will be published. Students will be also be informed of the procedure, place, date and time of grade revision following University regulations.
Retake Process
"To be eligible to participate in the retake process, it is required for students to have been previously been evaluated for at least two thirds of the total evaluation activities of the subject." Section 2 of Article 261. The recovery (UAB Academic Regulations). Additionally, it is required that the student to have achieved an average grade of the subject greater than or equal to 3.5 and less than 5.
The date of the retake exam will be posted in the calendar of evaluation activities of the Faculty. Students who take this exam and pass, will get a grade of 5 for the subject. If the student does not pass the retake, the grade will remain unchanged, and hence, student will fail the course.
Irregularities in evaluation activities
In spite of other disciplinary measures deemed appropriate, and in accordance with current academic regulations, "in the case that the student makes any irregularity that could lead to a significant variation in the grade of an evaluation activity, it will be graded with a 0, regardless of the disciplinary process that can be instructed. In case of various irregularities occur in the evaluation of the same subject, the final grade of this subject will be 0". Section 11 of Article 266. Results of the evaluation. (UAB Academic Regulations).
Bibliography
- Stock,J.H. & Watson, M.M., Introduction to Econometrics. Pearson Education.
- Wooldridge, J. M., Introductory Econometrics: A Modern Approach. South-Western Cengage learning.
Software
The software used in this course is: RStudio and Gretl.
Language list
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PAUL) Classroom practices | 1 | Spanish | second semester | morning-mixed |
| (PAUL) Classroom practices | 2 | Catalan | second semester | morning-mixed |
| (PAUL) Classroom practices | 4 | English | second semester | morning-mixed |
| (PAUL) Classroom practices | 8 | English | second semester | morning-mixed |
| (PAUL) Classroom practices | 51 | Catalan | second semester | afternoon |
| (PAUL) Classroom practices | 52 | Catalan | second semester | afternoon |
| (PAUL) Classroom practices | 60 | Catalan | first semester | morning-mixed |
| (PLAB) Practical laboratories | 11 | Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 12 | Spanish | second semester | morning-mixed |
| (PLAB) Practical laboratories | 21 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 22 | Catalan | second semester | morning-mixed |
| (PLAB) Practical laboratories | 41 | English | second semester | morning-mixed |
| (PLAB) Practical laboratories | 42 | English | second semester | morning-mixed |
| (PLAB) Practical laboratories | 81 | English | second semester | morning-mixed |
| (PLAB) Practical laboratories | 82 | English | second semester | morning-mixed |
| (PLAB) Practical laboratories | 511 | Catalan | second semester | afternoon |
| (PLAB) Practical laboratories | 512 | Catalan | second semester | afternoon |
| (PLAB) Practical laboratories | 521 | Catalan | second semester | afternoon |
| (PLAB) Practical laboratories | 522 | Catalan | second semester | afternoon |
| (PLAB) Practical laboratories | 601 | Catalan | first semester | morning-mixed |
| (PLAB) Practical laboratories | 602 | Catalan | first semester | morning-mixed |
| (TE) Theory | 1 | Spanish | second semester | morning-mixed |
| (TE) Theory | 2 | Catalan | second semester | morning-mixed |
| (TE) Theory | 4 | English | second semester | morning-mixed |
| (TE) Theory | 8 | English | second semester | morning-mixed |
| (TE) Theory | 51 | Catalan | second semester | afternoon |
| (TE) Theory | 52 | Catalan | second semester | afternoon |
| (TE) Theory | 60 | Catalan | first semester | morning-mixed |