Universitat Internacional de Catalunya

## Econometric Management Techniques

**Main language of instruction:**Catalan

**Other languages of instruction:**English, Spanish

If the student is enrolled for the English track then classes for that subject will be taught in the same language.

### Teaching staff

**Tuesdays** 13:00-14:00 or **Thursdays** 16:00-17:00. **Location**: Edifici Terré, D-D001. **Important**: please send an email in advance in order to confirm your meeting with the instructor (mflores@uic.es).

### Introduction

This is an introductory econometrics course for undergraduates. Its main focus is to introduce the multiple linear regression model (MLRM), which is the most widely used vehicle for empirical analysis in economics, business and other social sciences. MLRM can be used for estimating economic relationships, testing economic theories, and evaluating and implementing government and business policy. The emphasis in this course is on understanding and interpreting the assumptions of the MLRM in light of actual empirical applications. The course includes regular review sessions containing problems solving and problem sets using the econometrics software *Stata*. Problem sets will focus on applications in various fields including health economics, labor economics and business economics.

### Pre-course requirements

Basic knowledge of statistics, probability and matrix algebra is required. Students can refer to appendices A to D in Wooldridge’s “Introductory econometrics” textbook.

### Objectives

The main objective is to introduce the student to the basic concepts of econometrics and main steps that involve an applied econometric analysis (specification of the initial model, search of data, application of estimation techniques and statistical inference, interpretation of results and improvement of the model). The student is expected to learn how to correctly interpret and evaluate econometric results. This course should provide an adequate basis for expanding further in more advanced courses both the theoretical and applied econometric skills acquired by the student.

Additional:

To learn which are the most common issues within MLRMs (omission of relevant variables and other specification problems).

To apply computer tools to obtain estimates and test results in order to solve econometric problems.

### Competencies

- 19 - To analyse quantitative financial variables and take them into account when making decisions.
- 41 - To be able to descriptively summarise information.
- 42 - To be able to empirically analyse financial phenomena.
- 43 - To acquire skills for using statistical software.
- 44 - To be able to select appropriate econometric methods.
- 45 - To be able to work with academic papers.
- 50 - To acquire the ability to relate concepts, analyse and synthesise.
- 51 - To develop decision making skills.
- 52 - To develop interpersonal skills and the ability to work as part of a team.
- 53 - To acquire the skills necessary to learn autonomously.
- 54 - To be able to express one’s ideas and formulate arguments in a logical and coherent way, both verbally and in writing.
- 64 - To be able to plan and organise one's work.
- 65 - To acquire the ability to put knowledge into practice.
- 66 - To be able to retrieve and manage information.

### Learning outcomes

At the end of this course, students will have a basic knowledge of the econometrics software Stata and of linear regression analysis allowing them to design an econometric model, estimate it and interpet its estimation results.

Students will be able to understand empirical studies that use linear regression models.

Students will be able to use real data and linear regression analysis to make economic or business policy recommendations, and to help economic and managerial decision making.

### Syllabus

Lesson 1. Introduction.

1.1 Concept of Econometrics.

1.2 Economic and econometric models.

1.3 Elements of the model: relations, variables and parameters.

1.4 Limitations of Econometrics.

Lesson 2. The classical model of multiple linear regression: estimation.

2.1 Specification.

2.2 Basic Assumptions of multiple linear regression model standard.

2.3 Estimation by ordinary least squares (OLS). Statistical properties.

2.4 Analysis of the residuals and estimation of the variance of the disturbance term.

2.5 Measures of goodness of fit and model validation.

Lesson 3. The classical model of multiple linear regression: Hypothesis testing and prediction.

3.1 Formulation of hypotheses.

3.2 Comparison of linear constraints.

3.3 Restricted least squares estimation (MQR).

3.4 Comparison of individual and joint significance.

3.5 Point prediction and prediction interval.

3.6. Analysis of structural change. The Chow test.

Lesson 4. Regression models with the inclusion of qualitative variables.

4.1 Qualitative Variables and dummy variables.

4.2 Specifying dummy variables.

4.3 Including dummy variables in the MLRM and applications.

Lesson 5. Specification error and functional form misspecification.

5.1 Errors in the specification of the explanatory variables.

5.2 Problems associated with the omission of relevant variables.

5.3 Consecuences of the inclusion of irrelevant variables.

5.4 Errors in the specification of the functional form and random disturbance.

5.5 Consequences for OLS estimation: alternative estimators.

### Teaching and learning activities

#### In person

During the course the econometric content will be illustrated first theoretically and then implemented during practical sessions using an econometric software. Both methods will be used in class and also individually by the student for a better understanding of the contents.

### Evaluation systems and criteria

#### In person

**Continuous assessment (20% of the mark):**

**Exercises**will be collected, in some instances without prior notice**(10%)**. These exercises will have to be handed in at a particular date and time.**Final group work (20%)****.****Participation**and**adequate behavior**in class**(****10%****)**.

**Midterm exam (20% of the mark):**

- The partial exam will NOT exempt you from any lectures for the final exam.

**Final exam (60% of the mark)*:**

- The final exam will cover
**ALL**lectures. - To pass the course and for the continuous assessment to count,
**you must get at least a 5 in the final exam**. Otherwise, you will need to retake the exam in June

**Re-sit exam**

- If you fail the main exam, you will need to retake it in June. In this case, the final grade of the subject will depend 100% on the final examination of the re-sit exam. That is, the continuous assessment will NOT be taken into account.
- According with the internal rules of the Faculty, the
**maximum grade**you could get in the re-sit exam is a**7.**

**Observations**:

* To pass the course it is essential to get at least a 5 at the final exam of the subject, otherwise you must go directly to the re-sit exam. If the mark of the final exam is less than 5, the maximum total final mark for the subject will be 4,5 (even when accounting for the continuous assessment would lead to a mark greater than 5).

** COMPULSORY ASSISTANCE: In order to attend any exam (final or re-sit exam) you need to attend more than 75% of classes. If you miss more than 4 classes, then you will not be allowed to attend the exam. In this course the professor does not need to know the justification of your absence but you are only allowed to miss 4 classes. Exceptions occur with students that have professional sport competitions duties. In this case, please contact the professor.

### Bibliography and resources

Wooldridge, J.M. (2012). Introduction to Econometrics: A Modern Approach, South-Western College Publishing, 5th edition

Gujarati, D.N. (1997). Econometrics. 3 ª ed. McGraw-Hill.

Newbold, P., Carlson, W., & Thorne, B. (2012). Statistics for Business and Economics. Pearson Higher Ed.

Greene W.H. (2000). Econometric analysis. 3 rd edition. Prentice-Hall. New York.Artistic, M., J. Suriñach M. Clear, T. del Barrio and M. Guillen (1999). Introduction to econometrics. UOC.

Johnston, J and Dinardo, J. (2001). Econometric Methods. Vicens Vives.

Angrist, J.D. and Pischke, J-S. (2009) *Mostly Harmless Econometrics: An Empiricist's Companion.* Princeton University Press.