Course guide of Econometrics (2351133)

Having acquired the following knowledge from the Basic Training Module:

• Mathematics

• Statistics

• Economics

In the case of using AI tools for the development of the subject, the student must adopt an ethical and responsible use of them. The recommendations contained in the document “Recommendations for the Use of Artificial Intelligence at UGR” published at the following location must be followed:
https://ceprud.ugr.es/formacion-tic/inteligencia-artificial/recomendaciones-ia#contenido0

Introduction. The Role of Econometrics
The Classical Linear Regression Model. Assumptions
Inference and Prediction
Multicollinearity
Heteroskedasticity
Autocorrelation
Discrete Choice Models
Models with Lagged Variables
Panel Data Models. Fixed Effects. Random Effects
Simultaneous Equations Models. Identification and Estimation Methods
Nonlinear Models. Linear Approximations. Optimization Algorithms

KNOWLEDGE OR CONTENT

C03 – Analyzes and understands economic reality, identifies the role played by businesses and the State within the economy, and applies instrumental techniques and tools to solve economic problems and real-life situations.

SKILLS OR ABILITIES

HD01 – Applies appropriate techniques to achieve correct oral and written communication in Spanish. Effectively conveys information, ideas, and solutions to posed problems.
HD02 – Assesses the situation and expected evolution of a company based on relevant information records and produces reports and diagnoses on specific situations in companies and markets, or makes decisions based on the gathered information, offering well-reasoned and concise solutions.
HD03 – Designs, develops, implements, and manages projects effectively, coordinating resources and processes to achieve successful results and meet specific objectives. Maintains optimal performance and makes effective decisions under high-pressure and demanding situations.
HD04 – Organizes, plans, and controls overall management or the different functional areas of a company, as well as other public and private organizations, and is capable of designing the organizational structure.
HD06 – Uses quantitative tools for calculations, diagnostics, and analysis. Models business situations.

TEACHING METHODOLOGY
MD01 - In-person classroom teaching

The student will know / understand:

• The regression technique when it comes to quantifying the existing relationships between economic magnitudes.

• How to express an economic proposition in a regression equation.

• The different available estimation methods, as well as the properties of these estimations.

• The validity of the results obtained from econometric models, depending on how well the underlying assumptions fit the type of problem being analyzed.

The student will be able to:

• Estimate the parameters of a linear regression model.

• Validate linear hypotheses about the propositions regarding the parameters proposed by theoretical models.

• Make predictions about the future values of the dependent variables, assessing their reliability.

1. Introduction to Econometrics
Econometrics and econometric models.
Stages of the econometric method and components of an econometric model.
Course Guides
Academic Year: 2025 / 2026
Nature of the information used in Econometrics.

2. The Linear Model I
Model assumptions.
Estimation of model parameters using ordinary least squares.
Properties.
Goodness of fit: coefficients of determination and Akaike and Schwarz criteria.

3. The Linear Model II
Estimation of model parameters using confidence intervals.
Hypothesis testing about the model parameters.
Model application.

4. Multicollinearity
Concept, causes, and consequences.
Procedures for detecting multicollinearity in the sample.
Solutions to the problem of multicollinearity.

5. Heteroscedasticity
Concept, causes, and consequences.
Detection procedures: Goldfeld-Quandt, Breusch-Pagan, and Glejser tests.
Estimation of models with heteroscedasticity.

6. Autocorrelation
Concept, causes, and consequences.
Detection procedures: Durbin’s h test, Durbin-Watson test, and Ljung-Box test.
Estimation of models with autocorrelated disturbances.

BASIC BIBLIOGRAPHY
Alonso, A.; Fernández, J. and Gallastegui, I. (2005). Econometrics. Prentice Hall.
Caridad, J.M. (1998). Econometrics: Single-Equation Econometric Models. Reverté S.A.
Fernández-Sánchez, P.; Salmerón-Gómez R. and Blanco, V. (2016). Econometrics Practices with Excel, Gretl and RStudio. Fleming.
García, R.M.; Herrerías, J.M. and Palacios, F. (2017). Econometrics. Solved Exercises. Pirámide Editions.
Greene, W. (1999). Econometric Analysis. Prentice Hall.
Guisan, M.C. (1997). Econometrics. McGraw Hill.
Gujarati, D. (2010). Econometrics. McGraw Hill.
Johnston, J. (1987). Econometrics. McGraw Hill.
Johnston, J. and Dinardo, J. (2001). Econometric Methods. Vicens-Vives.
Maddala, G.S. (2001). Econometrics. McGraw Hill.
Martín, G.; Labeaga, J.M. and Mochón, F. (1997). Introduction to Econometrics. Prentice Hall.
Novales, A. (2000). Econometrics. 2nd Ed. McGraw Hill.
Palacios, F., García, R.M. and Herrerías, J.M. (2011). Econometrics Exercises 1. Pirámide Editions.

Guides
Course: 2025 / 2026

TEACHING METHODOLOGY
MD01 - In-person classroom teaching.

SUPPLEMENTARY BIBLIOGRAPHY
Aznar, A.; García, A. and Martín, A. (1994). Econometrics Exercises I. Pirámide Editions.
Fernández, A.I. et al. (1995). Econometrics Exercises. McGraw Hill.
González, S. (Coordinator) (2007). Solved Exercises in Econometrics. Multiple Regression Model. Delta Publications.
Hernández, J. (1989). Econometrics Exercises. ESIC Editions.
Pena, J.B. et al. (1999). One Hundred Econometrics Exercises. Pirámide Editions.
Pérez, T.; Amoros, P. and Relloso, S. (1993). Business Econometrics Exercises. McGraw Hill.

Pindyck, R.S. and Rubinfeld, D.L. (2001). Econometrics, Models and Forecasts. McGraw Hill.
Pulido, A. and Pérez, J. (2001). Econometric Models. Pirámide Editions.
Schmidt, S.J. (2005). Econometrics. McGraw Hill.
Sánchez, C. (1999). Econometric Methods. Ariel Economics. Barcelona.
Stewart, M.B. and Wallis, K.F. (1984). Introduction to Econometrics. Alianza Universidad.
Stock, J.H. and Watson, M.M. (2012). Introduction to Econometrics, 3rd Ed. Pearson.
Wooldridge, J.M. (2010). Introduction to Econometrics. A Modern Approach. 2nd Ed. Thomson

• MD01. Docencia presencial en el aula 
• MD02. Estudio individualizado del alumno, búsqueda, consulta y tratamiento de información, resolución de problemas y casos prácticos, y realización de trabajos y exposiciones. 
• MD03. Tutorías individuales y/o colectivas y evaluación  

• MD01. Face-to-face teaching in the classroom.

Article 17 of the Evaluation and Grading Regulations of the University of Granada states that the ordinary call will preferably be based on continuous assessment, except for students who have been granted the right to a single final evaluation.

The overall grade for the ordinary call will correspond to the weighted score of the different aspects and activities that make up the evaluation system:

1. Written test (weighted 70% of the final grade). This test will have theoretical and practical parts.

2. Continuous assessment (weighted 30% of the final grade). This assessment will be based on one or more of the following:

• Attendance and/or participation in class.

• Theoretical work (multiple choice questions, etc.), and/or practical work (solving exercises, etc.) and/or computer-based assignments positively evaluated.

• Observation scales based on practical classes and/or computer use.

Students who do not attend the written test in the ordinary call will be graded as "Not Present."
If deemed appropriate by the teaching staff, the test(s) may be conducted orally.

To pass the subject, students must obtain a minimum score of five points (on a scale from zero to ten) in the written tests and, additionally, achieve at least 35% in both theory and practice sections. If these minimum scores are not met, the final grade will be the minimum between 4 and the average score obtained in the evaluation system of the respective call.

In the extraordinary call, the student's grade will correspond to:
Written test (weighted 100% of the final grade). This test may include several of the following parts:

• Theoretical

• Practical

• Practical with computer (solving exercises or questions in the computer lab)

If applicable, this test will be conducted after the written test, possibly on subsequent days if space or scheduling issues arise.
If deemed appropriate by the teaching staff, the test(s) may be conducted orally.

To pass the subject, students must obtain a minimum score of five points (on a scale from zero to ten) in the written tests and, additionally, achieve at least 35% in both theory and practice sections. If these minimum scores are not met, the final grade will be the minimum between 4 and the average score obtained in the evaluation system of the respective call.

Article 8 of the Evaluation and Grading Regulations of the University of Granada states that students who cannot comply with the continuous assessment method for justified reasons may opt for the single final evaluation. The test for these students will take place on the same date as the written test for ordinary call students.

The grade in the single final evaluation will correspond to the following tests:

1. Written test (weighted 70% of the final grade), including theoretical and practical parts.

2. Written test (weighted 30% of the final grade), which may include one or more of the following parts:

• Theoretical

• Practical

• Practical with computer (solving exercises or questions in the computer lab)

If applicable, this test will be conducted after the first written test, possibly on subsequent days due to space or scheduling constraints.
If deemed appropriate by the teaching staff, the test(s) may be conducted orally.

To pass, students must obtain a minimum of five points on the single final evaluation and at least 35% in both theory and practice sections. If these minimum scores are not met, the final grade will be the minimum between 4 and the average obtained on the single final evaluation. Students who do not attend the single final evaluation after being granted this option will be graded as "Not Present."

You may consult the Evaluation and Grading Regulations of the University of Granada, the instructions for applying these regulations, and the instruction related to article 8.2 of the Regulations.

Información de interés para estudiantado con discapacidad y/o Necesidades Específicas de Apoyo Educativo (NEAE): Gestión de servicios y apoyos (https://ve.ugr.es/servicios/atencion-social/estudiantes-con-discapacidad).

Gretl Software
R Software
RStudio Software
Python Software
Multimedia Guide for Developing an Econometric Model (GUIME)