Skip to content.

UPV/EHU

Sections
Personal tools

Course guide

  • Core Syllabus

  • To provide a basic-to-intermediate-level introduction to the theory and practice of Econometrics.

  • Objectives

  • The General Linear Regression Model will be introduced and discussed in detail so that students taking the course are able to use a regression model to solve a simple economic problem both from a theoretical as well as from a practical point of view. More specifically, at the outset students will be capable of using analytical tools and computer software in order to quantify and validate economic relationships by way of the estimation and testing of a relevant econometric model fitted to the available data.

  • Competences

    1. To analyse critically the basic elements of Econometrics in order to understand the logic of econometric modelling and be able to specify causal relationships among economic variables.
    2. To identify the relevant statistical sources in order to be able to search for, organise and systematically arrange available economic data.
    3. To use with confidence appropriate statistical methods and available computing tools in order to correctly estimate and validate econometric models.
    4. To handle econometric prediction tools in order to estimate unknown or future values of an economic variable.
    5. To interpret adequately the results obtained in order to be able to write meaningful reports about the behaviour of economic data.

  • Prerequisites

  • An elementary knowledge of linear algebra and calculus (Maths I, II and III or equivalent) and of basic statistical theory (Elements of Probability & Statistics and Business Statistics or equivalent) will be useful.

  • Contents

  • 1.Introduction to Econometrics
    1.1. Elements of Econometrics.
    1.2. Concept of Model. Economic Model vs Econometric Model. Example.
    1.3. The Econometric Model. The error term.
    1.4. Stages in the elaboration of an Econometric Model.
    Basic Reference: AFG: c1.
    Other references: Wooldridge: c1.Ramanathan: c1.Gujarati: c1, c2.
    2.The Linear Regression Model (I). Specification and Estimation.
    2.1. General Linear Regression Model (GLRM): Specification and assumptions.
    2.2. Method of Ordinary Least Squares (OLS). Normal Equations.
    2.3. Properties of the Sample Regression Function.
    2.4. Goodness of Fit: the Coefficient of Determination (R2). Estimation of the error variance.
    2.5. Finite-sample Properties of the OLS Estimator. The Gauss-Markov Theorem.
    2.7. Specification problems: omission of relevant variables and multicollinearity.
    2.8. The Least-Squares Estimator under Restrictions.
    Basic Reference: AFG: c2, c3, c6.
    Other references: Wooldridge: c2, c3. Ramanathan: c3, c4, c5. Gujarati: c3, c4, c6, c8, c9, c13. Exercises: AFG: Appendices B, C, D: 2.1 to 3.23 and 6.45 to 7.67.
    3.The Linear Regression Model (II). Inference and Prediction.
    3.1. The Least-Squares Estimator under Normality.
    3.2. Single and Multiple Significance Tests. Confidence Intervals.
    3.3. General Tests of Linear Restrictions.
    3.4. Tests based on the Residual Sum of Squares.
    3.5. Point and Interval Prediction.
    Basic Reference: AFG: c4, c5.
    Other references: Wooldridge: c4, c6.Ramanathan: c3, c4.Gujarati: c5, c7, c8, c15. Exercises: AFG: Appendices B, C, D: 4.24 to 5.44.
    4.Dummy Variables
    4.1. Dummy Variables. Definition and use in the GLRM.
    4.2. Seasonal effects.
    4.3. Interaction with explanatory variables.
    Basic Reference: AFG: c7.
    Other references: Wooldridge: c7. Ramanathan: c7. Gujarati: c15. Exercises: AFG: Appendices B, C, D: 6.45 to 7.67.

  • Schedule

  • Teaching methodology

    • Classroom lectures, classroom exercises and computer lab classes. A subject page set up with the moodle teaching support platform will be used to organise the course, store and made available the teaching materials used throughout the course (contents, timetable, exercises, handouts, data, etc.), schedule the course activities and assignments and moderate a discussion forum and a course wiki among other things.
    • Teaching Material: Projection Slides, Lecture Notes and Handouts.
    • Computer lab classes and practical exercises will be carried out with the help of gretl open-source econometrics software.
    • Written work: Assignments and practical exercises will be provided. Students may be expected to hand in written answers to set problems and assignments.

  • Assesment method

  • All the competences will be evaluated through a continuous evaluation process and the total mark will be obtained as follows:
    • Active participation plus individual resolution of activities proposed during practical & computer laboratory classes and self-evaluation: up to 20%
    • Execution and presentation of a project: up to 25%
    • Written test: up to 55%