Teaching plan for the course unit

 

 

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General information

 

Course unit name: Forecasting Methods

Course unit code: 363676

Academic year: 2025-2026

Coordinator: Jordi Pons Novell

Department: Department of Econometrics, Statistics and Applied Economics

Credits: 6

Single program: S

 

 

Estimated learning time

Total number of hours 150

 

Face-to-face and/or online activities

60

 

-  Lecture with practical component

Face-to-face

 

45

 

-  Problem-solving class

Face-to-face

 

15

Supervised project

40

Independent learning

50

 

 

Recommendations

 

Although the content of this subject can stand in isolation, students should have previously taken Statistics I and II (which are second-year subjects in the recommended four-year degree pathway). Students are recommended to take (or to have already taken) the course in Business Econometrics, as this will help them better understand and assimilate the concepts covered in the course dedicated to Forecasting Methods.

 

 

Competences / Learning outcomes to be gained during study

 

   -

To be able to use ICT in professional practice.

   -

To be able to make financial and business decisions, taking into account the current economic situation.

   -

To use basic quantitative methods and instruments to obtain and analyse company information and its socioeconomic environment, in accordance with the characteristics of the available information.

Learning objectives

 

Referring to knowledge

The general objective of this course is to outline some of the most commonly used methods for making predictions of economic time series solely on the strength of past information on the variable being studied.

By the end of the course students are able to:

1) Understand the different approaches that can be used to make predictions about an economic variable, clearly distinguishing between a causal approach and a univariate approach (central focus of the course).

2) Know how to choose the best forecasting method from a range of options and how to evaluate the predictive capacity of the selected method in the univariate analysis of time series.

3) Know how to obtain forecasts using the different forecasting methods classified as classical methods of time series analysis.

4) Know how to apply all of the phases that make up the Box-Jenkins forecasting methodology (stochastic analysis of time series).

 

5) Understand the significance of economic forecasts that have been made and evaluate their strengths and limitations.

Therefore, by the end of the course students should have the following abilities:

1) Capacity to apply the knowledge obtained in class to solve real problems.

2) Ability to tackle a real forecasting problem for a variable. Thus, they must demonstrate that they are able to:

2.1) Use Excel to forecast the variable using classical analysis methods.

2.2) Use Gretl software to forecast the variable with the Box–Jenkins methodology.

 

 

Teaching blocks

 

Block 1. Introduction

Unit 1. Introduction

1.1. Forecasting and economic and business decisions
1.2. Classification of prediction methods
1.3. Prediction and assessment of predictive capacity

Block 2. Classical time series methods

Unit 2. Deterministic models (I)

2.1. Definition of time series and components
2.2. Prediction using non-trend models
2.3. Prediction with trend models

Unit 3. Deterministic models (II)

3.1. Analysis of the seasonal component
3.2. Prediction with non-trend models and seasonal component
3.3. Prediction with trend models and seasonal component

Block 3. Stochastic time series methods: Box–Jenkins methodology

Unit 4. Stochastic methods for time series (I): basic concepts

4.1. Stochastic processes
4.2. Concepts of stationarity and ergodicity
4.3. Autocovariance and autocorrelation functions
4.4. Elementary models: white noise and random path

Unit 5. Stochastic models for time series (II): linear models

5.1. Autoregressive models (AR)
5.2. Moving average models (MA)
5.3. Mixed models (ARMA)
5.4. Non-stationary processes. Integrated models (ARIMA)
5.5. Seasonal models (SARIMA)

Unit 6. Stochastic models for time series (III): Box–Jenkins methodology

6.1. Identification of models 
6.2. Parameter estimation
6.3. Model validation
6.4. Point and interval prediction
6.5. Intervention analysis

 

 

Teaching methods and general organization

 

Since the learning objectives require a considerable amount of practical work, the general teaching methodology involves a combination of theoretical concepts and a specific focus on their practical applicability. Therefore, the following activities are undertaken:

— Face-to-face learning activities: these are carried out in the classroom with the lecturer. These include theoretical activities and theoretical activities with a practical component (with the whole group), as well as problem-solving sessions. The total number of hours for these activities is 60. To facilitate these activities, students have access to the material used by the lecturers to explain each topic via Virtual Campus before the sessions.

— Independent learning activities: these include problem-solving activities and computer sessions, as well as self-reflection on the knowledge and competences acquired. More specifically, in the case of the practical problems, students are expected to answer a series of questions derived from real economic data. To do so, students must use calculus and econometric programs, and by means of the calculations and results, apply the theoretical knowledge acquired during the course. The total number of hours devoted to these activities is 50.

— Tutored/directed activities: these consist of discussion of problems to clarify doubts and the monitoring of independent work from students. The total number of hours devoted to these activities is 40. Once the theoretical and practical contents of the subject have been acquired, the problem-solving activities, independent work and tutored activities contribute to achieving the competences to elaborate, analyse and interpret economic and business information across time and their evolution; to critically analyse theories and economic models; to know the sources and uses of economic and business information, and to use suitable computer resources for data treatment and analysis. Besides, independent learning develops the ability to use ICTs, essential for the professional development of students.

To facilitate the theoretical learning activities, which take place in the class, students have access to the material used by the lecturers to explain each topic via the Virtual Campus before the sessions. In this way, note-taking time is reduced and students can pay more attention to understanding the concepts being introduced.

Independent learning activities are carried out with a computer, in the student’s own time without the lecturer. All of the materials that students need to carry out these activities are available via the Virtual Campus. This material includes:
  — a general description of the practical activity;
  — information and basic data;
  — general guidelines that should be followed in the corresponding software;
  — a solution for each stage or phase of the activity.

Students in the English-language groups follow basically the same methods, although some changes may be introduced to adapt the methodology to their needs.

 

 

Official assessment of learning outcomes

 

Continuous assessment

To pass the continuous assessment for this subject, students must demonstrate the general competence acquired by achieving an overall pass grade for all of the compulsory activities and the final examination in any of the official examination sessions, according to the criteria described below.

Continuous assessment consists of two components:

a) Final examination: a written examination on the theoretical and practical aspects of the course. Students must demonstrate the knowledge and skills they have acquired and perfected during the course and through the continuous assessment activities. The examination is scheduled during the official examination periods established by the Academic Board and is sat on campus; this exam represents 60% of the final grade.

b) Continuous assessment activities to be submitted on the established dates and following the criteria specified by the teaching staff in the Virtual Campus. These activities are worth 40% of the final grade.

These activities must be completed in accordance with fixed guidelines (time and place). All of the assessed activities completed during the course (continuous assessment activities and final examination) are marked out of 10. Students pass the subject if they achieve a weighted final grade of at least 5 out of 10. Note, however, that students who do not achieve a mark of 3.5 out of 10 in the final examination are not eligible for the continuous mode of assessment and are awarded the mark obtained on this examination.

Repeat assessment consists of a final examination with the same structure as that of the single mode of assessment. It takes place on the date established for this purpose in the Faculty’s academic calendar, coinciding with the other undergraduate examinations scheduled for that semester. The date for repeat assessment examinations is established by the Academic Board.

 

Examination-based assessment

Students unable to commit to the demands of continuous assessment may opt for the single mode. To pass the subject, students must sit a single examination, worth 100% of the final grade. This examination takes place during the official examination period as established by the Academic Board. The written examination assesses the theoretical and practical aspects of the course. Students must demonstrate the knowledge, skills and competences they have acquired during the course.

To pass the subject, students must:

a) Submit a written request indicating that they wish to waive their right to continuous assessment. The deadline for submitting this request is the final examination date.

b) Obtain the minimum pass mark of 5 out of 10 in the final examination in one of the official examination periods.

Repeat assessment

Students who do not pass the continuous or single modes of assessment are entitled to repeat assessment, which consists of a global evaluation aimed at demonstrating they have achieved the desired learning outcomes and acquired the skills and competences set out in this course plan.

To pass the repeat assessment, students must obtain the minimum pass mark of 5 out of 10.

Students in the English-language group follow basically the same assessment procedure, although some changes may be introduced to adapt the methodology to their specific characteristics.

 

 

Reading and study resources

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Book

AZNAR, Antonio; TRÍVEZ, Francisco J. Métodos de predicción en economía (Volumen 1). Barcelona: Ariel, 1993.

Catāleg UB  Enllaç

DIEBOLD, Francis X., Elements of forecasting. New York: South-Western2007.

Catāleg UB  Enllaç

HYNDMAN, Rob; ATHANASOPOULOS, George. Forecasting. Principles and practice. Melboure: OTexts, 2021.

Recurs electrōnic extern  Enllaç

MARTÍN PLIEGO, Francisco Javier. Introducción a la estadística económica y empresarial: teoría y práctica. 3a ed. Madrid: Thomson, 2011.

Catāleg UB  Enllaç

PEÑA, Daniel. Análisis de Series Temporales. Madrid: Alianza Editorial, 2010.

Catāleg UB  Enllaç

PULIDO, Antonio; LÓPEZ, Ana María. Predicción y simulación aplicada a la economía y gestión de empresas. Madrid: Pirámide, 1999.

Catāleg UB  Enllaç

SPIEGELHALTER, David. El arte de la estadística. Madrid: Capitán Swing, 2023.

Catāleg UB  Enllaç

URIEL JIMENEZ, Ezequiel. Análisis de datos: series temporales y análisis multivariante. Madrid: AC, 1995.

Catāleg UB  Enllaç

URIEL JIMENEZ, Ezequiel; PEIRÓ, Amado. Introducción al análisis de series temporales. Madrid: AC, 2005.

Catāleg UB  Enllaç