Statistical Methods for Social Sciences: Prevision Techniques

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Statistical Methods for Social Sciences: Prevision Techniques

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    International Studies, Statistics

  • Language Level

    Taught In English

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    Statistical Methods for Social Sciences: Prevision Techniques
    Bachelor in International Studies
    ECTS Credits: 6.0
    Semester: 2


    Nowadays, due to the development of the IT and massive access to data sources, quantitative analysis has become a basic tool in the sociological and economic research. Traditional qualitative analysis has been significantly enriched by the application of statistical methods that allow the processing of these data and facilitates the formulation and validation of hypotheses in the field of social sciences.

    However, these statistical methods have to be applied in such a way that they allow, not only to describe the social events that have already taken place, but also to anticipate what will be the future evolution of the analyzed data and how this evolution can be affected by the implementation of a particular policy. In addition, forecasting, as in any decision-making process, plays a crucial role in the implementation and evaluation of any socio-economic policy. It is therefore important that these predictions are not only based on a qualitative analysis. Instead they should be also supported by quantitative methods based on techniques of forecasting and econometric.

    Therefore, it is crucial in this context not only for the social researcher but also the legislator to become familiar with quantitative methods that are based on the forecasting techniques in order to be able to know in greater depth the evolution of the various socio-economic aspects and also to be able to quantify and assess what are the consequences of the policies that are implemented.

    This course is intended to address the problem of socio-economic forecasting, the study of the main methodologies for prediction and the analysis of the correct use of the predictions in decision-making.

    - Relevance of socio-economic forecasting and factors that determine the methods of prediction
    - Modeling and prediction with deterministic trend and seasonal structures
    - Prediction using time-series models
    - Prediction with dynamic regression models
    - Prediction with vectorial models
    - Models with integrated variables. Spurious regressions
    - Cointegration. Vector models with equilibrium correction mechanism (VEqCM)


    The course is composed by theoretical lectures where both blackboard and audiovisual media is used to present abstract concepts. In addition, there will be practical sessions in computer classrooms where students will learn the use of the software necessary to implement the models based on real data


    60% of the final grade will be obtained through a final exam. It will be necessary to get a grade of at least 5 points out of 10 to pass the course.
    40% remaining from the final grade corresponds to the continuous evaluation of the knowledge and skills acquired by the student at the theoretical level and in the resolution of practical problems and data analysis. This continuous evaluation will consist of two mid-term exams, each corresponds to 20% of the final grade.


    Dielbold. Elements of Forecasting. South-Western College Publishing, Cincinnati. 2004
    Espasa, A. and Cancelo, J.R.. Métodos Cuantitativos para el Análisis de la Coyuntura Económica . Alianza Editorial. 1993
    Gonzalez, C.W.J. Forecasting in Business and Economics. Pearson. 2013
    Granger, C.W.J. Forecasting in Business and Economics. Academic Press, San Diego. 1984
    Peña, D.. Análisis de Series Temporales. Alianza Editorial. 2005

Course Disclaimer

Please note that there are no beginning level Spanish courses offered in this program.

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.