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Predicting the Portuguese GDP Using Three Different Computational Techniques

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Information Systems and Technologies (WorldCIST 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 470))

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Abstract

In this work, we predict the Portuguese Gross Domestic Product using three different computational techniques: Principal Component Analysis, Confirmatory Factor Analysis, and Random Forest. We start by reviewing the literature on the topic and defining the variables motivated by the literature, and then study the viability of such models in our data set. The models are described and compared, and our conclusion is that all three models are viable, under certain assumptions and preprocessing of the data, although all have upsides and shortcomings.

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Acknowledgments

We gratefully acknowledge the financial support from FCT - Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant UIDB/04521/2020.

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Authors and Affiliations

  1. ISEG-UL - Lisbon School of Economics and Management, Univ. Lisboa, Lisbon, Portugal

    Vasco Capela Tavares, Joao d’Água, Guilherme Mendes & Eduardo Peso

  2. Advance/ISEG-UL - Lisbon School of Economics and Management, Univ. Lisboa, Lisbon, Portugal

    Carlos J. Costa

Authors
  1. Vasco Capela Tavares
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  2. Joao d’Água
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  3. Guilherme Mendes
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  4. Eduardo Peso
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  5. Carlos J. Costa
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Corresponding author

Correspondence to Vasco Capela Tavares .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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Tavares, V.C., d’Água, J., Mendes, G., Peso, E., Costa, C.J. (2022). Predicting the Portuguese GDP Using Three Different Computational Techniques. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 470. Springer, Cham. https://doi.org/10.1007/978-3-031-04829-6_46

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