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A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis

Author

Listed:
  • Carlos Poza
  • Manuel Monge

Abstract

The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS' forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization.

Suggested Citation

  • Carlos Poza & Manuel Monge, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, CEPII research center, issue 163, pages 163-175.
    • Handle: RePEc:cii:cepiie:2020-q3-163-12
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    File URL: https://www.sciencedirect.com/science/article/pii/S2110701719302008
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    Citations

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    Cited by:

    1. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
    2. Monge, Manuel & Poza, Carlos & Borgia, Sofía, 2022. "A proposal of a suspicion of tax fraud indicator based on Google trends to foresee Spanish tax revenues," International Economics, Elsevier, vol. 169(C), pages 1-12.
    3. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    4. Monge, Manuel & Romero Rojo, María Fátima & Gil-Alana, Luis Alberiko, 2023. "The impact of geopolitical risk on the behavior of oil prices and freight rates," Energy, Elsevier, vol. 269(C).
    5. Monge, Manuel & Lazcano, Ana & Parada, José Luis, 2023. "Growth vs value investing: Persistence and time trend before and after COVID-19," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Gutierrez-Lythgoe, Antonio, 2023. "Redes y autoempleo: Evidencia con datos de Facebook [Networks and self-employment: Evidence from Facebook data]," MPRA Paper 116656, University Library of Munich, Germany.
    7. Monge, Manuel & Gil-Alana, Luis Alberiko, 2021. "Spatial crude oil production divergence and crude oil price behaviour in the United States," Energy, Elsevier, vol. 232(C).

    More about this item

    Keywords

    Leading economic indicators; Business cycle; Google trends; Fractional cointegration VAR; Wavelet analysis;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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