Identifying economic cycles in Spain using wavelet functionsoil price, industrial production and consumer price indices

  1. Concepción Nieves González Concepción 1
  2. María Candelaria Gil Fariña 1
  3. Celina Pestano Gabino 1
  1. 1 Universidad de La Laguna

    Universidad de La Laguna

    San Cristobal de La Laguna, España


Rect@: Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA

ISSN: 1575-605X

Year of publication: 2018

Volume: 19

Issue: 1

Pages: 1-16

Type: Article

DOI: 10.24309/RECTA.2018.19.1.01 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Rect@: Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA


Cited by

  • Scopus Cited by: 0 (21-01-2023)

SCImago Journal Rank

  • Year 2018
  • SJR Journal Impact: 0.101
  • Best Quartile: Q4
  • Area: Economics and Econometrics Quartile: Q4 Rank in area: 629/702
  • Area: Applied Mathematics Quartile: Q4 Rank in area: 533/596

Índice Dialnet de Revistas

  • Year 2018
  • Journal Impact: 0.060
  • Field: ECONOMÍA Quartile: C3 Rank in field: 106/171


  • Social Sciences: C

Scopus CiteScore

  • Year 2018
  • CiteScore of the Journal : 0.1
  • Area: Economics and Econometrics Percentile: 5
  • Area: Applied Mathematics Percentile: 3


This paper analyses the economic cycles in Spain over a long period of time according to available data by using three related variables: Oil Price (1946M1-2015M9), Industrial Production Index (1993M2-2015M9) and Consumer Price Index (1961M1-2015M9). The impact of shocks on oil price has been the subject of an extensive study, although modelling their effects is not a trivial undertaking. Our contribution focuses on applying the Morlet Wavelets to identify the presence of unstable cycles in data series by calculating the Wavelet Power Spectrum with the MATLAB software. Moreover, some bivariate techniques are applied to display the mutual influence of the Oil Price with the Industrial Production Index and the Consumer Price Index. The Cross Wavelet Coherency and the relationship among phases can also be used to detect co-movements and potential causality relationships in frequency bands over time. Finally, by studying these variables we can draw certain comparative conclusions with the US and German economies, whose corresponding variables have been considered by other authors using this same tool.

Funding information

This research was funded in part by Spanish Ministry of Education and Science (MTM2012-38163-C06-01 and MTM2015-71352-P).


  • Ministry of Education and Science Spain
    • MTM2012-38163-C06-01
    • MTM2015-71352-P

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