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
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Journal:
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_openOpen access editor

Abstract

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.

Bibliographic References

  • Addison, P. S., The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance (CRC Press, 2017).
  • Aguiar-Conraria, L. and Soares, M.J., Oil and the macroeconomy: using wavelets to analyse old issues, Empirical Economics 40 (2011) 645-655.
  • Tiwari, A. K., Oil prices and the macroeconomy reconsideration for Germany: Using continuous wavelet, Economic Modelling 30 (2013) 636-642.
  • Gallegati, M., Wavelet analysis of stock returns and aggregate economic activity, Computational Statistics & Data Analysis 52(6) (2008) 3061-3074.
  • 5. González-Concepción, C., Gil-Fariña, M. C. and Pestano-Gabino, C., Wavelet power spectrum and cross-coherency of Spanish economic variables, Empirical Economics, (2017) 1-28, https://doi.org/10.1007/s00181-017-1295-5.
  • MATLAB 7.0, Software The Language of Technical Computing (The MathWorks, 2011).
  • Aguiar-Conraria, L. and Soares, M.J., The Continuous Wavelet Transform: A Primer, https://repositorium.sdum.uminho.pt/bitstream/1822/12398/4/NIPE_WP_16_2011.pdf, NIPE WP 16 (2011)
  • 8. Baumeister, Christiane; K., L., Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us", The Journal of Economic Perspectives 30 (1) (2016) 139–160. doi:10.1257/jep.30.1.139.
  • Bekiros, S., Nguyen, D. K., Junior, L. S., & Uddin, G. S., Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets, European Journal of Operational Research 256(3) (2017) 945-961.
  • 10. Álvarez, L. J., Hurtado, S., Sánchez, I. and Thomas, C., The impact of oil price changes on Spanish and euro area consumer price inflation, Economic Modelling 28(1) (2011) 422-431. https://doi.org/10.1007/s00181-017-1295-5 https://repositorium.sdum.uminho.pt/bitstream/1822/12398/4/NIPE_WP_16_2011.pdf
  • Mingming, T. and Jinliang, Z., A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices, Journal of Economics and Business, 64(4) (2012) 275-286.
  • Pulido San Román, A. La predicción en Economía: Posibilidades y Limitaciones, Estudios de Economía Aplicada, 35(2) (2017) 215-228.