Repository logo
Communities & Collections
All of DSpace
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Gómez Bermeo, Kevin Renato"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Results Per Page
  • Sort Options
  • Loading...
    Thumbnail Image
    Item
    Aplicación del análisis espectral de series de tiempo a los ciclos económicos de Ecuador periodo 1900-2018
    (Universidad de Cuenca, 2024-03-25) Gómez Bermeo, Kevin Renato; Sarmiento Jara, Juan Pablo
    The present article aims to identify the frequencies of economic cycles in Ecuador and demonstrate whether these cycles align with theories of long, medium, and short-term economic cycles. The analysis utilizes the Maddison Project's database, covering the years 1900 to 2018. To achieve this, various filters are first applied to extract the cyclical component of the GDP series. Subsequently, time series spectral analysis methodology is employed to identify the frequencies that constitute the country's economic cycle. Finally, the forecast quality of the frequency-domain characterized model is compared against other alternatives. The results of spectral analysis suggest that Ecuador's economic cycles exhibit frequencies consistent with Kondratieff, Kuznets, Juglar, and Kitchin theories. Additionally, Kondratieff cycles seem to start with a delay of approximately ten years compared to literature suggestions. On the other hand, Kuznets cycles align with the long booms and crises in Ecuador's economic history, such as the banana and oil booms. Juglar and Kitchin cycles are found to be closely related to international commodity market conditions. Finally, when comparing the forecasting quality of the spectral analysis model with various ARIMA models, it is found that ARIMA models provide the most accurate predictions for the economic cycle in the years 2014- 2018.

DSpace software copyright © 2002-2026 LYRASIS

  • Privacy policy
  • End User Agreement
  • Send Feedback