title: Economic Cycle Analysis Using Numerical Methods - Research by Jaouad Karfali
title: A New Methodology for Predicting Economic Cycles through Annual Data Transformation
titleType: Other
Creators: name: Karfali, Jaouad
Publisher: name: Zenodo
Year: 2025
Subjects: subject: (4-(m-Chlorophenylcarbamoyloxy)-2-butynyl)trimethylammonium Chloride/adverse effects
subject: (4-(m-Chlorophenylcarbamoyloxy)-2-butynyl)trimethylammonium Chloride/analysis
subject: (4-(m-Chlorophenylcarbamoyloxy)-2-butynyl)trimethylammonium Chloride/classification
subject: (4-(m-Chlorophenylcarbamoyloxy)-2-butynyl)trimethylammonium Chloride/economics
subject: (4-(m-Chlorophenylcarbamoyloxy)-2-butynyl)trimethylammonium Chloride/history

description: This methodology was tested against traditional economic models such as ARIMA and VAR, showing an accuracy of up to 82% in predicting economic cycles, compared to 68% for the ARIMA model and 72% for the VAR model.

The research provides a comparison with forecasts from international institutions such as the International Monetary Fund (IMF) and the World Bank. For example, for the year 2025 (which is transformed to the number 9), the research predicts a likelihood of economic contraction, while institutions like the IMF predict a 3.2% growth. This contradiction highlights the importance of the new methodology in offering insights that may not be captured by traditional models.
description: The research covers the period from 1927 to 2025, analyzing major economic cycles such as the Great Depression (1929) and the Global Financial Crisis (2008) using this methodology.

The research employs advanced statistical tests such as Ordinary Least Squares (OLS) regression analysis and Granger Causality tests to verify the validity of the results. Additionally, Monte Carlo Simulation was used to compare the outcomes with random cycles, where Karfali's methodology showed an accuracy of 82%, compared to 33% for random cycles.

 
description: Although the research has not yet been cited in academic circles (as of February 2025), it provides new insights that could be valuable for economic policies and future predictions.

The research employs advanced statistical tests such as Ordinary Least Squares (OLS) regression analysis and Granger Causality tests to verify the validity of the results. Additionally, Monte Carlo Simulation was used to compare the outcomes with random cycles, where Karfali's methodology showed an accuracy of 82%, compared to 33% for random cycles.
description: "The research is distinguished by integrating multiple economic variables such as Gross Domestic Product (GDP), unemployment rates, inflation, interest rates, and global trade volume in the analysis of economic cycles. This integration helps enhance the accuracy of predictions, as the model is tested against historical data from reliable sources such as the U.S. Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS).

Example: In the 1927–1935 cycle, the transformed figures were linked to major economic events like the Great Depression, where GDP dropped by 26.7% and unemployment reached 24.9%.
description:  

"This research represents an update to the previously published study on Zenodo under the DOI number 10.5281/zenodo.14788479. While the original research focuses on [Original Research Topic], this new work expands the scope to include a comparative analysis between Numerical Analysis and Traditional Methods in cycle analysis. By adding new methodologies and practical applications, this research provides further insights into how analytical accuracy can be improved in complex and changing data environments
description: A revised version of this research has been published on SSRN, including additional analyses and updates that enhance the accuracy of the predictions and provide a broader comparison with traditional economic models and forecasts from international institutions. You can access the new version of the research through the following links:

https://ssrn.com/author=7429208

https://papers.ssrn.com/abstract=5160775

https://data.mendeley.com/datasets/wv7dcm5834/1
description: The Karfali-VAR Model provides a practical framework for predicting economic trends and financial crises. Its real-world applications demonstrate its value in risk management and policy planning. We invite you to explore its practical use and support the independent researcher in further developing the methodology. Join us for an engaging discussion on its implications and future developments.

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