Following up on my previous posts about the Karfali-VAR model and its economic cycle analysis, with its hybrid methodology (30% cycle / 70% VAR), I want to emphasize that the model's discovery of a robust 9-year economic cycle, and its prediction of a potential slowdown or contraction phase around 2025, was established based on historical data analysis (since 1954) using strong statistical tools (like spectral analysis, Augmented Dickey-Fuller test with P=0.01, Granger causality test with P=0.03, plus out-of-sample testing) before the specific global economic frictions and events we see today escalated.
This prediction was not merely an abstract statistical result but was based on recurring patterns observed directly in the historical economic data. This data showed a repeated tendency towards slowing economic growth, manufacturing sector weakness (with the ISM index often nearing or falling below 50), and the appearance of other characteristic pressures of the late stages of the cycle.
Based only on recognizing these recurring historical rhythms, the methodology indicated that the 2024-2025 period represents a late and sensitive phase of the current cycle. Today, we observe that recent developments:
Heightened global discussions on trade policies and tariffs.
Notable volatility in commodity prices (like oil recently).
The ISM manufacturing index falling below 50, signaling contraction.
...strikingly align with the economic environment the model anticipated in advance, making the model serve as an analytical "warning" based on recurring patterns in the data.
ORCID :
https://orcid.org/0009-0002-9626-7289
(Statistical details, data, and charts are available via the application:
https://preview--cycle-insight-analyzer.lovable.app/ )
This alignment raises a question for discussion: Since the systematic analysis of these historical patterns enabled the model to signal potential economic weakness for the current period before the latest global events fully manifested, does this highlight the foresight value that can be gained by integrating statistically validated, medium-term cycle analysis into our economic monitoring and forecasting tools? Can these approaches offer critical foresight that complements short-term analyses? We invite your opinions on the value of integrating these cyclical approaches into economic monitoring and forecasting.
