Trends, Cycles and Seasonal Variations of Ukrainian Gross Domestic Product
Bibliographic record
Abstract
The article attempts to study trends, seasonal variations and cyclical fluctuations of Ukraine’s quarterly GDP at current prices. The period of the study is from the first quarter of 2010 to the first quarter of 2020. The methodological support of the study includes an approach based on the Hamilton regression filter, the Hodrick-Prescott filter and the asymmetric filter model of Cristiano and Fitzgerald. Based on the use of a Hamilton regression filter, which clearly gives one complete cycle with a peak and a depression, the study substantiates that the seasonally adjusted series of GDP has a slight difference with the remainder, but its seasonal fluctuations are homogeneous and have the shape of the letter V, which allowed us to draw the following conclusions: seasonal fluctuations in GDP are confirmed by the ACF and PACF models during the study period; the filter is very different from the Hamilton filter in terms of trend and cycle, but has common features in the context of asymmetry in time with the random walk filter of Cristiano and Fitzgerald. The paper substantiates the conclusions about stable and stationary series of GDP by volatility (leading to a decrease) of cyclical fluctuations based on the used forecast model ARIMA (4,0,4) for 2020-2030, which passed through the Hamilton regression filter. Based on the results of the study, the author provides recommendations on the need to introduce a new monetary and fiscal policy, including reform measures, which should be balanced with current trends in the functioning and development of international financial institutions and organizations. Such changes will be a motivating lever for the growth of the share of agriculture and related activities, production, transport, real estate, capital formation and other macroeconomic indicators of Ukraine’s economy, respectively, during the period of GDP decline. Keywords: Gross Domestic Product, decomposition, trends, cyclical fluctuations, seasonal variations, Hamilton Filter, Hodrick-Prescott Filter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".