Comparison of ARIMA and ARIMA Error Regression Models: Evidence from the Russian Consumer Price Index
Bibliographic record
Abstract
Consumer Price Index (CPI) is regarded as a common approach for measuring inflation. The present study examines the Russia CPI in the context of political and social upheavals, especially under the wars and the COVID-19. Due to the unstable political situation, the inflation rate in Russia sharply grow in 2014 and 2022 which creates two of the largest increase over the past decade after the Crimean war and the Ukrainian war were announced, thus it is crutial to make both long-term and short-term trends prediction. The paper aims to choose the best fitting model to estimate the future value of Russia monthly CPI data, and ultimately provides a suggestion and reference for monetary and fiscal authorities in Russia when making the policies to reduce inflation risk. The study applies the ARIMA (Autoregressive Integrated Moving Average) model to forecast Russia CPI, and uses regression with ARIMA errors to analyze the Russia CPI according to the ARIMA fitted values of the crude oil price in Europe. According to the result, ARIMA (2,3,1) is the best fitting model that can make comparatively sensible prediction for the future values.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".