Modeling the Clustering Volatility of India’s Wholesale Price Index and the Factors Affecting It
Bibliographic record
Abstract
This paper proposes to examine the clustering volatility of India’s Wholesale Price Index throughout the period 1960 to 2014 by applying the ARCH (1) and GARCH (1) model. The pre-conditional requirement for the computation of ARCH (1, 1) required us to perform several other tests i.e. Dickey Fuller, Ordinary Least Squared Regression and post OLS tests for investigating the ARCH effect in the first difference of WPI. The statistical analysis reveals a p-value of 0.569 for the GARCH mean model which is not significant at ∂ 0.05 to explain that the previous period’s volatility can influence the WPI. The coefficient of WPI at first difference exhibits a value of less than 1 which is nice in magnitude with a p-value of 0.005 for ARCH at ∂ 0.05 which is significant to explain the volatility of the WPI. The diagnostic test of autocorrelation in the residuals reveals that the residuals are white noise by exhibiting a corresponding probability value of 0.3757. Since, the overarching objective of this paper is to examine the clustering volatility of the aforementioned variable with regards to the internal shocks, there might have been other factors of external shocks on WPI that have deliberately been overlooked in this paper.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".