Constructing a Coincident Economic Indicator for India: How Well Does It Track Gross Domestic Product?
Bibliographic record
Abstract
In India, the first official estimate of quarterly gross domestic product (GDP) is released approximately 7–8 weeks after the end of the reference quarter. To provide an early estimate of current quarter GDP growth, we construct Coincident Economic Indicators for India (CEIIs) using a sequentially expanding list of 6, 9, and 12 high-frequency indicators. These indicators represent various sectors, display high contemporaneous correlation with GDP, and track GDP turning points well. CEII-6 includes domestic economic activity indicators, while CEII-9 incorporates indicators of trade and services and CEII-12 adds financial indicators in the model. We include a financial block in CEII-12 to reflect the growing influence of the financial sector on economic activity. CEIIs are estimated using a dynamic factor model which extracts a common trend underlying the high-frequency indicators. The extracted trend provides a real-time assessment of the state of the economy and helps identify sectors contributing to economic fluctuations. Furthermore, GDP nowcasts using CEIIs show considerable gains in both in-sample and out-of-sample accuracy. In particular, we observe that our GDP growth nowcast closely tracks the recent slowdown in the Indian economy.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".