Estimating Quarterly GDP for Nepal: An Application of Chow-Lin Procedure
Bibliographic record
Abstract
Nepal started producing annual national income estimates following an internationally accepted system of 'National Account Systems' since the late1960s; and, accordingly, the annual GDP figures for Nepal are available for a longer period. However, the production and publication of quarterly GDP for public consumption have been both rare and occasional. This paper aims to bridge this gap by providing an estimate for quarterly GDP for the period of 1997/98 Q1 to 2017/18 Q4 following well established Chow-Lin procedure. The quarterly exports and government tax revenue are used to extrapolate the magnitude and movement of quarterly GDP. The results show a deterministic seasonal movement over the quarters. In particular, increased economic activities are observed in the second and fourth quarters while making a comparison on quarter to quarter basis. It is expected that this paper will partially fulfill the gap of unavailability of quarterly GDP figures in the public domain, and documents that the researcher may use suitable econometric exercise to obtain inter-temporal disaggregation of low-frequency data such as annual GDP into quarterly figures.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".