Estimating the Output Gap for Saudi Arabia
Bibliographic record
Abstract
The objective of this paper is to estimate annual potential output growth and the output gap for the Saudi economy over the period 1980 to 2015, looking at both total output and non-oil output. The focus on the latter is so that the progress in diversifying the economy might be examined and the possible impact of diversification on potential output might be measured. We use three methods for estimating potential output proposed in the macroeconomic literature. The methodologies include the Hodrick-Prescott filter, Kalman filter, and the production function approach. We compare the three over the entire sample and the last five years. Our findings suggest that the output gap (the difference between actual and potential output, as measured by real GDP) is positive on average over the entire period (i.e., actual output has on average exceeded potential); however, the gap has turned negative and has shrunk in recent years, as fiscal expenditures, particularly in infrastructure, have acted to better align actual and potential. Our analysis also indicated that growth in both potential GDP and total factor productivity have accelerated in the 2011-2015 period. In contrast, growth in these factors has slowed in many other countries, particularly the advanced economies. This better performance of the Saudi economy is possibly due to the development of a resilient financial sector in the Saudi 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".