Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial
Bibliographic record
Abstract
In this paper, we analyze short-term forecasts of Turkish GDP growth using Mixed DAta Sampling (MIDAS) approach. We consider six alternatives for functional form of the lag polynomial in the MIDAS equation, five to twelve lags of the explanatory high frequency variables and produce short-term forecasts for nine forecast horizons starting with the release of data for six months before the start of the target quarter to the release of the data for the last month of the quarter. Our results indicate that functional form of the lag polynomials play non-negligible role on the short-term forecast performance but a specific functional form does not perform globally well for all forecast horizons, for all lag lengths or for all indicators. Import quantity indices perform relatively better until first month’s data for the target quarter become available. As data accumulate for the monthly indicators for the target quarter, real domestic turnover and industrial production indicators stand out in terms of short-term forecasting performance. When all of the three months’ realizations for the monthly indicators become available for the quarter that we want to forecast, unrestricted MIDAS type equations with around five lags with real domestic turnover and industrial production indicators track the GDP growth relatively successfully.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".