Exchange Rate Pass-Through Effects on Food and Cereal Inflation in Morocco: An Asymmetric Analysis Under Climate Change Constraints Using an ARDL Model
Bibliographic record
Abstract
This study examines the determinants of food price inflation in Morocco using a comprehensive econometric framework based on an Autoregressive Distributed Lag (ARDL) model. Relying on monthly data and controlling for major structural shocks, the analysis captures both the short-run dynamics and long-run equilibrium relationships between food prices and key macroeconomic, external, and climatic variables. The estimation results reveal strong inflation inertia, indicating that past food prices are the most significant driver of current price changes. External cost variables, including the nominal effective exchange rate, world oil prices, and international cereal prices, are mostly insignificant in the short run, suggesting a muted and delayed pass-through. Import volumes exert a marginal but lagged effect, while rainfall emerges as a consistent determinant, highlighting Morocco’s structural vulnerability to climatic variability. The error-correction term is negative and significant, confirming the existence of a stable long-run relationship. Long-run estimates show that oil prices and precipitation remain relevant drivers of food price dynamics, whereas the exchange rate appears largely neutral, reflecting the impact of subsidies, managed exchange rate arrangements, and domestic supply-chain characteristics. Nonlinear NARDL estimations provide no evidence of asymmetric exchange rate pass-through. The findings underscore some policy recommendations to enhance agricultural resilience, strengthen climate adaptation, and improve supply-chain efficiency for food price stability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".