Explaining and Forecasting Inflation in Emerging Markets: The Case of Mexico
Bibliographic record
Abstract
The authors apply existing inflation models that have worked well in industrialized countries to Mexico, an emerging market that has recently moved to adopt an inflation-targeting framework for monetary policy. They compare the performance of these models with a mark-up model that has been used extensively to analyze inflation in Mexico. The authors focus on three models that have some theoretical foundations and that can therefore help explain the causes of inflation as well as be used for forecasting purposes: a mark-up model, a money-gap model, and a Phillips curve. The authors' empirical results suggest that the evolution of the exchange rate remains a very important factor for forecasting inflation in Mexico. Indeed, in the best-performing model, the mark-up model, the exchange rate plays the most significant role. The Phillips curve explains and forecasts inflation well when using actual values for the explanatory variables, but does not perform well when using forecasted values for the explanatory variables. The money-gap model does not appear to be useful in its current form, because it is unable to beat even a simple AR1.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".