Determining co-movements of tomato prices in the United States and macroeconomic variables in Mexico for 2023
Bibliographic record
Abstract
Objective: To analyze the co-movements of macroeconomic variables in Mexico and prices of Mexican tomato exports and to estimate the prices of Mexican tomatoes in American and Canadian supply markets based on Mexican macroeconomic variables. Design/Methodology/Approach: The research was conducted using Pearson's coefficient—calculating the standard scores for X and Y. We determined the co-movements of Mexican tomato market prices and Mexico’s GDP, the Interbank Equilibrium Interest Rate (IEIR), natural gas prices, and consumer inflation. Econometric techniques were thus combined with agricultural sector variables as a reliable precedent of the relation intensity between said variables. Results: The coefficient of determination showed an acceptable degree of linear relationship between the market prices of Mexican tomatoes in different cities and the selected macroeconomic variables, with an average correlation of 20%. We concluded that the variables are not entirely independent since they show a weak linear relationship between them. Study limitations/implications: It is crucial to conduct studies to determine whether the coefficients of determination support linearity or independence between the evaluated macroeconomic variables. Findings/Conclusions: Econometric techniques were combined with agricultural sector variables as a reliable precedent of the relation intensity between said variables. The coefficient of determination showed an acceptable degree of linear relationship between the market prices of tomatoes in different cities and the selected macroeconomic variables. We recommend the creation of a price forecasting model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".