Structural Estimation and Evaluation of Calvo-Style Inflation Models
Bibliographic record
Abstract
The authors structurally estimate and evaluate, for the U.S., the E.U., and Canada, various classes of recently-proposed Calvo-type models, using identification-robust methods. The models differ in their assumptions regarding price indexation (when firms cannot re-optimize their pices), in the way capital is used (homogenous or firm-specific), and in the elasicity of intermediate goods demand facing firms. Our approach is to obtain point and confidence-set structural parameter estimates, based on inverting identification-robust test statistics. Importantly, we maintain the focus on the structural aspect of the model, and formally impose the restrictions that map the theoretical model into the econometric one. In addition, we propose a test statistic that is invariant to the considered delay between the time firms re-optimize their prices and the time they implement these new prices. Results are as follows. For the U.S., we find no statistical support for the standard Calvo model. Instead, there is evidence in favour of a dynamic indexation model with firm-specific capital and an increasing elasicity of intermediate goods demand facing firms. For Canada, there is some support for a dynamic indexation Calvo model regardless of whether capital is assumed to be firm-specific or not, but only if no price implementation delays are present. For the E.U., the results are mixed. Overall, we also find that, in all cases, when firm-specific capital is assumed, results are almost identical whether adjustment costs are assumed to be zero or not. Second, outcomes are very different depending on the considered implementation delay. Third, except when allowing for uncertainty in the considered implementation delay, the uncertainty about the average frequency of price adjustment in the economy is dramatically large.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".