Testing The Pricing-To-Market Hypothesis Case Of The Transportation Equipment Industry
Bibliographic record
Abstract
Most of the evidence in favor of pricing-to-market (PTM) was obtained by estimating partial equilibrium models using OLS, instrumental variable (IV) and single-equation error-correction methods. However, we know from the recent econometric literature that Wald tests applied to some of these estimates may give erroneous results in the presence of endogeneity and weak instruments. In this paper we examine the reliability of the evidence supporting the hypothesis of pricing-to-market using LIML-based LR Monte Carlo tests. These tests, developed by Dufour and Khalaf (1998), have good power and, unlike the Wald test, also have the correct test size.We first estimate a typical PTM model by OLS and subject certain regressors to a test for exogeneity which does not depend on the "quality" of instruments used. Since the null is rejected, we then re-estimate the model by both IV and limited information maximum likelihood methods. Subsequently, we apply Wald and LR-based tests to the parameters of interest to examine the hypothesis of PTM. We find that the size-correct Monte Carlo LR-based test reverses half of the results obtained from the popular Wald test indicating that PTM may not be as widespread as previously believed. In addition, our results support the viewpoint suggesting that PTM behavior is likely to be present in the same industry across different countries and that pass-through is possibly higher with a larger market share of exports.The above findings are illustrated using the model developed by Marston (1990) and our analysis is conducted for export pricing firms in the transportation equipment industry for three country pairs: Canada exporting to the United States, the United States exporting to Canada, and Japan exporting to (mainly) the United States.
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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 | 0.019 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".