Estimating the Armington elasticity: The importance of study design and publication bias
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The FAT-PET-PEESE approach implicitly assumes that publication selection bias is linearly proportional to the size of the standard error, which might not be practical in some cases.
“as pointed out by Bajzik et al. (2020) and Zigraiova et al. (2021), the FAT-PET-PEESE approach implicitly assumes that publication selection bias is linearly proportional to the size of the standard error, which might not be practical in some cases.”
From Economic development and the finance–growth nexus: a meta-analytic approach (2024) · cites this paper as Bajzik et al. (2020)
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- Authors
- Bajzik, J., Havranek, T., Irsova, Z., & Schwarz, J.
- Venue
- Journal of International Economics
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 127
- Pages
- 103383
- ISSN
- 0022-1996 (ISSN-L)
- OpenAlex
- W3082305249
- Cited by
- 150
- References
- 141
- Citations by year
- 2026: 28 · 2025: 18 · 2024: 26 · 2023: 32 · 2022: 20 · 2021: 14 · 2020: 9 · 2019: 1 · 2016: 1 · 2015: 1
- FWCI
- 30.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of International Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Univerzita Karlova v Praze (PRIMUS/17/HUM/16)
- Grantová Agentura České Republiky (18-02513S, 19-26812X, 21-09231S)
- Topics
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Global trade and economics
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Monetary Policy and Economic Impact
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economics of Agriculture and Food Markets
- Keywords
- Armington elasticity, elasticity of substitution, publication bias, research design, model averaging, data aggregation, data frequency, dataset size, study quality, heterogeneity of estimates, international trade
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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