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Record W2151043792

Understanding Movements in Aggregate and Product-Level Real-Exchange Rates ∗

2008· article· en· W2151043792 on OpenAlexaboutno aff
Ariel Burstein, Nir Jaimovich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasing power parityRelative priceEconomicsExchange rateCompetitor analysisProduct (mathematics)Monetary economicsFinancial economicsLiberian dollarPoint (geometry)International economicsEconometricsFinance
DOInot available

Abstract

fetched live from OpenAlex

We document new facts on international relative price movements using wholesale price data for common products sold in Canada and United States over the period 2004-2006, and information on the country of production for individual products. We find that international relative prices at the level of individual products are roughly three to four times as volatile as the Canada-US nominal exchange rate at quarterly frequencies. Aggregate real-exchange rates, constructed by averaging movements in international relative prices for individual goods, closely follow the appreciation of the Canadian dollar over this period. These patterns hold both for matched products that are locally produced in each country, as well as for goods that are produced in one country and traded to other countries. The large movements in international relative prices for traded goods are in conflict with the hypothesis of relative purchasing power parity, but instead point to the practice of pricing-to-market by exporters. In light of these findings, we construct a model of international trade and pricingto-market that can account for the observed movements in product- and aggregate real-exchange rates for both traded and non-traded products. The international border plays a key role in accounting for our pricing facts by segmenting competitors across countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.353
GPT teacher head0.248
Teacher spread0.106 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations120
Published2008
Admission routes1
Has abstractyes

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