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Record W4296707610 · doi:10.1111/roie.12639

Partial dollarization and financial frictions in emerging economies

2022· article· en· W4296707610 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of International Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversity of Victoria
FundersEconomic and Social Research CouncilBritish Academy
KeywordsEconomicsEmerging marketsStylized factVolatility (finance)CurrencyMonetary economicsDynamic stochastic general equilibriumSmall open economyFinancial acceleratorStructural estimationMacroeconomicsExchange rateMonetary policyFinanceEconometrics

Abstract

fetched live from OpenAlex

Abstract How do financial frictions affect macroeconomic volatility and monetary policy in emerging market economies? This article assesses the empirical relevance of such frictions by estimating a two‐bloc emerging market/rest‐of‐the‐world model containing two key features of emerging economies: partial transaction and liability dollarization, and financial frictions where capital financing is partially or totally in foreign currency. Our estimation employs the “one‐step approach” which allows us to be “agnostic” regarding nonstationarity in the data and simultaneously estimate structural and trend parameters. Using data for Peru and the US, we find substantial empirical support for both the financial accelerator and partial dollarization mechanisms. The data fit of the baseline model improves with the addition of each of these frictions, exogenous shocks are significantly amplified in their presence and our preferred model captures several important stylized facts of a small emerging open economy.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.732

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)0.0010.000

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 head0.016
GPT teacher head0.239
Teacher spread0.223 · 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