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Record W1501602225 · doi:10.3386/w12564

Endogenous Sudden Stops in a Business Cycle Model with Collateral Constraints:A Fisherian Deflation of Tobin's Q

2006· report· en· W1501602225 on OpenAlexaff
Enrique G. Mendoza

Bibliographic record

VenueNational Bureau of Economic Research · 2006
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsWestern University
Fundersnot available
KeywordsDeflationBusiness cycleCollateralEconomicsKeynesian economicsMonetary economicsMathematical economicsMonetary policyFinance

Abstract

fetched live from OpenAlex

The current account reversals, large recessions, and price collapses that define Sudden Stops contradict the predictions of a large class of models in which the current account is a vehicle for consumption smoothing and investment financing. This paper shows that the quantitative predictions of a business cycle model with collateral constraints are consistent with the key features of Sudden Stops. Standard shocks to imported input prices, the world interest rate, and productivity trigger collateral constraints on debt and working capital when borrowing levels are high relative to asset values, and these high-leverage states are endogenous outcomes. In these situations, Irving Fisher's debt-deflation mechanism causes Sudden Stops as the deflation of Tobin's Q leads to a spiraling decline in the prices and holdings of collateral assets. This has immediate effects on output and factor demands because collapsing collateral values cut access to working capital. In contrast with previous findings, collateral constraints induce significant amplification in the responses of macroaggregates to shocks. Because of precautionary saving, Sudden Stops are infrequent events nested within normal cycles in the long run, but they remain a positive probability event.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.468
GPT teacher head0.413
Teacher spread0.055 · 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.

Study designSimulation or modeling
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

Citations66
Published2006
Admission routes1
Has abstractyes

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