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Record W2587586230 · doi:10.5539/res.v9n1p209

Holding Cash and Spontaneous Behavior: A Modification of the Baumol Equation

2017· article· en· W2587586230 on OpenAlexvenueno aff
Limor Dina Gonen, Michal Weber, Tchai Tavor, Uriel Spiegel

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsnot available
Fundersnot available
KeywordsCashLiquidity trapEconomicsInterest rateMarket liquidityElement (criminal law)Monetary policyPurchasingMonetary economicsValue (mathematics)MacroeconomicsLiquidity riskComputer scienceLawOperations management

Abstract

fetched live from OpenAlex

During the decades following the presentation of the original Baumol equation (1952) the role of holding cash was significantly changed. The original Baumol equation considered the two elements of (i) the value of transactions, positively affecting cash holding; and (ii) the interest rate, negatively affecting cash holding. A third element that was not considered is the economic behavioral aspect of the availability of money that may lead to spontaneous purchasing. This element reduces the inclination of customers towards holding cash.The present paper develops various kinds of loss functions due to spontaneous purchasing behavior and presents several different modified Baumol equations that are more reliable and realistic than the original Baumol equation.An important implication of our paper relates to the ineffectiveness of monetary policy. When the interest rate is very low, in the original Baumol model we approach the liquidity trap range in which monetary policy is ineffective. However, according to our new model the monetary policy still remains effective, even at low or zero interest rates. This is the case even in an environment in which the monetary policy seems to be totally inefficient, as we recently find in several industrial countries throughout the world. In some sense, this reminds us of the idea of an automatic stabilizer that supports fiscal policies. The new modified Baumol equation in the current paper reveals an automatic stabilizer which accelerates the effectiveness of monetary policy, and avoids the phenomenon of the liquidity trap, even in cases of zero interest rates.

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)0.001
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 score0.890
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.196
GPT teacher head0.315
Teacher spread0.119 · 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 designTheoretical or conceptual
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

Citations1
Published2017
Admission routes1
Has abstractyes

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