Bibliographic record
Abstract
Purpose The purpose of this paper is to derive the real implications of inflation targeting using optimizing models characterized by endogenous time preference. Design/methodology/approach To ensure consistent consumption and savings behavior, the rate of time preference is modeled as an increasing function of real wealth. Findings The results are not uniform and depend on the methods for modeling money in the general equilibrium framework; money in the utility function (MIU) and cash‐in‐advance constraints (CIA). With MIU, time preference wealth effects link the monetary and real sectors by endogenizing real interest rate. Monetary growth raises steady state capital and consumption by the Tobin effect. However, if money is introduced through CIA constraints, inflation policies are sensitive to the structure of the constraint itself. If the constraint applies to consumption and capital purchases, monetary growth lowers the steady state demand for both commodities and reverses the Tobin effect. If the constraint applies only to consumption goods, the same monetary policy is superneutral. This time preference specification has important advantages. It is consistent with the literature that integrates reinforcing wealth effects into aggregative models using ad‐hoc consumption or savings functions. Allowing the rate of time preference to depend positively on real wealth implies that optimizing behavior, not ad‐hoc specification yields wealth effects that endogenize the real interest rate and generate a Tobin effect. This time preference specification provides optimizing foundations for modeling savings as a decreasing function of real wealth, which is empirically verifiable and consistent with empirical predictions of consumption as an increasing function of real wealth. Originality/value This paper demonstrates the different effects that monetary policy maintains on steady state capital, consumption and real balance holdings in economies characterized by an endogenous rate of time preference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".