Bibliographic record
Abstract
In this paper we present a generalized sticky price model which allows, depending on the parameterization, for demand shocks to maintain strong expansionary effects even in the presence of perfectly flexible prices. The model is constructed to incorporate the standard three-equation New Keynesian model as a special case. We refer to the parameterizations where demand shocks have expansionary effects regardless of the degree of price stickiness as Real Keynesian parameterizations. We use the model to show how the effects of monetary policy–for the same degree of price stickiness–differ depending whether the model parameters are within the Real Keynesian subset or not. In particular, we show that in the Real Keynesian subset, the effect of a monetary policy that tries to counter demand shocks creates the opposite tradeoff between inflation and output variability than under more traditional parameterizations. Moreover, we show that under the Real Keynesian parameterization neo-Fisherian effects emerge even though the equilibrium remains unique. We then estimate our extended sticky price model on U.S. data to see whether estimated parameters tend to fall within the Real Keynesian subset or whether they are more in line with the parameterization generally assumed in the New Keynesian literature. In passage, we use the model to justify a new SVAR procedure that offers a simple presentation of the data features which help identify the key parameters of the model. The main finding from our multiple estimations, and many robustness checks is that the data point to model parameters that fall within the Real Keynesian subset as opposed to a New Keynesian subset. We discuss both how a Real Keynesian parametrization offers an explanation to puzzles associated with joint behavior of inflation and employment during the zero lower bound period and during the Great Moderation period, how it potentially changes the challenge faced by monetary policy if authorities want to achieve price stability and favor employment stability.
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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) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".