Nominal Income Targeting in an Open-Economy Optimizing Model
Bibliographic record
Abstract
The purpose of this paper is to examine the merits of monetary policy rules that utillize as their princip target variable the level or growth rate of some aggregate measure of nominal spending, such as nominal GDP, rahter than a monetary aggregate or an index of inflatoin (either alone or in combination with some measure of the output gap). Considerable academic support for nominal spending targets has existed since the early 1980s, and therefore predates the upsurge of interest in inflation targeting that began in the early 1990s with the adoption of inflation targeting by the central banks of New Zealand, Canada, the United Kingdom, and Sweden. In our discussion we shall adopt the term "nominal income targeting" because of its widespread usage, although it does not most accuratley reflect the logic of the approach, according to the discussion below. Also, we shall use the word "targeting" in the manner familiar from the existing literature, rather than in the more tightly defined sense suggested by Svensson (1997a) and Rudebusch and Svensson (1998). That is, we shall use the term "X-targeting" when the central bank sets its instrument in response to a rule that refers to deviations from a desired path for the variable X, whereas Rudebusch and Svensson would call this "responding to the variable X," andwould reserve the term "target" for variables appearing in the central bank's objective function. Because there is a large and rich literature on nominal income targeting (briefly, NIT), we began in Section 2 with a short review of existing arguments in its favor. Then in Section 3 we present some evidence which suggests that NIT is in effect utilized in practice in the United States. Our paper's main objective, however, is to develop new results concerning the possible desirability of NIT in the context of a quantitative structural macroeconomic model that represents an improved and extended version of the semi-classical framework presented in McCallum and Nelson (1998). Toward that end, aggregate demand and aggregate supply specifications are developed in Section 4 and 5. Both of these sections feature modification designed to make the model one that depicts an economy open to trade and capital flows. In addition, our new demand specification incorporates habit-formation features that increase its ability to match aggregate U.S. data at the quarterly frequency. The model is summarized and log-linearized in Section 6. Calibration of the model, based on properties of quarterly data for the United States, is undertaken in Section 7. The main simulation exercises are finally reported in Section 8 and their implied messages are summarized in Section 9.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".