Bibliographic record
Abstract
Chapter 1.—This chapter addresses model specification uncertainty using the Bayesian Generalized Method of Moments (GMM). Employing Canadian data, I estimate 64 hybrid New Keynesian models which differ in their lag specification, and use a modified GMM quadratic function to produce model posteriors. I compute optimal discretionary policies for each model and then derive a posterior-weighted policy and loss. My results show that i) policy should respond more to the output gap than inflation, ii) a more aggressive policy is prescribed for the period of stagflation in the 1970s and early 1980s and iii) a relatively light-touch policy is recommended during the Great Moderation, and produces better outcomes. This last result supports the hypothesis of ‘good luck’ over ‘good policy’. Chapter 2.—In this chapter I develop an inverse control procedure to recover the under- lying preferences of a monetary authority engaged in discretionary policymaking. I adjoin the first-order condition (FOC) of the optimal interest rate rule-setting derived under discretion to the usual least squares moment conditions during the GMM procedure. Using Monte Carlo simulations, I show that the preferences on output gap stabilization and interest rate smoothing may be recovered. Robustness reveals that recovering the preference on the output gap is dependent upon policy actions having sufficient effect on the macroeconomy. Further testing indicates that the procedure functions for alternative starting values, may be adapted to different lag specifications of the underlying model, and is able to recover different sets of policy preferences. Chapter 3.—This chapter tests the hypothesis that the monetary authorities of Canada, the United States and the United Kingdom have exhibited similar preferences over stabilizing the output gap and smoothing the interest rate, by way of an inverse control algorithm (FOC- based GMM) for a discretionary policymaker. For the sample period covering 1968:1-2006:4, the FOC-based provides comparable structural estimates to a benchmark specification using an instrument-based GMM. The data suggest no role for output stabilization in any country, but a large and significant concern for interest rate smoothing is observed in Canada. Measures of fit reject optimality in the United States for baseline specification sample, but do not preclude it in any country when sample periods are restricted to the current man- dates. Policymakers’ reaction functions are shown to be sensitive to the underlying policy preferences, though decreasingly so at high levels of interest rate smoothing. Robustness is seen with respect to starting values and fixed policy coefficients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".