Factor IV Estimation in Conditional Moment Models with an Application to Inflation Dynamics
Bibliographic record
Abstract
Abstract In a conditional moment model, we develop a new integrated conditional moment (ICM) estimator which directly exploits factor-based conditional moment restrictions without having to first parametrize, or estimate such restrictions. We focus on a time series framework where the large number of available instruments and associated lags is driven by a relatively small number of unobserved factors. We build on the ICM principle originally proposed by Bierens (1982) and combine it with information reduction methods to handle the large number of potential instruments which may exceed the sample size. Under the maintained validity of the true factors, but not that of observed instruments, and standard regularity assumptions, our estimator is consistent, asymptotically normally distributed, and easy to compute. In our simulation studies, we document its reliability and power in cases where the underlying relationship between the endogenous variables and the instruments may be heterogeneous, non-linear, or even unstable over time. Our estimation of the New Keynesian Phillips curve with U.S. data reveals that forward- and backward-looking behaviors are quantitatively equally as important, while the driver’s role is nil.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".