Measuring Macroeconomic Conditions
Bibliographic record
Abstract
This dissertation focuses on three measurement questions that are central to the design of macroeconomic policy. The first chapter, which is joint work with Thomas Laubach and John C. Williams, introduces time-varying volatility and incorporates a persistent supply shock to the Holston-Laubach-Williams model of the natural rate of interest to address the extraordinary effects of the COVID-19 pandemic on the economy. We find no evidence that estimated natural rates of interest in the United States, Canada, and the Euro Area have increased from their historically low pre-pandemic levels. In the context of our model, the main consequence from the pandemic period was a reduction in estimated natural rates of output. The second chapter explores estimation of the natural rate of output across unobserved components models that vary in terms of their labor market, output, and interest rate dynamics. Estimates of the output gap in the United States from similar models diverge dramatically: differences exceed 5 percentage points and paint a markedly different picture of macroeconomic conditions not just during the COVID-19 pandemic, but also over the entire fifteen-year period following the global financial crisis. This dispersion translates to differences in prescribed policy rates from simple monetary policy rules and acts as a source of uncertainty for central bankers. In the third chapter, I provide new evidence that banking crises commence throughout the business cycle: a large share of crises do not follow the canonical boom-bust timeline. I document differences in the start dates of banking crises across twelve databases, covering 467 episodes in 143 countries, and explore implications for the measurement of the macroeconomic aftermath of banking crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.071 |
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; both teacher heads agree on what is shown here.
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".