Bibliographic record
Abstract
A popular way to describe the business cycle is as a movement between distinct phases of expansion and recession. During expansions, output growth and employment are relatively high, whereas recessions are characterized by sluggish output growth and high unemployment. Hamilton (1989) used regime-switching models to describe this evolution of the business cycle. In this dissertation, I extend the model of Hamilton (1989) to address a number of prevalent macroeconomic questions regarding business cycle comovement. First, in joint work with Neville Francis and Michael T. Owyang, we assess the leading role played by U.S. in the global economy by analyzing if U.S. output growth informs the timing of business cycle turning points of other nations. We find that the U.S. economic growth influences both the timing and duration of business cycle phases for Canada, Germany, the United Kingdom, and, to a lesser extent, Mexico. Conversely, we find no relationship between U.S. output growth and the business cycles of France, Italy, and Japan. In the second paper, again with Neville Francis and Michael T. Owyang, we study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find three groups of countries which experience idiosyncratic recessions relative to the global business cycle. Additionally, we find the primary indicator of international recessions to be large movements in asset prices. In a third paper with James D. Hamilton and Michael T. Owyang, we extend Hamilton and Owyang (2012) which examined the comovements of state-level business cycles using a clustered Markov-switching approach. Here, we consider whether industries also comove and, if so, whether this comovement is limited to subsectors within a single industry classification. We find four industry clusters, with their composition implying some degree of propagation of recessions up the production chain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".