Bibliographic record
Abstract
In this thesis I study a major structural change in the US economy, namely, the consequences of the Great Moderation on the US economy and the operation of monetary policy.My first chapter explores a variety of empirical relationships on lead-lag properties of the US business cycle and how these relationships have changed since the onset of the Great Moderation.We emphasize four major changes in lead-lag properties.Since these relationships serve as a benchmark for many models of the business cycle, we examine if a variety of models can account for these changes.We find that they cannot.My second chapter provides an explanation for the first property in my first chapter, that the real interest rate switched from negatively leading the US business cycle to positively lagging.The explanation rests on the fact that uncertainty about the current state of the economy has become less severe since the onset of the Great Moderation.This allows policymakers to set monetary policy closer to their rule-based policy prescription under no uncertainty and reduce unintended monetary induced fluctuations.My third chapter explores business cycle asymmetry prior to and after the Great Moderation.I show that the business cycle has become more asymmetric since the onset of the Great Moderation with booms becoming smaller and busts staying relatively the same.I highlight that this type of asymmetry is consistent with a class of models which feature occasionally binding collateral constraints.i My fourth chapter is a contribution to New Keynesian (NK) models.In a standard NK model, monetary policy operates through the real interest rate channel.This channel has recently drawn some criticism when the model is extended to include capital accumulation.In this setting it is possible for the real interest rate to fall in response to a positive monetary policy shock, contradicting the intuition of the real interest rate channel.We show that result vanishes when frictions on the flow of investment are present, as in modern NK models.Under this framework the response of the real interest rate is always the same as the monetary policy shock.I thank my supervisor, Hashmat Khan, for his support and guidance throughout my PhD.I learned a great amount from him and always enjoyed our collaborations.He has always been supportive and enthusiastic about my work, which made my PhD a very enjoyable experience.I am looking forward to our future collaborations
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".