Three Essays in Macroeconomics with a Focus on Confidence, Investment and the Business Cycle
Bibliographic record
Abstract
This thesis includes three essays on empirical macroeconomics.The first chapter studies how informative business confidence is for investment growth, using US business confidence survey data for 1955Q1-2016Q4.Our main findings are: (i) business confidence has predictive ability for investment growth; (ii) remarkably, business confidence has superior forecasting power, relative to conventional predictors, for investment downturns over 1-3 quarter forecast horizons and for the sign of investment growth over a 2-quarter forecast horizon; and (iii) exogenous shifts in business confidence reflect short-lived non-fundamental factors, consistent with the 'animal spirits' view of investment.Our findings have implications for improving investment forecasts, developing new business cycle models, and studying the role of social and psychological factors determining investment growth.The second chapter explores the hypothesis that consumer confidence drives household investment.We use a survey-based consumer confidence measure in structural VAR analysis to identify a confidence shock.Household investment increases and follows a persistent hump-shaped response after a positive confidence shock.The responses of total hours-worked and output are also highly persistent.Confidence First and foremost, I would like to express my sincere gratitude to my main advisor
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".