Bibliographic record
Abstract
This thesis studies the relationship between business cycles, banks, and money. The first chapter focuses on the relationship between bank credit supply and economic volatility. I build a DSGE model with nominal and financial frictions. The novel aspect of the model is the banking sector which finances its lending activity with both pre-accumulated household savings and deposits that are created—ex nihilo—by the banking sector. I contribute to the literature by studying the relative importance of bank credit supply shocks in driving economic volatility. The model is estimated using US data. There are three main results of this chapter. First, I find that an unexpected contraction in the supply of bank credit leads to an economic recession. Second, bank credit supply shocks account for 55% of the volatility in investment and GDP. Third, the model can replicate two stylized facts previous models failed to capture: bank net worth is sticky and bank leverage is procyclical. In the second chapter I focus on the Canadian banking sector. The first half of the chapter provides a brief historical account of the development of the banking system in Canada and makes comparisons with the development of the US banking system. The second half of the chapter uses detailed bank-level balance sheet data of the Big Five Canadian banks to tease out six stylized facts of the Canadian banking sector. They are: first, it is dominated by five large banks; second, credit is deepening; third, net worth is ‘sticky’; fourth, leverage is procyclical; fifth, balance sheet risk is falling; and sixth, rising bank output is driven by liquid liabilities. In the third chapter I estimate a bank credit supply shock using both firm and bank qualitative survey data regarding lending/borrowing conditions in the Canadian economy. The survey data is purged of factors which also affect loan demand. Using a monetary VAR model, I find that an unexpected one-standard-deviation contraction of bank credit supply leads to a mild economic recession. Moreover, I find that the results are robust to a variety of specifications and a different identification strategy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".