Investment and the Real Interest Rate in Business Cycle Models
Bibliographic record
Abstract
A pervasive prediction of business cycle models is that investment by …rms in durable goods (capital, inventories) is highly sensitive to ‡uctuations in real interest rates (Thomas 2002, House 2007, Kryvtsov and Midrigan 2008). This prediction stands in sharp contrast with the data: investment is virtually insensitive to movements in interest rates. We ask: can an inventory-theoretic model of cash management by …rms account for the low sensitivity of investment to interest rates in the data? In the model, a cash-in-advance constraint and frictions in the asset market lead …rms to hold positive inventories of cash (low interest bearing liquid assets). These cash holdings disconnect the user cost of durable goods from the real interest rate and have the potential of reconciling the model’s predictions with the data. We investigate this hypothesis by calibrating the model to account for patterns of cash holdings and investment in the Compustat …rm-level data. We then study the model’s aggregate implications. JEL classi…cations: E31, E32, E43.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".