The role of endogenous capital depreciation rate in Dynamic Stochastic General Equilibrium models: Evidence from Canada
Bibliographic record
Abstract
This paper investigates the optimal behavior of the main real macroeconomic variables in a Dynamic Stochastic General Equilibrium (DSGE) framework augmented with a time-varying depreciation rate of capital stock and an endogenous production of maintenance goods. For this purpose I explicitly define a depreciation rate function which is positively related to the utilization rate of capital and inversely related to the ratio of maintenance to capital stock. Along the balanced growth path, the trend of the depreciation rate is driven by the steady state value of the investment-specific technology progress (IST). The Bayesian estimation exercises performed on the Canadian economy show that, in response to a positive shock on marginal efficiency of investment (MEI) which drives the economic business cycle, the model is able to generate co-movement in all the main real endogenous variables including consumption, maintenance and depreciation. The optimal paths are amplified with respect to the baseline model with a constant depreciation and no maintenance costs, and their convergence dynamics are delayed as a consequence of acceleration in depreciation through the obsolescence effect. The model also shows that, in response to a positive IST shock both depreciation and maintenance decline due to an increase in the average service life of existing capital. Finally, I include in the model a shock which affects the transformation process of final goods into maintenance goods, named the maintenance-specific technology progress (MST). In the short run, this shock is the key-driver of the growth in real maintenance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".