Evaluating the Unconventional Monetary Policy of the Bank of Japan: A DSGE Approach
Bibliographic record
Abstract
When the nominal interest rate reaches the zero lower bound (ZLB), a conventional monetary policy, namely, the adjustment of short-term interest rate, may become impractical and ineffective for central banks. Therefore, quantitative easing (QE) is one of the few available policy options of central banks for stimulating the economy and dealing with deflationary pressure. Since February 1999, the Bank of Japan (BoJ) has conducted several unconventional monetary policy programs. Considering the scarce research in this field from a structural macroeconomic model approach, a medium-scale New Keynesian DSGE model with government bonds of different maturities was developed to check the portfolio rebalancing channel of quantitative qualitative easing (QQE) conducted by the BoJ from April 2013 on the basis of the assumption of imperfect asset substitutability. The model was calibrated on the basis of the structure of the Japanese economy in April 2013. The main conclusion is that the BoJ’s asset purchase has a real effect on pushing output and inflation higher, and long-term interest rates lower. Sensitivity simulation analysis shows that, given the same size of asset purchase, the persistence of asset purchase determines the peak effect in the short run. A long-lasting asset purchase can push up inflation higher, and long-term interest rates lower for a relatively longer period, but the long-run effect on output and investment does not have much difference. The policy implication for BoJ is just to announce a long-lasting QE program and make it credible to the market.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".