Financial frictions and the zero lower bound on interest rates: a DSGE analysis
Bibliographic record
Abstract
Recent developments in Canada, the United Kingdom, the euro area, Japan, Sweden, Switzerland and the United States have triggered a debate on whether monetary policy is effective when the nominal interest rate is close to zero. In this context, the monetary authority is no longer in a position to pursue a policy of monetary easing by lowering nominal interest rates further. However, some economists have down-played the risk of hitting the zero lower bound, at least for the US economy. In this paper, I assess the implications of the zero lower bound in a DSGE model with financial frictions. The financial accelerator mechanism is formalized as in Bernanke, Gertler and Gilchrist (1995). The paper attempts to address three main issues. First, I evaluate whether the zero lower bound -- by limiting the use of the nominal interest rate as a policy instrument -- might hamper the monetary authority from offsetting the negative effects of an adverse shock. Second, I analyze whether price-level targeting, through the stabilization of private sector expectations, might be a better monetary rule than inflation targeting in order to avoid the "liquidity trap". Third, I investigate the effectiveness of fiscal stimulus (namely, an increase in government expenditure) when financial markets are imperfect and the nominal interest rate is close to its zero lower bound. In this context, two questions will be addressed: first, do financial frictions weaken the effect of a fiscal expansion? Second, how are results affected when the zero lower bound is binding? To address these questions, I introduce a negative demand shock and an adverse financial shock. I find that by adopting a price-level targeting rule, the monetary authority might alleviate the recession generated by the interaction of financial frictions and lower-bounded nominal interest rates. Alternatively, an increase in government expenditure has a positive impact on output, but fiscal multipliers are below one, due to a strong crowding-out effect of private consumption. This effect is muted when the nominal interest rate is lower bounded. In analyzing discretionary fiscal policy, this paper does also focus on two crucial aspects: the duration of the fiscal stimulus and the presence of implementation lags.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".