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Record W2706931317

Macroeconomic Policy in a Liquidity Trap

2017· article· en· W2706931317 on OpenAlexaboutno aff
Gauti B. Eggertsson

Bibliographic record

VenueEconstor (Econstor) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLiquidity trapZero lower boundEconomicsInterest rateDeflationMarket liquidityNominal interest rateOrder (exchange)Keynesian economicsMonetary economicsMonetary policyMacroeconomicsReal interest rateLiquidity crisisFinance
DOInot available

Abstract

fetched live from OpenAlex

main focus of my research for nearly two decades has been macroeconomic policy during periods when the central bank has cut the short-term nominal interest rate to zero, periods that are often referred to as exhibiting a liquidity trap. In this summary, I describe my key conclusions. work can be divided quite neatly into four parts, roughly following the time line in which it was written. I highlight each phase of my research agenda and three generations of models which evolved along the way. While I focus primarily on my own research, I must acknowledge at the outset that many others have contributed to this research agenda. First-Generation Models My interest in the liquidity trap was triggered by events in Japan in the late 1990s. At that time, Japan suffered from subpar growth and deflation, and the short-term interest rate had collapsed to zero. If it could happen in Japan, it could happen here as well, and it seemed to me a first-order priority for those concerned with macroeconomic policy to understand those events. My first published work on this topic was written with my adviser, Michael Woodford. (1) Central to it was the idea that once a central bank is constrained by the zero lower bound (ZLB), it can still have an impact on the economy by giving markets about the evolution of future interest rates, rates that would prevail once the ZLB is no longer binding. For example, it could set explicit thresholds, saying that the interest rate will stay at zero until the price level or unemployment rate reaches a particular level, an idea we formalized in the paper. These results have received quite a bit of attention over the years, perhaps due to the fact that during the Great Recession the Federal Reserve used the analysis, and closely related work by other authors, as part of the rationale for its guidance policy once the ZLB became a concern. (2) Several other central banks--including the Bank of Canada, the European Central Bank, the Bank of Japan, and the Bank of England--utilized this research for similar policy purposes. Another important result was an irrelevance proposition, the idea that increasing the money supply at a zero interest rate has no effect on output or prices if it does not change expectations about future interest rates. Woodford and I further showed that it was irrelevant how this was done, that is, which assets the central bank bought in order to increase the money supply. This was a quite controversial proposition when reported, but one that has stood the test of time, with several central banks more than doubling the monetary base during the most recent crisis, using various purchasing schemes, with little or no apparent effect on prices. (3) This was consistent with the empirical prediction of that paper. It was a direct violation, however, of the quantity theory of money, which was a reigning paradigm in the '90s. A second major theme of my early work was how policies aimed at manipulating expectations, such as forward guidance, could be made credible. Specifically, I wanted to know what could be done by the government to back up an announcement of future intervention by the appropriate use of fiscal policy, exchange rate policy, or various forms of quantitative easing. This was the main focus of the paper, The Deflation Bias and Committing to Being Irresponsible, the title of which played on Paul Krugman's proposal that the Bank of Japan needed to commit to being irresponsible. (4) It was a theme I would return to repeatedly in work on the Great Depression in order to interpret various government policy actions in the 1930s, an agenda I took up after leaving graduate school at the urging of one of my advisers, Ben Bernanke, and many others. Great Depression and the Liquidity Trap My work on the Great Depression yielded three major conclusions. First, it gave a somewhat novel interpretation of the U. …

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.029
GPT teacher head0.259
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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