Journal of Monetary Economics最新文献

Asymmetric information about both private valuations of assets and their quality gives rise to uncertainty over sellers’ motives of trade, allowing high-valuation holders of low-quality assets to engage in speculative trades that involve no allocative gains. When sellers compete to find buyers, such speculative behaviour not only dilutes the average quality of assets but also creates a welfare-detrimental congestion externality that lengthens the time on market for each individual seller. A market designer can mitigate the inefficiencies by imposing a transaction tax and, in the case of severe adverse selection, limiting market participation.

We analyze the effects of borrower-based macroprudential policy at the household level. We exploit administrative Dutch tax and housing records in conjunction with the introduction of a mortgage loan-to-value (LTV) limit. We find that the regulation sharply reduces mortgage leverage with bunching at the LTV limit. While (regulation) affected households reduce total leverage and interest expenses, they also decrease cash balances to satisfy the LTV limit, generating an important solvency-liquidity trade-off. Nevertheless, affected households experience less financial distress after the introduction of the LTV regulation. Moreover, these households experience better liquidity management and smoother consumption following income loss. Overall, our results highlight the key financial stability and real effects of borrower-based macroprudential policy.

Using data from the Survey of Professional Forecasters, we observe that a large fraction of analysts’ expectations about future economic growth is not due to technology or other shocks to fundamentals measured by the business cycle literature. We find that these unexplained changes in forecast revisions predict significant boom-bust dynamics in the key macroeconomic aggregates. We offer a novel theory where boom-bust dynamics stem from expectation shocks orthogonal to fundamentals.

What is the most cost-efficient way to impose trade sanctions against Russia? We build a quantitative model of international trade with input–output connections. Sanctioning countries choose import tariffs to simultaneously maximize their income and minimize Russia’s income, with different weights placed on these objectives. We find, first, that for countries with low willingness to pay for sanctions against Russia, the most cost-efficient sanction is an approximately 20% tariff on all Russian products. Second, if countries are willing to pay at least US$0.70 for each US$1 drop in Russian welfare, an embargo on Russia’s mining and energy products is the most cost-efficient policy.

This paper evaluates the efficacy of the Secondary Market Corporate Credit Facility, a program designed to stabilize the U.S. corporate bond market during the COVID-19 pandemic. The program announcements on March 23 and April 9, 2020, significantly reduced investment-grade credit spreads across the maturity spectrum – irrespective of the program’s maturity-eligibility criterion – and ultimately restored the normal upward-sloping term structure of credit spreads. The Federal Reserve’s actual purchases reduced credit spreads of eligible bonds 3 basis points more than those of ineligible bonds, a sizable effect given the modest volume of purchases. A calibrated variant of the preferred habit model shows that a “dash for cash” – a selloff of shorter-term lowest-risk investment-grade bonds – combined with a spike in the arbitrageurs’ risk aversion, can account for the inversion of the investment-grade credit curve during the height of turmoil in the market. Consistent with the empirical findings, the Fed’s announcements, by reducing risk aversion and alleviating market segmentation, helped restore the upward-sloping credit curve in the investment-grade segment of the market.

Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Our Monte Carlo simulations and empirical applications illustrate how the weights leverage the strengths of each model by (i) trading off properties of each model depending on variable, horizon, and application and (ii) accounting for the full predictive distribution rather than being restricted to specific moments.¹

The outcome of the Brexit referendum in June 2016 was largely unanticipated. Even after the “Leave” vote, surprises regarding the withdrawal process affected the UK economy. We draw on an official list of political events published by the House of Commons Library and daily data on asset prices and economic policy uncertainty to construct a novel instrument for Brexit surprises. Including a monthly aggregate of this instrument into a vector-autoregressive model of the UK economy, an adverse Brexit surprise lowers GDP growth while raising CPI inflation. We provide evidence that the Bank of England fended off a worse economic contraction.

We study the optimal monetary policy problem in a New Keynesian economy with a zero lower bound (ZLB) on the nominal interest rate, when the steady state natural rate ($r^{\ast }$) becomes permanently negative. We show that the optimal policy aims to approach gradually a new steady state with positive average inflation. Around that steady state, the optimal policy implies well defined (second-best) paths for inflation and output in response to shocks to the natural rate. Under plausible calibrations, the optimal policy implies that the nominal rate remains at its ZLB most of the time. Despite the latter feature, the central bank can implement the optimal outcome as a unique equilibrium by means of an appropriate nonlinear interest rate rule. In order to establish that result, we derive sufficient conditions for local determinacy in a general model with endogenous regime switches.

Investors’ learning can drastically alter the dynamics of the variance risk premium: it no longer increases as economic conditions deteriorate but exhibits a highly nonlinear pattern, occasionally even turning negative. We demonstrate this intuition using a model where investors rationally form their belief about the hidden economic state. When the “bad” state becomes probable, investors start liking high future variance because it overwhelmingly correlates with lower marginal utility. This mechanism rationalizes the puzzling observation that risk-neutral volatility falls short of physical volatility at the peak of a severe crisis. Our results shed light on the interpretation of good economic uncertainty.

With quantity-based innovation targets and subsidy programs launched since the mid-2000s, China has seen a patent surge, accounting for 46% of the world’s total patent applications in 2020; however, the overall patent quality has been declining after 2008. This paper develops a Schumpeterian growth model featuring innovating firms’ quantity–quality trade-off between radical and incremental innovations, and decomposes subsidies’ aggregate impact into quantity and quality channels. We calibrate the model to Chinese firm-level data in the early 2010s. Our quantitative analysis shows that the quality channel effects are negative and dominant, and quantity-based subsidies in that period reduce the TFP growth rate and welfare by 0.19 percentage points and 3.31%, respectively. We evaluate welfare gains under a constrained planner’s problem, and propose skill subsidies which are quality-biased and effectively recover the optimal allocation.