Journal of Economic Dynamics & Control最新文献

This article analyzes the effect of technological uncertainty in an innovation market where upstream innovators develop innovations and downstream technology adopters use them. Technological uncertainty takes the form that it is unclear when future innovations will arrive and how they will perform. We show that technological uncertainty not only reduces adopters’ incentives to use new innovations, but also decreases innovators’ incentives to develop them. This uncertainty effect leads to lower innovation and adoption rates at the market equilibrium, resulting in inefficiently slow innovation and diffusion processes compared to the social optimum. To solve this uncertainty problem, we show that a quantity-based regulation is more recommended than a market-based R&D or price subsidy.

Over the last four decades, the U.S. economy has experienced a few secular trends: declining labor share, increasing profit share, widening income and wealth inequalities, rising household sector leverage and associated financial instability, manifested in an increase in the probability of financial crises. This paper provides a unifying framework for explaining these trends based on a rise in firm market power in both product and labor markets. We develop a general equilibrium model and show that the rise in firm market power over the last few decades can generate all of these secular trends.

We show that a positive superelasticity of demand, often referred to as “Marshall's Second Law of Demand,” is a new push factor of optimal inflation in staggered price models. The positive superelasticity alters the trade-offs between output and inflation in the models. It allows higher trend inflation to steepen the slope of a model-based Phillips curve and lower the steady-state average markup, and thus reduces the inflation-related weight in a model-based welfare function for higher trend inflation and the steady-state welfare cost of higher trend inflation. Consequently, the positive superelasticity can make the optimal trend inflation rate positive and lessen the welfare difference between inflation targets of 2 percent and 4 percent.

We present a systematic treatment of dynamic effects of Odyssean forward guidance – explicit announcements about future policy rates. We focus on normal times unlike the mainstream of the literature that focuses on episodes of an effective (zero) lower bound on nominal interest rates. We present novel closed-form solutions to both deterministic and stochastic versions of a stylized new Keynesian model with fully anticipated future shocks. We establish a theorem that delineates the parameter space into real and complex-root regions characterized by different characteristic roots. We formulate a simple recipe for testing the effectiveness of forward guidance: the size of the smallest root is a single sufficient statistics that determines whether or not forward guidance generates significant contemporaneous effects.

We compare the economic effects of forward guidance and quantitative easing utilizing the four-equation New Keynesian model of Sims et al. (2023) with agents forming expectations via an adaptive learning rule. The results indicate forward guidance can have a greater influence on macroeconomic variables compared to quantitative easing. Adaptive learning agents estimate a higher effect of forward guidance on the economy leading to a greater impact on expectations, and thus, contemporaneous inflation. However, the performance gap between forward guidance and quantitative easing can change. If quantitative easing includes anticipated shocks, more households finance consumption through long-term borrowing, and the central bank provides a greater percentage of liquidity in the long-term borrowing market, the performance of quantitative easing can increase, and at times, outperform forward guidance.

Macroprudential policy improves economic outcomes by reducing the likelihood and severity of financial crises. Yet, are there unintended long run consequences to the introduction of a macroprudential policy regime, and are they conditional on the level of wealth inequality? I answer these questions by looking at the effect of a reduction in the maximum loan-to-value (LTV) ratio on homeownership rates, house prices and housing wealth inequality across two economies with different initial wealth dispersion. I use a heterogeneous agent model in which households face an endogenous borrowing limit in the form of a collateral constraint. A reduction in the LTV limit tightens the borrowing constraint and changes the levels of wealth at which households rent or become constrained or unconstrained owners. The key finding of this paper is that initial conditions matter; a greater share of households is affected when wealth is distributed more equally. This larger impact on aggregate demand leads to a stronger fall in house prices and a larger rise in the share of constrained homeowners and housing wealth inequality. The effects are also non-linear in the LTV ratio, with progressively stronger effects at lower LTV ratios.

This paper investigates how financial conditions and macroeconomic uncertainty jointly affect macroeconomic tail risks. We first document that tight financial conditions decrease all conditional quantiles of future output growth in the near term, while high macroeconomic uncertainty only stretches the interquartile range. Because financial conditions and uncertainty comove substantially, the conditional means and variances shift simultaneously in the opposite direction. Consequently, the downside risk varies much more than the upside risk. A quantile impulse response analysis indicates that both financial and uncertainty shocks are responsible for the asymmetric behaviors in the downside and upside risks.

We compute missing growth due to imputation practices in Korea by applying the market share approach of Aghion et al. (2019) and investigate its contribution to the trend and the cyclicality of measurement errors in official inflation and GDP. We find that missing growth is strongly procyclical and that it correlates significantly to an oil shock and a monetary policy shock. The procyclical missing growth, together with the significant correlation with a monetary policy shock, provides additional difficulties in identifying the slope of the Phillips curve from officially measured statistics. Furthermore, missing growth complicates the policy tradeoff between inflation and output stabilization in the face of cost-push shocks, calling for a greater emphasis on inflation stabilization.

We study how the output gap affects potential output over time—i.e., the dynamic hysteresis effect. To do so, we introduce novel unobserved components (UC) models that consider hysteresis as a sequence of lagged effects, thus separating the long-run recession-induced adverse effects from other trend-cycle interactions. The proposed models nest several existing UC models in the literature and accommodate two key characteristics of output dynamics: non-neutrality in the long-run and time-to-build effects. Using Bayesian estimation methods, we find robust evidence supporting the presence of hysteresis effects after the 1970s, with the negative long-run effect of the Global Financial Crisis and the COVID-19 recessions robustly identified. Via Bayesian model averaging, we provide precise and intuitive output gap estimates that highlight the relationship between business cycle fluctuations and the decline in economic growth. Our findings indicate that output trend-cycle decompositions that do not consider hysteresis effects can alter stabilization policy trade-offs.

Using data from Binance, we find strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies. The results are robust across various methods, including the adaptive LASSO and principal component analysis. Furthermore, a long-short portfolio formed on the past returns of cryptocurrencies can generate a sizable return out-of-sample after accounting for transaction costs. Overall, our findings corroborate cross-cryptocurrency return predictability and are consistent with the spillover effect mechanism, where common shocks among cryptocurrencies coupled with the limited attention of investors lead to slow information diffusion across coins.