Journal of Economic Dynamics & Control最新文献

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.

This paper investigates the effects of a tax reform that eliminates tax rate heterogeneity and cumulative taxation using a general equilibrium model with multiple sectors with market power. Industries are connected through input-output linkages, and changes in taxation are not confined within industries. We calibrate the model to Brazil, a country with a highly distorted tax system. The revenue-neutral tax reform generates gains of 7.9% of GDP and 1.8% of welfare. Just eliminating VAT rate dispersion leads to a 6.0% increase in GDP. Due to propagation effects, in 10 sectors direct taxes increased but output and profits did not fall.

Using a panel of 31,027 firms in 47 developing countries over the period 2006-2020, this paper looks at the effects of inflation targeting on firm performance. Estimations performed using entropy balancing to address endogeneity in policy adoption reveal that inflation targeting significantly increases firm performance, mainly measured by sales growth and productivity growth. This effect is robust to a wide range of tests (including alternative models, measures, and samples), and may vary under different macroeconomic and firms' structural characteristics. Lastly, by looking at possible transmission channels, we reveal that these favorable effects seem related with the capacity of the inflation targeting framework to reduce macroeconomic instability.

We show how to simulate exactly the asset price and the variance under the Hull and White stochastic volatility model. We derive analytical formulas for the Laplace transform of the time integral of volatility conditional on the variance level at the endpoint of the time interval and the Laplace transform of integrated variance conditional on both integrated volatility and variance. Based on these results, we simulate the model through a nested-conditional factorization approach, where Laplace transforms are inverted through the (conditional) Fourier-cosine (COS) method. Under this model, our approach can be used to generate unbiased estimates for the price of derivatives instruments. We propose some variants of the exact simulation scheme for computing unbiased estimates of option prices and sensitivities, a difficult task in the Hull and White model. These variants also allow for a significant reduction in the Monte Carlo simulation estimator's variance (around 93-98%) and the computing time (around 22%) when pricing options. The performances of the proposed algorithms are compared with various benchmarks. Numerical results demonstrate the faster convergence rate of the error in our method, which achieves an $O\left(s^{-1/2}\right)$ convergence rate, where s is the total computational budget, largely outperforming the benchmark.

The paper presents a sparse grid approximation method based on the hyperbolic cross and applies it to solve non-linear macroeconomic models. We show how the standard hyperbolic cross can be extended to give greater control over the approximating grid and we discuss how to implement an anisotropic hyperbolic cross. Applying the approximation method to four macroeconomic models, we establish that it delivers a level of accuracy on par or better than Smolyak's method and that it can produce accurate approximations using fewer points than Smolyak's method.