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基于时间齐次高质量标记信息的未来条件风险率超高效估计AI 解读

Summary We introduce a new concept for forecasting future events based on marker information. The model is developed in the nonparametric counting process setting under the assumptions that the marker is of so-called high quality and with a time-homogeneous conditional distribution. Despite the model having nonparametric parts, it is established herein that it attains a parametric rate of uniform consistency and uniform asymptotic normality. In usual nonparametric scenarios,…

ℓ2 松弛：预测组合与投资组合分析的应用AI 解读

Abstract We propose ℓ2-relaxation, which is a novel convex optimization problem, to tackle a forecast combination with many forecasts or a minimum variance portfolio with many assets. ℓ2-relaxation minimizes the squared Euclidean norm of the weight vector subject to a set of relaxed linear inequalities to balance the bias and variance. It delivers optimality with approximately equal within-group weights when latent block equicorrelation patterns dominate the high-dimensional…

通胀知识、通胀预期与央行信任：一项调查实验AI 解读

Abstract This paper explores the causal effects of inflation literacy using a two-step randomized controlled trial. The first step shows that qualitative information about inflation and monetary policy (Literacy treatment) enhances knowledge about these issues and raises both the likelihood of providing inflation predictions and trust in the central bank. The second step tests the joint impact of the Literacy treatment and further quantitative treatments. While the level of…

通过杠杆效应识别波动率风险价格AI 解读

In asset pricing models with stochastic volatility, uncertainty about volatility affects risk premia through two channels: aversion to decreasing returns and aversion to increasing volatility. We analyze the identification of and robust inference for structural parameters measuring investors’ aversions to these risks: the return risk price and the volatility risk price. In the presence of a leverage effect (instantaneous causality between the asset return and its…

平稳向量自回归模型中的长期风险AI 解读

This paper introduces a local-to-unity/small sigma model for stationary processes with long-range persistence and non-negligible long-run prediction and estimation risks. The model represents a process containing unobserved short and long-run components measured on different time scales. The short-run component is defined in calendar time, while the long-run component evolves in rescaled time with ultra-long units. We develop estimation and long-run prediction methods for…

条件谱方法AI 解读

We model predictive scale-specific cycles. By employing suitable matrix representations, we express the forecast errors of covariance-stationary multivariate time series in terms of conditionally orthonormal scale-specific bases. The representations yield conditionally orthogonal decompositions of these forecast errors. They also provide decompositions of their variances and betas in terms of scale-specific variances and betas capturing predictive variability and…

利用大量辅助统计量进行模拟估计与长期动态分析AI 解读

The existing asymptotic theory for estimators obtained by simulated minimum distance does not cover situations in which the number of components of the auxiliary statistics (or number of matched “moments”) is large — typically larger than the sample size. We establish the consistency of the simulated minimum distance estimator in this situation and derive its asymptotic distribution. Our estimator is easy to implement and allows us to exploit all the informational content of…

通过相关性的多重检验正则化股票收益协方差矩阵AI 解读

This paper develops a large-scale inference approach for the regularization of stock return covariance matrices. The framework allows for the presence of heavy tails and multivariate GARCH-type effects of unknown form among the stock returns. The approach involves simultaneous testing of all pairwise correlations, followed by setting non-statistically significant elements to zero. This adaptive thresholding is achieved through sign-based Monte Carlo resampling within…

美国货币政策与均衡不确定性AI 解读

ABSTRACT In this paper, I investigate the stance of the US monetary policy in the postwar period. To this end, I show that two features are key: a rich structural model and a parameterization of the indeterminate solution that ensures a sufficient exploration of the indeterminacy region during the estimation procedure. Therefore, I estimate a medium‐scale model on the US macroeconomic data using a novel solution method to allow for indeterminacy. The evidence of a passive…

识别经济增长放缓的来源：需求与供给AI 解读

ABSTRACT Long‐run GDP growth has declined in the United States over the past two decades. Two competing views take centre stage in accounting for this slowdown: the demand‐side view and the supply‐side view. I empirically quantify their relative importance in a Bayesian SVAR with common trends identified using sign restrictions. While supply‐side factors were the main driver of long‐run GDP growth prior to 2000, demand‐side factors explain half of its slowdown afterwards.…

重新评估收益率利差对美国衰退的预测能力AI 解读

ABSTRACT Rudebusch and Williams (2009) predict recessions in the United States utilising a probit model with the lagged yield spread as a real‐time predictor. Mindful of the importance of recent yield curve movements, we update their analysis and evaluate quarterly forecasts from their probit model up to the end of 2023. We also analyze lagged financial conditions as an alternative real‐time predictor. We find that both the yield spread and financial conditions perform…

菲利普斯曲线的断点：来自面板数据的证据AI 解读

ABSTRACT We revisit the Phillips curve, applying new Bayesian panel methods with structural breaks to US and EU disaggregate data. Our approach lets us estimate both the number and timing of breaks and to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns. We find evidence of a flattening for US sectoral data and among EU countries, particularly poorer ones. Evidence of flattening is weaker for MSA‐level…

以大型因子增广 VAR 分析企业上下游与共同冲击AI 解读

ABSTRACT We evaluate the roles of upstream (supplier‐to‐user), downstream (user‐to‐supplier), and common factor shock transmission across firms by deriving interfirm networks and common factors from US equities over 1989–2017. We overcome the econometric challenges of estimating the large factor‐augmented vector autoregressive (FAVAR) system by developing two alternative approaches: one prioritizing computational efficiency and the other providing the full posterior…

混合半鞅的速率最优估计AI 解读

Consider the sum Y = B + B(H) of a Brownian motion B and an independent fractional Brownian motion B(H) with Hurst parameter H ∈ (0, 1). Even though B(H) is not a semimartingale, it was shown by Cheridito (Bernoulli 7 (2001) 913–934) that Y is a semimartingale if H > 3/4. Moreover, Y is locally equivalent to B in this case, so H cannot be consistently estimated from local observations of Y. This paper pivots on another unexpected feature in this model: if B and B(H) become…

具有 ARCH 误差的次几何遍历自回归模型AI 解读

In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in the 1980s, and it means that the transition probability measures converge to the stationary measure at a rate slower than geometric; this rate is also closely related to the convergence rate of $\beta $ -mixing…

时变系数面板模型的设定检验AI 解读

This paper provides nonparametric specification tests for the commonly used homogeneous and stable coefficients structures in panel data models. We first obtain the augmented residuals by estimating the model under the null hypothesis and then run auxiliary time series regressions of augmented residuals on covariates with time-varying coefficients (TVCs) via sieve methods. The test statistic is then constructed by averaging the squared fitted values, which are close to zero…

嵌套模型预测准确性检验的新方法AI 解读

We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to…

信息性观测时点下的函数主成分分析AI 解读

Summary Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven…

加性协方差矩阵模型：英国区域电力净需求建模AI 解读

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain’s…

大型有向网络上的重复故障建模AI 解读

Many lifeline infrastructure systems consist of thousands of components configured in a complex directed network. Disruption of the infrastructure constitutes a recurrent failure process over a directed network. Statistical inference for such network recurrence data is challenging because of the large number of nodes with irregular connections among them. Motivated by 16 years of Scottish Water operation records, we propose a network Gamma-Poisson Autoregressive NHPP (GPAN)…

高维时间序列中可解释且高效的无限阶向量自回归模型AI 解读

As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR model. However, its practicality has long been hindered by its non-identifiability, computational intractability, and difficulty of interpretation, especially for high-dimensional time series. This paper proposes a novel sparse infinite-order VAR model for high-dimensional time…

利用经济叙事预测通货膨胀AI 解读

We use economic narratives to forecast inflation with a large news corpus and machine learning algorithms. The economic narratives from the full text content of over 880,000 Wall Street Journal articles are decomposed into multiple time series representing interpretable news topics, which are then used to predict inflation. The results indicate that narrative-based forecasts are more accurate than the benchmarks, especially during recession periods. Narrative-based forecasts…

后验敏感性的估计及其在贝叶斯 VAR 结构分析中的应用AI 解读

The inherent feature of Bayesian empirical analysis is the dependence of posterior inference on prior parameters, which researchers typically specify.However, quantifying the magnitude of this dependence remains difficult.This article extends Infinitesimal Perturbation Analysis, widely used in classical simulation, to compute asymptotically unbiased and consistent sensitivities of posterior statistics with respect to prior parameters from Markov chain Monte Carlo inference…

分位数政策效应：美国宏观审慎政策应用AI 解读

To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for…