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广义误差独立性的非参数自助检验AI 解读

In this paper, we develop a general method of testing for independence when unobservable generalized errors are involved. Our method can be applied to testing for serial independence of generalized errors, and testing for independence between the generalized errors and observable covariates. The former can serve as a unified approach to testing the adequacy of time series models, as model adequacy often implies that the generalized errors obtained after a suitable…

已实现波动率估计量的Edgeworth展开有效性AI 解读

The main contribution of this paper is to establish the formal validity of Edgeworth expansions for realized volatility estimators. First, in the context of no microstructure effects, our results rigorously justify the Edgeworth expansions for realized volatility derived in Gonçalves and Meddahi (2009, Econometrica 77, 283–306). Second, we show that the validity of the Edgeworth expansions for realized volatility might not cover the optimal two‐point distribution wild…

基于广义最小二乘去趋势数据的残差协整检验AI 解读

We provide generalized least‐squares (GLS) detrended versions of single‐equation static regression or residuals‐based tests for testing whether or not non‐stationary time series are cointegrated. Our approach is to consider nearly optimal tests for unit roots and to apply them in the cointegration context. We derive the local asymptotic power functions of all tests considered for a triangular data‐generating process, imposing a directional restriction such that the…

具有非对称效率溢出的面板空间自回归随机前沿模型AI 解读

By blending seminal literature on non-spatial stochastic frontier models with key contributions to spatial econometrics we develop a spatial autoregressive (SAR) stochastic frontier for panel data. The specification of the SAR frontier allows efficiency to vary over time and across the cross-sections. Efficiency is calculated from a composed error structure by assuming a half-normal distribution for inefficiency. The SAR frontier is estimated using maximum likelihood methods…

平稳与非平稳自回归中自适应 Lasso 的一致及保守模型选择AI 解读

We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency as if only these had been included in the model from the outset. In particular, this implies that it is able to discriminate between stationary and nonstationary autoregressions and it…

非平稳数据的非参数变换回归AI 解读

We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.

通过边际似然提取阶梯状信号AI 解读

This paper studies the estimation of stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally…

潜在因子模型中的个性化预测与稀疏性追求AI 解读

Yunzhang Zhu, Xiaotong Shen & Changqing YeYunzhang Zhu (E-mail: zhuxx351@umn.edu), Xiaotong Shen (E-mail: shenx002@umn.edu), and Changqing Ye (E-mail: cqye@stat.umn.edu), School of Statistics, University of Minnesota, Minneapolis, MN 55455. Research supported in part by NSF grants DMS-1207771 and DMS-1415500, NIH grants R02GM08153 and R01HL105397. The authors would like to thank Professor Hui Jiang for technical help in OpenMP coding, and thank the editor, the associate…

In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how…

The late-2000s financial crisis stressed the need to understand the world financial system as a network of countries, where cross-border financial linkages play a fundamental role in the spread of systemic risks. Financial network models, which take into account the complex interrelationships between countries, seem to be an appropriate tool in this context. To improve the statistical performance of financial network models, we propose to generate them by means of…

非同步含噪资产价格的平顶已实现核二次协变差估计AI 解读

This article develops a general multivariate additive noise model for synchronized asset prices and provides a multivariate extension of the generalized flat-top realized kernel estimators, analyzed earlier by Varneskov (2014), to estimate its quadratic covariation. The additive noise model allows for α-mixing dependent exogenous noise, random sampling, and an endogenous noise component that encompasses synchronization errors, lead-lag relations, and diurnal…

门限双重自回归模型的检验与估计AI 解读

This article first proposes a score-based test for a double autoregressive model against a threshold double autoregressive (AR) model. It is an asymptotically distribution-free test and is easy to implement in practice. The article further studies the quasi-maximum likelihood estimation of a threshold double autoregressive model. It is shown that the estimated threshold is n-consistent and converges weakly to a functional of a two-sided compound Poisson process and the…

利用非伪因子预测美国宏观经济时间序列AI 解读

This study examines the practical implications of the fact that structural changes in factor loadings can produce spurious factors (or irrelevant factors) in forecasting exercises. These spurious factors can induce an overfitting problem in factor-augmented forecasting models. To address this concern, we propose a method to estimate nonspurious factors by identifying the set of response variables that have no structural changes in their factor loadings. Our theoretical…

低频时间序列的高频拆分：方法、信息损失与诊断AI 解读

Duk B. Juna, Jihwan Moonb & Sungho Parkca KAIST, College of Business, Seoul, Republic of Korea b Warrington College of Business, University of Florida, Gainesville, FL 32611 c W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287 This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to…

时间序列模型中的非线性、结构突变与长程依赖AI 解读

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. We develop asymptotic theory for parameter estimation and propose two model-building procedures. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects in…

异方差极端值的统计分析AI 解读

Summary We extend classical extreme value theory to non-identically distributed observations. When the tails of the distribution are proportional much of extreme value statistics remains valid. The proportionality function for the tails can be estimated non-parametrically along with the (common) extreme value index. For a positive extreme value index, joint asymptotic normality of both estimators is shown; they are asymptotically independent. We also establish asymptotic…

重尾GARCH模型的GEL估计与稳健经验似然推断AI 解读

We construct a Generalized Empirical Likelihood estimator for a GARCH(1, 1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic normality, efficiency and empirical likelihood based confidence regions for very heavy-tailed random volatility data. We show the implied probabilities from the tail-trimmed Continuously Updated Estimator elevate weight for usable large values, assign…

两个期权市场的定价核与波动率风险AI 解读

Using both S&P 500 option and recently introduced VIX option prices, we study pricing kernels and their dependence on multiple volatility factors. We first propose nonparametric estimates of marginal pricing kernels, conditional on the VIX and the slope of the variance swap term structure. Our estimates highlight the state-dependence nature of the pricing kernels. In particular, conditioning on volatility factors, the pricing kernel of market returns exhibit a downward…

含趋势协整模型的伪高斯与局部最优秩检验AI 解读

This paper provides pseudo-Gaussian and locally optimal rank-based tests for the cointegration rank in linear cointegrated error-correction models with common trends and i.i.d. elliptical innovations. The proposed tests are asymptotically distribution-free, hence their validity does not depend on the actual distribution of the innovations. The proposed rank-based tests depend on the choice of scores, associated with a reference density that can freely be chosen. Under…

横截面相关下的级数估计AI 解读

An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, including conditional and unconditional heteroscedasticity, along with conditions on regressors that allow dependence and do not require existence of a density. The conditions aim to accommodate various settings plausible in economic…

粒子高效重要性抽样AI 解读

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by…

股票回购的实体经济效应AI 解读

We employ a regression discontinuity design to identify the real effects of share repurchases on other firm outcomes. The probability of share repurchases that increase earnings per share (EPS) is sharply higher for firms that would have just missed the EPS forecast in the absence of the repurchase, when compared with firms that “just beat” the EPS forecast. We use this discontinuity to show that EPS-motivated repurchases are associated with reductions in employment and…

非常规货币政策措施的影响：欧洲央行证券市场计划的实证证据AI 解读

We assess the yield impact of asset purchases within the European Central Bank׳s (ECB) Securities Markets Programme (SMP) in five euro area sovereign bond markets from 2010–11. In addition to large announcement effects, we find an impact of approximately −3 basis points at the five-year maturity for purchases of 1/1000 of the outstanding debt. Bond yield volatility and tail risk are lower on intervention days for most SMP countries. A dynamic specification points to both…

价格与波动率的共同跳跃AI 解读

The nature of the dependence between discontinuities in prices and contemporaneous discontinuities in volatility (co-jumps) has been reported by many as being elusive, in terms of sign, magnitude, and statistical significance. Using a novel identification strategy in continuous time relying on trade-level information for spot variance estimation, as well as infinitesimal cross-moments, we document that a sizeable proportion of discontinuous changes in prices are associated…