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无条件异方差下基于 LAD 的自适应 Bootstrap 单位根检验AI 解读

This paper studies unit root testing based on least absolute deviations (LAD) regression under unconditional heteroscedasticity. We first derive asymptotic properties of the LAD estimator under both unit root and local-to-unity settings in the presence of unconditional heteroscedasticity and weak dependence. The results show that the limiting distribution of the LAD estimator (and thus the derived test statistics) is closely associated with unknown heteroscedasticity. To…

时变方差下瞬时因果关系的非参数检验AI 解读

This paper proposes a consistent nonparametric test with good sampling properties to detect instantaneous causality between vector autoregressive (VAR) variables with time-varying variances. The new test takes the form of the U -statistic, and has a limiting standard normal distribution under the null. We further show that the test is consistent against any fixed alternatives, and has nontrivial asymptotic power against a class of local alternatives with a rate slower than $T^{-1/2}$…

多变量波动率结构变化的符号检验AI 解读

This paper proposes sign-based CUSUM and QS tests for structural changes in multivariate volatility using least absolute deviation (LAD) regression. These tests offer the advantage of relaxing moment conditions and providing greater robustness against various heavy-tailed innovations. To address potential power losses in the two basic statistics due to inaccurate estimation of the long-run variance (LRV) under alternatives, the paper then proposes two modified statistics…

过滤非平稳成分后的半参数动态 Copula 模型改进估计AI 解读

This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component…

基于随机森林的分位数控制法AI 解读

This paper studies robust inference procedure for treatment effects in panel data with flexible relationship across units via the random forest method. The key contribution of this paper is twofold. First, we propose a direct construction of prediction intervals for the treatment effect by exploiting the information of the joint distribution of the cross-sectional units using random forest. In particular, we propose a Quantile Control Method (QCM) using the Quantile Random…

离散程度、偏度与峰度回归：美国工资结构应用AI 解读

ABSTRACT Quantile regression provides a powerful tool for investigating the effects of covariates on key quantiles of a conditional distribution. However, we often lack a general picture of how covariates affect the overall shape of the conditional distribution. Using quantile regression estimation and quantile‐based measures of spread, skewness, and kurtosis, we propose spread regression, skewness regression, and kurtosis regression as empirical tools to quantify the…

函数型分位数自回归AI 解读

This paper proposes a new class of time series models, the functional quantile autoregression (FQAR) models, in which the conditional distribution of the observation at the current time point is affected by its past distributional information, and is expressed as a functional of the past conditional quantile functions. Different from the conventional functional time series models which are based on functionally observed data, the proposed FQAR method studies functional…

含局部二次趋势的单位根新检验AI 解读

Summary This paper proposes a new test for unit root processes with a partial quadratic trend on an unknown break date, denoted as the URQ process herein. Such a process is extremely similar to the explosive bubble process, and both can capture the sharp rise in prices. We develop the asymptotic distributions under the local-to-unity hypothesis, which covers the URQ null and explosive root alternatives. Simulations show that the test has good finite sample performances and…

面板数据分位数回归的自助法推断AI 解读

This article develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations…

面板数据模型中的趋势设定检验AI 解读

This article proposes a consistent nonparametric test for common trend specifications in panel data models with fixed effects. The test is general enough to allow for heteroscedasticity, cross-sectional and serial dependence in the error components, has an asymptotically normal distribution under the null hypothesis of correct trend specification, and is consistent against various alternatives that deviate from the null. In addition, the test has an asymptotic unit power…

尾部删失数据下尾部特征的估计与推断AI 解读

This paper considers estimation and inference about tail features such as tail index and extreme quantile when the observations beyond some threshold are censored. Ignoring such tail censoring could lead to substantial bias and size distortion, even if the censored probability is tiny. We first propose a new maximum likelihood estimator (MLE) based on the Pareto tail approximation and derive its asymptotic properties. Then, we propose an alternative method of constructing…

滤除非平稳性的 Copula 时间序列模型AI 解读

Economic and financial time series data can exhibit nonstationary and nonlinear patterns simultaneously. This paper studies copula-based time series models that capture both patterns. We introduce a procedure where nonstationarity is removed via a filtration, and then the nonlinear temporal dependence in the filtered data is captured via a flexible Markov copula. We propose two estimators of the copula dependence parameters: the parametric (two-step) copula estimator where…

分布相等的光滑检验AI 解读

This note re-investigates the smooth tests for the equality of distributions introduced by Bera et al. (2013, Econometric Theory 29, 419–446) and provides a modified smooth test which works for the general case with two sample sizes m and n . Asymptotic properties of the proposed test statistic under both the null and the alternative hypothesis are studied.

半参数回归的统一推断AI 解读

Summary In the literature, a discrepancy in the limiting distributions of least square estimators between the stationary and nonstationary cases exists in various regression models with different persistence level regressors. This hinders further statistical inference since one has to decide which distribution should be used next. In this paper, we develop a semiparametric partially linear regression model with stationary and nonstationary regressors to attenuate this…

动态异方差下非参数回归的有效估计AI 解读

We study the efficient estimation of nonparametric regression in the presence of heteroskedasticity. We focus our analysis on local polynomial estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. We show that, although traditionally it is advised that one should not weight for heteroskedasticity in nonparametric…

平均效应遗漏了什么？企业多元化的分布效应AI 解读

Corporate diversification is one of the most debated topics in finance over the past two decades. While it is widely believed that there exists a discount in the stock market valuation of conglomerate firms, the extant research based on least squares methods points to different directions. We argue that the existing empirical analyses ignore some important data features, especially cross sectional heterogeneity, predicted by both theories and casual observations on corporate…

条件异方差时间序列模型的混合分位数回归估计AI 解读

Summary Estimating conditional quantiles of financial time series is essential for risk management and many other financial applications. For time series models with conditional heteroscedasticity, although it is the generalized auto-regressive conditional heteroscedastic (GARCH) model that has the greatest popularity, quantile regression for this model usually gives rise to non-smooth non-convex optimization which may hinder its practical feasibility. The paper proposes an…

波动率变化检验AI 解读

In this paper, we propose a consistent U‐statistic test with good sampling properties to detect changes in volatility. We show that the test has a limiting standard normal distribution under the null hypothesis, and that it is powerful compared with various alternatives. A Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes in volatility. An empirical example is examined to demonstrate the…

正态分布的新刻画与正态性检验AI 解读

We study the asymptotic covariance function of the sample mean and quantile, and derive a new and surprising characterization of the normal distribution: the asymptotic covariance between the sample mean and quantile is constant across all quantiles, if and only if the underlying distribution is normal. This is a powerful result and facilitates statistical inference. Utilizing this result, we develop a new omnibus test for normality based on the quantile-mean covariance…

条件异方差下的自适应非参数回归AI 解读

In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton–Raphson estimator and the local profile likelihood estimator are investigated. We show that the proposed estimators are…

通过最优组合分位数信息实现高效回归估计AI 解读

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As…

单位根：Peter C. B. Phillips 贡献的选择性综述AI 解读

Peter C. B. Phillips has made fundamental contributions to unit root econometrics. This paper provides a selective review of Peter’s contribution to unit roots, with a focus on unit root asymptotics, unit root tests, and testing for stationarity against the unit root alternative. The discussion puts a relatively heavier weight on Peter’s most recent work.

金融市场中的右尾信息AI 解读

It is well known that when investors evaluate risk or opportunity, they often depart from predictions of expected utility. In addition, for both academic and financial communities it is a familiar stylized fact that stock return distributions are not normal. Both empirical evidence and experimental evidence indicate that distributional information of asset returns has an important impact on investors. In this paper, we argue that the right-tail distributional information of…

无限方差时间序列的预期损失估计与推断AI 解读

We study estimation and inference of the expected shortfall for time series with infinite variance. Both the smoothed and nonsmoothed estimators are investigated. The rate of convergence is determined by the tail thickness parameter, and the limiting distribution is in the stable class with parameters depending on the tail thickness parameter of the time series and on the dependence structure, which makes inference complicated. A subsampling procedure is proposed to carry…