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非平稳性与模型误设引起的低频污染理论：对异方差与自相关稳健推断的影响AI 解读

We establish theoretical results about the low frequency contamination (i.e., long memory effects) induced by general nonstationarity for estimates such as the sample autocovariance and the periodogram, and deduce consequences for heteroskedasticity and autocorrelation robust (HAR) inference. We present explicit expressions for the asymptotic bias of these estimates. We show theoretically that nonparametric smoothing over time is robust to low frequency contamination.…

无条件异方差下温和爆炸性自回归模型的推断AI 解读

Mildly explosive autoregressions have been extensively employed in recent theoretical and applied econometric work to model the phenomenon of asset market bubbles. An important issue in this context concerns the construction of confidence intervals for the autoregressive parameter that represents the degree of explosiveness. Existing studies rely on intervals that are justified only under conditional homoskedasticity/heteroskedasticity. This paper studies the problem of…

预测分位数回归中的统计推断AI 解读

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t -statistic in terms of functionals of Ornstein–Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a…

分位数回归中的异方差与自相关稳健协方差矩阵估计AI 解读

This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroscedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroscedasticity and…

对非平稳性稳健的预白化长期方差估计AI 解读

We introduce a nonparametric nonlinear VAR prewhitened long-run variance (LRV) estimator for the construction of standard errors robust to autocorrelation and heteroskedasticity that can be used for hypothesis testing in a variety of contexts including the linear regression model. Existing methods either are theoretically valid only under stationarity and have poor finite-sample properties under nonstationarity (i.e., fixed-b methods), or are theoretically valid under the…

局部投影的偏差AI 解读

Local projections (LPs) are a popular tool in macroeconomic research. We show that LPs are often used with very small samples in the time dimension and, consequently, that LP point estimates can be severely biased. Under regularity conditions, we derive simple expressions to approximate this bias and propose a way to correct for bias in LPs. Using a medium-scale macroeconomic time-series model, we demonstrate that the bias in point estimates can be economically meaningful.…

重新审视免带宽推断：样本分割方法AI 解读

Abstract The bandwidth-free tests for a multi-dimensional parameter have attracted considerable attention in econometrics and statistics literature. These tests can be conveniently implemented due to their tuning-parameter free nature and possess more accurate size as compared to the traditional heteroskedasticity and autocorrelation consistent-based approaches. However, when sample size is small/medium, these bandwidth-free tests exhibit large size distortion when both the…

非平稳条件下 HAR 检验的 fixed-b 极限分布与拒绝概率误差AI 解读

We show that the limiting distribution of HAR test statistics under fixed-b asymptotics is not pivotal when the data are nonstationary (i.e., time-varying autocovariance structure). It takes the form of a complicated function of Gaussian processes and depends on the second moments of the relevant series (e.g., of the regressors and errors for the case of the linear regression model). Hence, fixed-b inference methods based on stationarity are not theoretically valid in…

可能错设与非平稳模型中稳健推断的演化谱理论AI 解读

The literature on heteroskedasticity and autocorrelation robust (HAR) inference is extensive but its usefulness relies on stationarity of the relevant process, say Vt, usually a function of the data and estimated model residuals. Yet, a large body of work shows widespread evidence of various forms of nonstationarity in the latter. Also, many testing problems are such that Vt is stationary under the null hypothesis but nonstationary under the alternative. In either case, the…

稳健的预测优越性检验及专家预测组合评估AI 解读

Summary We develop forecast superiority tests that are robust to the choice of loss function by following Jin, Corradi and Swanson (JCS: 2017), and relying on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid and are correctly sized only under the least favorable case, our tests are uniformly asymptotically valid and non‐conservative. To show this, we establish uniform convergence…

时变波动下的稳健推断：专业预测者的实时评估AI 解读

Summary In many forecast evaluation applications, standard tests as well as tests allowing for time‐variation in relative forecast ability build on heteroskedasticity‐and‐autocorrelation consistent (HAC) covariance estimators. Yet, the finite‐sample performance of these asymptotics is often poor. “Fixed‐ ” asymptotics, used to account for long‐run variance estimation, improve finite‐sample performance under homoskedasticity, but lose asymptotic pivotality under time‐varying…

横截面相关数据下扩散指数预测的稳健推断AI 解读

In this article, we propose the time-series average of spatial HAC estimators for the variance of the estimated common factors under the approximate factor structure. Based on this, we provide the confidence interval for the conditional mean of the diffusion-index forecasting model with cross-sectional heteroscedasticity and dependence of the idiosyncratic errors. We establish the asymptotics under very mild conditions, and no prior information about the dependence structure…

均值结构与自相关稳健的协方差矩阵估计AI 解读

We consider estimation of the asymptotic covariance matrix in nonstationary time series. A nonparametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points (CPs) is proposed. It is algorithmically fast because neither a search for CPs, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally…

异方差分数单整时间序列模型中的自适应推断AI 解读

We consider estimation and inference in fractionally integrated time series models driven by shocks which can display conditional and unconditional heteroscedasticity of unknown form. Although the standard conditional sum-of-squares (CSS) estimator remains consistent and asymptotically normal in such cases, unconditional heteroscedasticity inflates its variance matrix by a scalar quantity, λ>1, thereby inducing a loss in efficiency relative to the unconditionally…

利用平滑聚类标准误进行时间序列模型推断AI 解读

This paper proposes a long run variance estimator for conducting inference in time series regression models that combines the nonparametric approach with a cluster approach. The basic idea is to divide the time periods into non-overlapping clusters. The long run variance estimator is constructed by first aggregating within clusters and then kernel smoothing across clusters or applying the nonparametric series method to the clusters with Type II discrete cosine transform. We…

允许条件异方差的多变量分数积分检验：收益波动率与成交量应用AI 解读

Summary We introduce a new joint test for the order of fractional integration of a multivariate fractionally integrated vector autoregressive (FIVAR) time series based on applying the Lagrange multiplier principle to a feasible generalised least squares estimate of the FIVAR model obtained under the null hypothesis. A key feature of the test we propose is that it is constructed using a heteroskedasticity‐robust estimate of the variance matrix. As a result, the test has a…

美国股票风险溢价可预测状态的实时检测AI 解读

Summary We propose new real‐time monitoring procedures for the emergence of end‐of‐sample predictive regimes using sequential implementations of standard (heteroskedasticity‐robust) regression t ‐statistics for predictability applied over relatively short time periods. The procedures we develop can also be used for detecting historical regimes of temporary predictability. Our proposed methods are robust to both the degree of persistence and endogeneity of the regressors in…

时间序列的一致非参数推断AI 解读

This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions.…

动态面板同期相关的异方差稳健 Breusch–Pagan 检验AI 解读

This paper proposes a heteroskedasticity-robust Breusch–Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel data models, without necessarily assuming independence of the cross-sections. The procedure allows for either fixed, strictly exogenous and/or lagged dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. The asymptotic validity of the…

未知形式异方差下分数阶时间序列的准极大似然与 bootstrap 推断AI 解读

We consider the problem of conducting estimation and inference on the parameters of univariate heteroskedastic fractionally integrated time series models. We first extend existing results in the literature, developed for conditional sum-of-squares estimators in the context of parametric fractional time series models driven by conditionally homoskedastic shocks, to allow for conditional and unconditional heteroskedasticity both of a quite general and unknown form. Global…

动态 Copula 的隐马尔可夫结构AI 解读

Understanding the time series dynamics of a multi-dimensional dependency structure is a challenging task. Multivariate covariance driven Gaussian or mixed normal time varying models have only a limited ability to capture important features of the data such as heavy tails, asymmetry, and nonlinear dependencies. The present paper tackles this problem by proposing and analyzing a hidden Markov model (HMM) for hierarchical Archimedean copulae (HAC). The HAC constitute a wide…

无条件协方差非恒定时的瞬时因果关系检验AI 解读

This article investigates the problem of testing instantaneous causality between vector autoregressive (VAR) variables with time-varying unconditional covariance. It is underlined that the standard test does not control the Type I errors, while the tests with White and heteroscedastic autocorrelation consistent (HAC) corrections can suffer from a severe loss of power when the covariance is not constant. Consequently, we propose a modified test based on a bootstrap procedure.…

强自相关时间序列的 HAC 修正AI 解读

Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I…

评论AI 解读

Conducting valid inference in a time series setting often requires the use of a heteroscedasticity and autocorrelation (HAC) robust variance estimator. Under mild dependence structures, the theory ...