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持久时间序列中随机周期的估计AI 解读

A number of economic, financial, and climatic time series exhibit persistent cycles which are characterized by time-dependence patterns and peaks in the spectrum. In this article, we introduce a class of semiparametric cyclical–memory processes which enable the modeling of random cyclical patterns in stationary and non-stationary time series. We develop a theoretical background and asymptotic estimation theory for the frequency of a cycle represented by the location of a…

GARCH 的预测密度与分布泛函预测AI 解读

This paper derives the analytic form of the multi-step ahead prediction density of a Gaussian GARCH(1,1) process with a possibly asymmetric news impact curve in the GJR class. These results can be applied when single-period returns are modeled as a GJR Gaussian GARCH(1,1) and interest lies in single-period returns at some future forecast horizon. The Gaussian density has been used in applications as an approximation to this as yet unknown prediction density; the analytic…

变换前后矩的联系及其在厚尾分布重抽样中的应用AI 解读

Let x be a transformation of y , whose distribution is unknown. We derive an expansion formulating the expectations of x in terms of the expectations of y . Apart from the intrinsic interest in such a fundamental relation, our results can be applied to calculating E( x ) by the low-order moments of a transformation which can be chosen to give a good approximation for E( x ). To do so, we generalize the approach of bounding the terms in expansions of characteristic functions,…

方差矩阵的无结构设定估计AI 解读

This paper introduces a new method for estimating variance matrices. Starting from the orthogonal decomposition of the sample variance matrix, we exploit the fact that orthogonal matrices are never ill-conditioned and therefore focus on improving the estimation of the eigenvalues. We estimate the eigenvectors from just a fraction of the data, then use them to transform the data into approximately orthogonal series that deliver a well-conditioned estimator (by construction),…

线性过程加权和的渐近正态性AI 解读

We establish asymptotic normality of weighted sums of linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and innovations. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular, they are…

重新审视 Nelson–Plosser：自相关函数方法AI 解读

We detect a new stylized fact that is common to the dynamics of all macroeconomic series, including financial aggregates. Their Auto-Correlation Functions (ACFs) share a common four-parameter functional form that arises from the dynamics of a general equilibrium model with heterogeneous firms. We find that, not only does our formula fit the data better than the ACFs that arise from auto-regressive and fractionally-integrated models, but it also yields the correct shape of…

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两种长期方差估计量：超越短记忆AI 解读

This paper deals with the estimation of the long-run variance of a stationary sequence. We extend the usual Bartlett-kernel heteroskedasticity and autocorrelation consistent (HAC) estimator to deal with long memory and antipersistence. We then derive asymptotic expansions for this estimator and the memory and autocorrelation consistent (MAC) estimator introduced by Robinson [Robinson, P. M., 2005. Robust covariance matrix estimation: HAC estimates with long…

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均值—中位数—众数不等式的反例AI 解读

Let x be a random variable whose first three moments exist. If the density of x is unimodal and positively skewed, then counterexamples are provided which show that the inequality mode ≤ median ≤ mean does not necessarily hold.I thank Andrey Vasnev for help with the graphs and Jan Magnus for various helpful discussions. I also thank Martin Bland, Paolo Paruolo, Peter Phillips, Michael Rockinger, and a referee for their comments. ESRC grant R000239538 is gratefully…

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Solution to Normal's deconvolution and the independence of sample mean and variance problem.

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密度泛函及其期权定价应用AI 解读

We present a method of estimating density-related functionals, without prior knowledge of the density's functional form. The approach revolves around the specification of an explicit formula for a new class of distributions that encompasses many of the known cases in statistics, including the normal, gamma, inverse gamma, and mixtures thereof. The functionals are based on a couple of hypergeometric functions. Their parameters can be estimated, and the estimates then reveal…

Normal's deconvolution and the independence of sample mean and variance.

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The paper by Kiefer, Vogelsang and Bunzel (2000), KVB henceforth, provides an interesting unconventional application of functional limit theory to a conventional problem. In this note, we point out that the limiting distribution of the t^{∗} test proposed by KVB turns out to be equivalent to the asymptotic distribution of one of the statistics analysed by Abadir and Paruolo (1997), AP henceforth. The mixed-Normal random variables studied in AP and KVB are different, but they…

多元自回归序列中二次型的联合矩母函数AI 解读

Let { X t } follow a discrete Gaussian vector autoregression with deterministic components. We derive the exact finite-sample joint moment generating function (MGF) of the quadratic forms that form the basis for the sufficient statistic. The formula is then specialized to the limiting MGF of functionals involving multivariate and univariate Ornstein–Uhlenbeck processes, drifts, and time trends. Such processes arise asymptotically from more general non-Gaussian processes and…

VAR 维度对估计量偏误的影响AI 解读

Vector AutoRegressions (VARs) have now become the most popular tool of Time Series analysis amongst econometricians. Unfortunately, little is known about the analytic finite-sample properties of parameter estimators for such systems. The asymptotic analysis of VARs published to date does not address questions regarding the influence of the number and nature of the system's variates on parameter estimates. Clearly, both questions will have repercussions on the way VARs are…

汇合特征函数反演中的“魔鬼之角”问题AI 解读

WE WARN OF A CLASS of problems that can occur when inverting confluent characteristic functions (CF's). The term confluence is often used in Mathematics in connection with analysis and/or dynamic (difference, differential, and integral) equations; for example, see the classic text by Whittaker and Watson (1927). A confluence (of singularities) is a joint degeneracy that occurs within a function; here, the CF. When one is dealing with the CF of a k-dimensional variate where k…

协整分析中的两个混合正态密度AI 解读

We derive explicitly the exact density functions of two key mixed normal variates that arise from cointegration analysis. We also plot them, and analyze their analytical features and implications.

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多变量自回归序列中二次型的联合矩母函数AI 解读

Let ( X 1 ) be a discrete multivariate Gaussian autoregressive process of order 1. The paper derives the exact finite-sample joint moment generating function (m.g.f.) of the three quadratic forms constituting the sufficient statistic of the process. The formula is then specialized to some cases of interest, including the m.g.f. of functional of multivariate Ornstein-Uhlenbeck processes that arise asymptotically from more general ( X 1 ) processes as well.