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Forecasting the term structure of government bond yields

政府债券收益率期限结构预测

Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach, which focuses on accurately fitting the cross section of interest rates at any given time but neglects time-series dynamics, nor the equilibrium approach, which focuses on time-series dynamics (primarily those of the instantaneous…

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Estimation of copula-based semiparametric time series models

基于 Copula 的半参数时间序列模型估计

This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric invariant (or marginal) distributions and parametric copula functions that capture the temporal dependence of the processes; the implied transition distributions are all semiparametric, and a member in this class can be expressed as a generalized semiparametric regression transformation model. One advantage of this copula…

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STATIONARITY CONDITION FOR AR INDEX PROCESS

自回归指数过程的平稳性条件

The stationarity conditions for an autoregressive (AR) process in general are reduced to a remarkably simple inequality if the lag coefficients are restricted to be identical. The condition is not only analytically elegant but also applicable in checking the validity of the stationarity conditions for such a restricted AR process of any order.We are deeply indebted to Professor Paolo Paruolo, NP co-editor of Econometric Theory, and anonymous referees for constructive…

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BREAK DATE ESTIMATION FOR VAR PROCESSES WITH LEVEL SHIFT WITH AN APPLICATION TO COINTEGRATION TESTING

含水平突变的 VAR 断点日期估计及其协整检验应用

In testing for the cointegrating rank of a vector autoregressive process it is important to take into account level shifts that have occurred in the sample period. Therefore the properties of estimators of the time period where a shift has taken place are investigated. The possible structural break is modeled as a simple shift in the level of the process. Two alternative estimators for the break date are considered, and their asymptotic properties are derived under various…

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UNIT ROOT TESTING FOR FUNCTIONALS OF LINEAR PROCESSES

线性过程非线性变换下的单位根检验

We consider the unit root testing problem with errors being nonlinear transforms of linear processes. When the linear processes are long-range dependent, the asymptotic distributions in the unit root testing problem are shown to be functionals of Hermite processes. Functional limit theorems for nonlinear transforms of linear processes are established. The obtained results differ sharply from the classical cases where asymptotic distributions are functionals of Brownian…

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MORE EFFICIENT ESTIMATION IN NONPARAMETRIC REGRESSION WITH NONPARAMETRIC AUTOCORRELATED ERRORS

非参数自相关误差下非参数回归的更有效估计

We define a three-step procedure for more efficient estimation of the nonparametric regression mean with nonparametric autocorrelated errors. The procedure is based upon a nonparametric prewhitening transformation of the dependent variable that has to be estimated from the data by a local polynomial technique. We establish the asymptotic distribution of our estimator under weak dependence conditions and show that it is more efficient than the conventional local polynomial…

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Evaluating Models of Autoregressive Conditional Duration

自回归条件持续期模型的评价

This article contains two novelties. First, a unified framework for testing and evaluating the adequacy of an estimated autoregressive conditional duration (ACD) model is presented. Second, two new classes of ACD models, the smooth transition ACD model and the time-varying ACD model, are introduced and their properties discussed. New misspecification tests for the ACD class of models are introduced. These are Lagrange multiplier and Lagrange multiplier–type tests against…

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Are Statistical Reporting Agencies Getting It Right? Data Rationality and Business Cycle Asymmetry

统计发布机构做对了吗?数据理性与经济周期不对称

This article provides new evidence on the rationality of early releases of industrial production (IP) and producer price index (PPI) data. Rather than following the usual practice of examining only first available and fully revised data, we examine the entire revision history for each variable. Thus we are able to assess, for example, whether earlier releases of data are in any sense “less” rational than later releases, and when data become rational. Our findings suggest…

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On the Relationships Between Real Consumption, Income, and Wealth

实际消费、收入与财富之间的关系

Many studies relate real nondurables and services consumption to real income and wealth, with the latter measures obtained by deflating with a price index for total consumption expenditures. This procedure is appropriate only if real nondurables and services consumption is a constant multiple of aggregate real consumption outlays, which is not the case in U.S. data. We develop an alternative approach that exploits the fact that the ratio of these series has historically been…

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Idiosyncratic Volatility, Stock Market Volatility, and Expected Stock Returns

特质波动率、股票市场波动率与预期股票收益

We find that the value-weighted idiosyncratic stock volatility and aggregate stock market volatility jointly exhibit strong predictive power for excess stock market returns. The stock market risk–return relation is found to be positive, as stipulated by the capital asset pricing model; however, idiosyncratic volatility is negatively related to future stock market returns. Also, idiosyncratic volatility appears to be a pervasive macrovariable, and its forecasting abilities…

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Multivariate GARCH models: a survey

多变量 GARCH 模型综述

Abstract This paper surveys the most important developments in multivariate ARCH‐type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research. Copyright © 2006 John Wiley & Sons, Ltd.

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Concordance probability and discriminatory power in proportional hazards regression

比例风险回归的一致性概率与区分能力

The concordance probability is used to evaluate the discriminatory power and the predictive accuracy of nonlinear statistical models. We derive an analytical expression for the concordance probability in the Cox proportional hazards model. The proposed estimator is a function of the regression parameters and the covariate distribution only and does not use the observed event and censoring times. For this reason it is asymptotically unbiased, unlike Harrell's c-index based on…

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First-order intrinsic autoregressions and the de Wijs process

一阶内蕴自回归与 de Wijs 过程

We discuss intrinsic autoregressions for a first-order neighbourhood on a two-dimensional rectangular lattice and give an exact formula for the variogram that extends known results to the asymmetric case. We obtain a corresponding asymptotic expansion that is more accurate and more general than previous ones and use this to derive the de Wijs variogram under appropriate averaging, a result that can be interpreted as a two-dimensional spatial analogue of Brownian motion…

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Symmetric diagnostics for the analysis of the residuals in regression models

回归模型残差分析的对称诊断方法

Typical alternative hypotheses in the analysis of residuals of a standard regression model are considered, and for each one a Bayesian diagnostic based on a symmetric form of the Kullback–Leibler divergence is determined. The results include an explicit expression for the diagnostic when the alternative hypothesis is that the errors are generated by an unknown distribution function with a Dirichlet process prior. This expression is immediately interpretable, exactly…

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On recursive estimation for time varying autoregressive processes

时变自回归过程的递归估计

This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the time-varying autoregressive parameters belong to appropriate smoothness classes. An adequate normalization for the correction term used in the recursive estimation procedure allows for very mild assumptions on the innovations distributions. The rate of convergence of the pointwise…

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Distribution free goodness-of-fit tests for linear processes

线性过程的分布无关拟合优度检验

This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence. Test statistics for composite hypotheses are functionals of a (approximated) martingale transformation of the Bartlett Tp-process with estimated parameters, which converges in distribution to the standard Brownian motion under the null hypothesis. We discuss tests of different natures such as omnibus, directional…

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Parameter estimates for fractional autoregressive spatial processes

分数自回归空间过程的参数估计

A binomial-type operator on a stationary Gaussian process is introduced in order to model long memory in the spatial context. Consistent estimators of model parameters are demonstrated. In particular, it is shown that $\hat{d}_{N}-d=O_{P}(\frac{(\operatorname{Log}N)^{3}}{N})$, where d=(d1,d2) denotes the long memory parameter.

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Testing for a linear MA model against threshold MA models

线性移动平均模型对门限移动平均模型的检验

This paper investigates the (conditional) quasi-likelihood ratio test for the threshold in MA models. Under the hypothesis of no threshold, it is shown that the test statistic converges weakly to a function of the centred Gaussian process. Under local alternatives, it is shown that this test has nontrivial asymptotic power. The results are based on a new weak convergence of a linear marked empirical process, which is independently of interest. This paper also gives an…

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Repeated surveys and the Kalman filter

重复调查与卡尔曼滤波

The time-series nature of repeated surveys is seldom taken into account. The few studies that do so smooth the period-wise estimates without using the cross-sectional information. This leads to inefficient estimation. We present a statistical model of repeated surveys and construct a computationally simple estimator based on the Kalman filter algorithm. The method efficiently uses the whole underlying data set, but only the first and second moments of the data are required…

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