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Estimating invariant laws of linear processesby U-statistics

用U统计量估计线性过程的平稳分布泛函

Suppose we observe an invertible linear process with independent mean-zero innovations and with coefficients depending on a finite-dimensional parameter, and we want to estimate the expectation of some function under the stationary distribution of the process. The usual estimator would be the empirical estimator. It can be improved using the fact that the innovations are centered. We construct an even better estimator using the representation of the observations as…

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Statistical inference for time-inhomogeneous volatility models

时变波动率模型的统计推断

This paper offers a new approach for estimating and forecasting the volatility of financial time series. No assumption is made about the parametric form of the processes. On the contrary, we only suppose that the volatility can be approximated by a constant over some interval. In such a framework, the main problem consists of filtering this interval of time homogeneity; then the estimate of the volatility can be simply obtained by local averaging. We construct a locally…

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News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns

个股收益的新闻到达、跳跃动态与波动率成分

ABSTRACT This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time‐varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically…

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Assessing Generalized Method-of-Moments Estimates of the Federal Reserve Reaction Function

评估美联储反应函数的广义矩估计

Estimating a forward-looking monetary policy rule by the generalized method of moments (GMM) has become a popular approach. We reexamine estimates of the Federal Reserve reaction function using several GMM estimators and a maximum likelihood (ML) estimator. First, we show that over the baseline period 1979–2000, these alternative approaches yield substantially different parameter estimates. Using Monte Carlo simulations, we show that the finite-sample GMM bias can account…

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Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model

利用全球误差修正宏观计量模型刻画地区间相互依赖

Financial institutions are ultimately exposed to macroeconomic fluctuations in the global economy. This article proposes and builds a compact global model capable of generating forecasts for a core set of macroeconomic factors (or variables) across a number of countries. The model explicitly allows for the interdependencies that exist between national and international factors. Individual region-specific vector error-correcting models are estimated in which the domestic…

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ISSUES CONCERNING THE APPROXIMATION UNDERLYING THE SPECTRAL REPRESENTATION THEOREM

谱表示定理所用逼近的若干问题

In many important textbooks the formal statement of the spectral representation theorem is followed by a process version, usually informal, stating that any stationary stochastic process {ξ(t), t ∈ T} is the limit in quadratic mean of a sequence of processes {S(n,t), t ∈ T}, each consisting of a finite sum of harmonic oscillations with stochastic weights. The natural issues, whether the approximation error ξ(t) − S(n,t) is stationary or whether at least it converges to zero…

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A GENERALIZED PORTMANTEAU GOODNESS-OF-FIT TEST FOR TIME SERIES MODELS

时间序列模型的广义 Portmanteau 拟合优度检验

We present a goodness-of-fit test for time series models based on the discrete spectral average estimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statistic allows the null hypothesis to be either a short- or long-range dependence model. Our test is in the frequency domain, is easy to compute, and does not require the calculation of residuals from the fitted model. This is especially advantageous when the fitted model is not a…

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ON THE ROBUSTNESS OF HYPOTHESIS TESTING BASED ON FULLY MODIFIED VECTOR AUTOREGRESSION WHEN SOME ROOTS ARE ALMOST ONE

部分根接近一时完全修正 VAR 假设检验的稳健性

This paper proves that the fully modified vector autoregression (FM-VAR) estimator has second-order bias effects when some roots are local to unity. These bias effects are shown to result in potentially severe size distortions in FM-VAR testing when the hypothesis involves near unit root variables. In addition, the paper reveals that with the FM-VAR method near unit roots become estimated as exact unit roots with convergence speed faster than the order of the sample size.…

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COINTEGRATING SMOOTH TRANSITION REGRESSIONS

协整平滑转换回归

This paper studies the smooth transition regression model where regressors are I(1) and errors are I(0). The regressors and errors are assumed to be dependent both serially and contemporaneously. Using the triangular array asymptotics, the nonlinear least squares estimator is shown to be consistent, and its asymptotic distribution is derived. It is found that the asymptotic distribution involves a bias under the regressor-error dependence, which implies that the nonlinear…

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ROBUST TESTS OF THE UNIT ROOT HYPOTHESIS SHOULD NOT BE “MODIFIED”

稳健单位根检验不应被“修改”

The rank-based unit root tests proposed by Hasan and Koenker (1997, Econometrica 65, 133–161) have power equal to size for normal innovations. Unit root tests based on M-estimators exhibit the same behavior. The problem occurs because the test statistics are transformed to obtain computationally convenient critical values. I describe a convenient way to compute critical values without transforming the test statistics. The resulting tests are almost as powerful as least…

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Revisiting simple linear regression with autocorrelated errors

重新考察误差自相关的简单线性回归

This paper studies properties of ordinary and generalised least squares estimators in a simple linear regression with stationary autocorrelated errors. Explicit expressions for the variances of the regression parameter estimators are derived for some common time series autocorrelation structures, including a first‐order autoregression and general moving averages. Applications of the results include confidence intervals and an example where the variance of the trend slope…

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Generalised likelihood ratio tests for spectral density

谱密度的广义似然比检验

There are few techniques available for testing whether or not a family of parametric times series models fits a set of data reasonably well without serious restrictions on the forms of alternative models. In this paper, we consider generalised likelihood ratio tests of whether or not the spectral density function of a stationary time series admits certain parametric forms. We propose a bias correction method for the generalised likelihood ratio test of Fan et al. (2001). In…

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A multi-move sampler for estimating non-Gaussian time series models: Comments on Shephard & Pitt (1997)

非高斯时间序列模型的多步采样器:对 Shephard 与 Pitt(1997)的评论

This note points out a problem in the multi‐move sampler as proposed by Shephard & Pitt (1997) and provides an alternative correct formulation.

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Contiguity of the Whittle measure for a Gaussian time series

高斯时间序列中 Whittle 测度的相互毗连性

For a stationary time series, Whittle constructed a likelihood for the spectral density based on the approximate independence of the discrete Fourier transforms of the data at certain frequencies. Whittle's likelihood has been widely used in the literature for constructing estimators. In this paper, we show that, for a Gaussian time series, the Whittle measure is mutually contiguous with the actual distribution of the data. As a consequence, most asymptotic properties of…

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On the Statistical Analysis of Smoothing by Maximizing Dirty Markov Random Field Posterior Distributions

通过最大化“脏”马尔可夫随机场后验进行平滑的统计分析

We consider Bayesian nonparametric function estimation using a Markov random field prior based on the Laplace distribution. We describe efficient methods for finding the exact maximum a posteriori estimate, which handle constraints naturally and avoid the problems posed by nondifferentiability of the posterior distribution; the methods also make links to spline and wavelet smoothers and to a dual posterior distribution. Three automatic smoothing parameter selection…

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Testing for Differences Between Conditional Means in a Time Series Context

时间序列背景下条件均值差异的检验

In this article we study tests for equality of two regression curves when the inputs are driven by a time series. The basic process underlying the test statistics is the empirical process of the time series marked by the difference in the pertaining dependent variables. The main results hold under strict stationarity of the input variables, but no mixing condition or special modeling of the time series will be necessary. A simulation study is reported on, which illustrates…

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Monte Carlo Smoothing for Nonlinear Time Series

非线性时间序列的蒙特卡洛平滑

We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the…

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Testing for the Cointegrating Rank of a VAR Process with Level Shift at Unknown Time

A systems cointegration rank test is proposed that is applicable for vector autoregressive (VAR) processes with a structural shift at unknown time. The structural shift is modeled as a simple shift in the level of the process. It is proposed to estimate the break date first on the basis of a full unrestricted VAR model. Two alternative estimators are considered and their asymptotic properties are derived. In the next step the deterministic part of the process including the…

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