论文检索

若干常用时间序列估计量的偏差AI 解读

We study the bias of Yule-Walker and least squares estimates for univariate and multivariate autoregressive processes. We obtain explicit formulae for the large-sample bias of YuleWalker estimates in the scalar first- and second-order cases and for least squares estimates in the general case. Both simulations and theory indicate that Yule-Walker estimates are inferior to least squares estimates. For strongly autocorrelated processes, Yule-Walker estimates can be severely…

时间序列模型的后验赔率AI 解读

This paper is concerned with model selection in time series analysis. A Bayesian approach is adopted to investigate various decision criteria previously advocated. The discussion is conducted in the context of a class of nonlinear time series models of sufficient generality to encompass a wide range of processes currently of interest.

多元自回归指数模型的若干结果AI 解读

We discuss methods for modelling multivariate autoregressive time series in terms of a smaller number of index series which are chosen to provide as complete a summary as possible of the past information contained in the original series necessary for prediction purposes. The maximum likelihood method of estimation and asymptotic properties of estimators of the coefficients which determine the index variables, a well as the corresponding autoregressive coefficients, are…

列联表观测的序列依赖及卡方统计量修正AI 解读

The asymptotic behaviour of the Pearson goodness-of-fit test and of the test of independence in 2 × 2 tables is examined when the data are generated by Markov dependent sequences. The results allow simple quantification of the effects of such serial dependence on some standard test statistics.

高斯 ARMA 过程最大似然估计量的协方差矩阵计算AI 解读

We present a simple procedure for deriving the covariance matrix for the maximum likelihood estimator of a Gaussian autoregressive-moving average process. An illustration of the procedure is given.

缺失或聚合数据下向量 ARMA 过程的精确似然AI 解读

This note points out that by using the Kalman filter with nonconstant coefficients, we can compute the exact likelihood of an autoregressive-moving average process observed with noise, when some of our observations are either missing or aggregated.

若干时间序列模型的最优与渐近最优设计札记AI 解读

The problem of regression design in the presence of correlated errors is considered. Under the assumption that derivative information is available on the response process, uniqueness results for optimal designs given in Eubank, Smith and Smith (1981) for the BLUE of the regression coefficient are extended to a wider class of processes. In the event that derivatives cannot be sampled, an asymptotic solution is developed and extended to the multiparameter setting.

季节性时间序列模型中非平稳参数设定的检验AI 解读

Let $Y_t$ be an autoregressive process satisfying $Y_t = \alpha_1 Y_{t - 1} + \alpha_2 Y_{t - d} + \alpha_3 Y_{t - d - 1} + e_t$, where $\{e_t\}^\infty_{t = 0}$ is a sequence of $\operatorname{iid}(0, \sigma^2)$ random variables and $d \geq 2$. Such processes have been used as parametric models for seasonal time series. Typical values of $d$ are 2, 4, and 12 corresponding to time series observed semi-annually, quarterly, and monthly, respectively. If $\alpha_1 = 1, \alpha_2 = 1, \alpha_3 = - 1$…

观测依赖对卡方拟合检验的影响AI 解读

Tests of fit that assume i.i.d. observations may be affected by dependence among the observations. This effect is studied for the Pearson chi squared test when the data form a stationary stochastic process. The general form of asymptotic distribution theory under the null hypothesis is outlined and examples are given. For testing fit to a specified normal law, it is shown that when observations come from a quite general class of Gaussian stationary processes, positive…

非对称滤波器对季节因子修订的影响AI 解读

Recent data seasonally adjusted by moving average methods are subject to revisions due to differences in the properties of the linear filters for the same seasonal adjustment when later data become available. This article introduces a measure of the total revision associated with the concurrent and forecasting seasonal filters and applies it to the Census Method II-X-11 variant and the X-11-ARIMA method. To consider the fact that the spectrum of many economic indicators…

非对称滤波器对季节因子修订的影响AI 解读

Recent data seasonally adjusted by moving average methods are subject to revisions due to differences in the properties of the linear filters for the same seasonal adjustment when later data become available. This article introduces a measure of the total revision associated with the concurrent and forecasting seasonal filters and applies it to the Census Method II-X-11 variant and the X-11-ARIMA method. To consider the fact that the spectrum of many economic indicators…

美国生育率的联立方程分析：评论AI 解读

Comments on a study by Conger and Campbell which postulated a 6 equation dynamic model including a fertility equation, estimated by 2 stage least squares on the basis of aggregate data for the U.S. The authors present a table with 3 sets of estimates for each of the 6 structural equations specified in the earlier study; 2 stage least squares estimates obtained for 1946-70 and 1946-76 and the estimates obtained by Conger and Campbell are indicated. The authors report that…

建设时滞与总量波动AI 解读

The equilibrium growth model is modified and used to explain the cyclical variances of a set of economic time series, the covariances between real output and the other series, and the autocovariance of output. The model is fitted to quarterly data for the post-war U.S. economy. Crucial features of the model are the assumption that more than one time period is required for the construction of new productive capital, and the non-time-separable utility function that admits…

混合分布时间序列的分析AI 解读

Some stationary and non-stationary time series arise from mixed distributions, the probabilities attached to the occurrence of certain values being positive, while a continuum of possible values is also involved. Such series are modeled in terms of a stationary Gaussian process $X_t$, which is censored when it crosses certain thresholds. Procedures are proposed for estimating the autocorrelation function of $X_t$. Their strong consistency and asymptotic normality are…

利用高阶过轴次数辨别时间序列AI 解读

A new methodology is proposed for discrimination among stationary time-series. The time series are transformed into binary arrays by clipping (retaining only the signs of) the $j$th difference series, $j = 0, 1, 2, \cdots$. The degeneracy of clipped $j$th differences is studied as $j$ becomes large. A new goodness of fit statistic is defined as a quadratic form in the counts of axis-crossings by each of the first $k$ differences of the series. Simulations and the degeneracy…

自相关、自回归与自回归逼近AI 解读

Theorems are proved relating to the rate of almost sure convergence of autocovariances, and hence autocorrelations, to their true values. These rates are uniform in the lag up to some order $P(T)$, increasing with $T$. The key assumption is that the process is stationary and the best linear predictor is the best predictor. In particular for an ARMA process and $P(T) = O\{(\ln T)^a\}, a < \infty$, the rate is $O\{(\ln \ln T/T)^{1/2}\}$. These results are used to discuss…

利用 Akaike 准则选择经济时间序列模型AI 解读

In this article we use the procedures introduced by Akaike (1974, 1976) and Schwarz (1978) to investigate the optimal specification of autoregressive moving average (ARMA) models for a group of major economic time series (see also Fine and Hwang 1979; Sawa 1978; Kashyap 1977; Leamer 1979; Chow 1979). The basic question we ask is whether the short AR representations that are estimated on an ad hoc basis in economics are validated by these more objective criteria. The article…

月度时间序列的日历效应：建模与调整AI 解读

Most monthly time series that represent a total of some variable for each month contain calendar effects due to changing month length, day-of-the-week effects, and holidays. It is important to remove the calendar variation to allow an effective assessment of the variation due to important factors. New procedures for calendar adjustment are presented in this article. A plausible model for the daily data is used to derive a model for the monthly data in which the power…

月度时间序列的日历效应：建模与调整AI 解读

Most monthly time series that represent a total of some variable for each month contain calendar effects due to changing month length, day-of-the-week effects, and holidays. It is important to remove the calendar variation to allow an effective assessment of the variation due to important factors. New procedures for calendar adjustment are presented in this article. A plausible model for the daily data is used to derive a model for the monthly data in which the power…

相关与谱分析的工程应用AI 解读

Discusses engineering applications and recent developments based upon correlation and spectral analysis. Illustrations deal with applications to acoustics, mechanical vibrations, system identification, and fluid dynamics problems in aerospace, automotive, industrial noise control, civil engineering and oceanographic fields, as well as similar problems in other fields. Tackles problems and solutions, assuming reader has required hardware and software to compute estimates of…

修订与勘误：ARMA过程样本逆自相关的渐近分布AI 解读

J. R. M. Hosking, Amendments and Corrections: The Asymptotic Distribution of the Sample Inverse Autocorrelations of an Autoregressive Moving-Average Process, Biometrika, Vol. 69, No. 2 (Aug., 1982), p. 491

高斯移动平均过程参数的最大似然估计AI 解读

Several authors have described methods for computing the exact maximum likelihood estimates for the parameters of a Gaussian moving average process. In this paper, an iterative procedure for solving the likelihood equations is derived which includes the covariance determinant. The procedure expresses the estimator of each parameter as a linear combination of a suitably large set of sample serial correlations which has many of the computational advantages of the direct…

固定区间平滑算法的几何推导AI 解读

The Kalman filter fixed-interval smoothing algorithm is derived from geometrical considerations. This approach is considerably simpler than standard methods.

季节移动平均模型中缺项检验的一则札记AI 解读

A. C. Harvey, J. Tomenson; A Note on Testing for Gaps in Seasonal Moving Average Models, Journal of the Royal Statistical Society Series B: Statistical Met