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检测分布变化的最优停止时间AI 解读

It is shown that Page's stopping time is optimal for the detection of changes in distributions, in a well defined sense. This work is a generalization of an existing result where it was shown that Page's stopping time is optimal asymptotically.

利用时间序列模型联合预测美国婚内生育、婚姻、出生与结婚AI 解读

This article presents a new approach to forecasting U.S. marital fertility, nuptiality, births, and marriages. The analysis represents a wedding of demographic and statistical time series in models amenable to Box-Jenkins techniques of model identification, estimation, diagnosis, and forecasting. The models demonstrate the advantages in this approach in forecasting both rates and events as opposed to the common practice of simply forecasting events. Using the best models of…

理解计量经济学中的伪回归AI 解读

This paper provides an analytical study of linear regressions involving the levels of economic time series. An asymptotic theory is developed for regressions that relate quite general integrated random processes. This includes the spurious regressions of Granger and Newbold (1974) and the recent cointegrating regressions of Granger and Engle (1985). An asymptotic theory is developed for the regression coefficients and for conventional significance tests. It is shown that the…

含滞后因变量模型中 Durbin–Watson 统计量原假设分布的近似AI 解读

We consider testing for autoregressive disturbances in the linear regression model with a lagged dependent variable. An approximation to the null distribution of the Durbin—Watson statistic is developed using small-disturbance asymptotics, and is used to obtain test critical values. We also obtain nonsimilar critical values for the Durbin—Watson and Durbin's h and t tests. Monte Carlo results are reported comparing the performances of the tests under the null and alternative…

合并横截面重复序列中的时间序列分析AI 解读

This article proposes the use of spectral methods to pool cross-sectional replications ( N ) of time series data ( T ) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article…

混合存量与流量数据下含外生变量的高阶连续时间动态模型估计AI 解读

This article extends recent work on the Gaussian or quasi-maximum likelihood estimation of the parameters of a closed higher-order continuous time dynamic model by introducing exogenous variables into the model The method presented yields exact maximum likelihood estimates when the innovations are Gaussian and the exogenous variables are polynomials in time of degree not exceeding two, and it can be expected to yield very good estimates under more general conditions. It is…

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季节振幅变化下的时间序列季节调整方法AI 解读

The official seasonally adjusted figures of the unemployment series in the Netherlands proved to be unsatisfactory in the years 1976 until 1980 because of the occurrence of a residual seasonal pattern in the adjusted series. There is indication that this failure is due to the presence of variations in the seasonal amplitude of the unemployment series. To improve this unsatisfactory state of affairs further research on methods of seasonal adjustment was undertaken at the…

初步数据误差及其对联立方程模型预测误差的影响AI 解读

Econometric models, especially when designed for forecasting purposes, tend to use updated economic series, the last figures(s) of which embed first-published or preliminary data errors. This article identifies and assesses the contribution of errors in preliminary data to the forecast error and to the forecast error variance of linear dynamic simultaneous equation models. The effect of preliminary data errors is shown to be pervasive, although not necessarily weighty. The…

抽样未来：单变量时间序列模型预测的贝叶斯方法AI 解读

The Box–Jenkins methodology for modeling and forecasting from univariate time series models has long been considered a standard to which other forecasting techniques have been compared. To a Bayesian statistician, however, the method lacks an important facet—a provision for modeling uncertainty about parameter estimates. We present a technique called sampling the future for including this feature in both the estimation and forecasting stages. Although it is relatively easy…

季节振幅变化下的时间序列季节调整方法AI 解读

H. van der Hoeven, A. J. Hundepool, A Method for Seasonally Adjusting Time Series with Variation in the Seasonal Amplitude, Journal of Business & Economic Statistics, Vol. 4, No. 4 (Oct., 1986), pp. 455-471

季节差分指数平滑的一则注记AI 解读

Filtering economic time series can be justified if variations within a certain frequency interval are relevant for the problem at hand. It is shown here that exponential smoothing of seasonal differences provides a simple means of damping short oscillations, including seasonal ones.

使用单边滤波器进行时间序列季节调整AI 解读

This article presents a model-based signal extraction seasonal adjustment procedure to extract estimates of the independent unobserved seasonal and nonseasonal components from an observed time series. The decomposition yields a one-sided filter that is optimal for adjusting the most recent observation under the assumption of using only the past observed series. Some advantages of this procedure are that no forecasts are required for implementation and there are no problems…

利用预期产出调查数据的英国制造业就业计量模型AI 解读

Abstract A model of U.K. manufacturing employment is estimated in which output expectations data are derived from Confederation of British Industry (CBI) survey information. The output expectations terms are highly significant, and equations including them encompass more traditional models that use current and lagged output. In addition, the equations also successfully predict the sharp falls in manufacturing employment that occurred after 1979. One interesting implication…

非高斯状态空间模型及其纪录预测应用AI 解读

SUMMARY We develop a class of state space models for censored data. The basic model assumes an exponential distribution for the observations, conditionally on unobserved state variables. The model may be generalised by allowing transformations. We develop an application to the prediction of records. This is illustrated with some athletics data, though we also discuss briefly the possibility of more general applications connected with extreme values.

贝叶斯模型监测AI 解读

SUMMARY A simple method of monitoring the predictive performance of a class of Bayesian models is introduced. The models involve sequential analyses of sequences of observations and are appropriate for a variety of monitoring and forecasting applications. For any given model, the monitoring technique is based on a comparison of the predictive ability of the model, measured by the observed values of predictive densities, with that of a single alternative. The alternative is…

区分时间序列中的异常值类型AI 解读

SUMMARY Distinguishing an outlier in a time series arising through measurement error from one arising through a perturbation of the underlying system can be of use in data validation. In this paper a method of testing for the presence of an outlier of unknown type is proposed. Then the properties of a rule based on the likelihood ratio which attempts to distinguish the two types of outlier are examined and compared with those of the corresponding Bayes rules. An example…

线性过程误差回归中 GLSE 与 MLE 的二阶风险结构AI 解读

In a regression model with an error that is a general linear process, the second-order expansion of the risk matrix of GLSE or MLE is obtained. a set of sufficient conditions for the effect of estimating the structural parameter of the linear process to vanish in the above expasion is obtained. The relation of the covariance matrix of SLSE with those of GLSE and MLE up to $O(T^-2)$ is elucidated.

一阶自回归中的最小距离估计与拟合优度检验AI 解读

This paper gives a class of minimum $L_2$-distance estimators of the autoregression parameter in the first-order autoregression model when the errors have an unknown symmetric distribution. Within the class an asymptotically efficient estimator is exhibited. The asymptotic efficiency of this estimator relative to the least-squares estimator is the same as that of a certain signed rank estimator relative to the sample mean in the one sample location model. The paper also…

一阶自回归过程的最小距离估计AI 解读

In this paper we construct a class of minimum distance Cramer-von Mises-type estimators for the parameter in the first-order stationary autoregressive time series. The estimator is proved to be asymptotically normal under appropriate assumptions. The proofs involve some results of independent interest.

时间序列的影响泛函AI 解读

A definition is given for influence functionals of parameter estimates in time-series models. The definition involves the use of a contaminated observations process of the form $y^\gamma_t=(1-z^\gamma_t){x_t+z^\gamma_tw_t}$, $p=1,2,...,0\leq\gamma\leq1$ where $x_t$ is a core process (usually Gaussian), $w_t$ is a contaminating process, and $z^\gamma_t"$ is a zero-one process with $P(z^\gamma_t=1)={\gamma+0(\gamma)}$. This form is sufficiently general to model such diverse…

利用子序列统计量估计平稳序列中一般统计量的方差AI 解读

Let $Z_i:-\infty<i <+\infty}$ be a strictly stationary $\infty$ -mixing sequence. Without specifying the dependence model giving rise to ${Z_i}$ and without specifying the marginal distribution of $Z_i$ , we address the question of variance estimation for a general statistic $t_n=t_n (Z_1,...,Z_n)$. For estimating $Var{t_n}$ from just the available data $(Z_1,...,Z_n)$ we propose computing subseries values: $t_m(Z_{i+1},Z_{i+2}, Z_{i+m})$, $0\leq i

贝叶斯预测模型的监控与适应AI 解读

Practical aspects of a new technique for monitoring and controlling the predictive performance of Bayesian forecasting models are discussed. The basic features of the approach to model monitoring introduced in a general setting in West (1986) are described and extended to a wide class of dynamic, nonnormal, and nonlinear Bayesian forecasting models. An associated method of automatically detecting and rejecting outliers and adapting models to abrupt structural changes in the…

平稳自回归过程加权平均估计量的效率AI 解读

The criterion of second-order efficiency is used to distinguish among estimators, which have the same asymptotic variance, of the mean of a stationary autoregressive process. The best linear unbiased estimator is typically unknown, since it depends on the parameters of the process. It is demonstrated by second-order efficiency that the sample mean performs poorly under certain conditions, whereas some weighted averages maintain a more consistent performance as the parameters…