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频域中位数时间序列AI 解读

This article discusses the median of a batch of time series. We focus on the frequency-domain median (FDM) series (Zeger 1985), which is calculated from the “mediancenters” (Gower 1974) of the discrete Fourier transforms. Like the univariate median, it has a breakdown value of 50%. Like the mean, the FDM time series of independent realizations from a stationary Gaussian process has a spectrum proportional to that of the component series. Analogs of the univariate median have…

同期季节调整AI 解读

Concurrent seasonal adjustment uses all information up to and including the current month's figure and thus should provide more accurate estimates of final seasonally adjusted data than the prevalent official method where the current month's component is forecasted from data through the preceding December. This article evaluates the expected gain from employing concurrent seasonal adjustment, including the case in which the data contain both nonseasonal and seasonal…

同期季节调整AI 解读

Concurrent seasonal adjustment uses all information up to and including the current month's figure and thus should provide more accurate estimates of final seasonally adjusted data than the prevalent official method where the current month's component is forecasted from data through the preceding December. This article evaluates the expected gain from employing concurrent seasonal adjustment, including the case in which the data contain both nonseasonal and seasonal…

频域中位数时间序列AI 解读

This article discusses the median of a batch of time series. We focus on the frequency-domain median (FDM) series (Zeger 1985), which is calculated from the “mediancenters” (Gower 1974) of the discrete Fourier transforms. Like the univariate median, it has a breakdown value of 50%. Like the mean, the FDM time series of independent realizations from a stationary Gaussian process has a spectrum proportional to that of the component series. Analogs of the univariate median have…

变点问题的秩检验AI 解读

We consider procedures based on quadratic form rank statistics to test for one or more changepoints in a series of independent observations. Models incorporating both smooth and abrupt changes are introduced. Various test statistics are suggested, their asymptotic null distributions are derived and tables of significance points are given. A Monte Carlo study shows that the asymptotic significance points are applicable to moderately sized samples.

一阶自回归参数的渐消加权估计AI 解读

In the estimation of autoregressive parameters, biases from ordinary least-squares or maximum likelihood estimator are big even for moderately large sample sizes. A new tapering estimation technique is developed to reduce the bias. Monte Carlo experiments show that the tapering estimation in the time domain yields a substantial reduction in the bias when autoregressive parameters are positive and not very near unity, over a wide range of sample sizes.

迈向自回归模型的统一渐近理论AI 解读

This paper develops an asymptotic theory for a first order autoregression with a root near unity. Deviations from the unit root theory are measured through a noncentrality parameter. When this parameter is negative we have a local alternative that is stationary; when it is positive, the local alternative is explosive; and when it is zero we have the standard unit root theory. Our asymptotic theory accommodates these alternatives and helps to unify earlier theory in which the…

协整向量最小二乘估计量的渐近性质AI 解读

Time series variables that stochastically trend together form a cointegrated system. OLS and NLS estimators of the parameters of a cointegrating vector are shown to converge in probability to the true parameter value at the rate T11d for any positive d. These estim mators can be written asymptotically in terms of relatively simple nonnormal random matrices which do not depend on the parameters of th e system. These asymptotic representations form the basis for simple and…

平稳过程谱密度的最小对比估计AI 解读

SUMMARY Let {z(n)} be a stationary process with mean zero and true spectral density g(λ). In this paper we approximate g(λ) by a parametric family f θ(λ) and propose a minimum contrast estimator θ^ which minimizes D(fθ,g^N)=∫−ππK{fθ(λ)/g^N(λ)}dλ with respect to θ, where K(.) is an appropriate function and ĝ N(λ) is a nonparametric spectral estimator of g(λ). We give the asymptotic distribution of θ^ and sufficient conditions such that θ^ is asymptotically efficient. Also, we…

交易日效应时间序列模型中的多重共线性问题AI 解读

This article discusses the multicollinearity problems associated with the estimation of time series models influenced by trading-day variation. An analysis of the design matrix is performed, and measures of the degree of multicollinearity are computed. The characteristics of the design matrix of a popular parameterization are also analyzed, and it is shown that in some cases use of this reparameterization significantly alleviates the multicollinearity problem.

预测小麦出口：汇率是否重要？AI 解读

A vector autoregression is fit to recent U.S. data on wheat prices, wheat export sales, wheat export shipments, and exchange rates. Forecast error decompositions and out-of-sample forecasts indicate that exchange rates have little influence on wheat sales and shipments.

过度差分与季节模型等价性：⊖=1 时的月度均值与季节 ARIMA 模型AI 解读

Two general models for monthly seasonal time series are considered, one in which seasonality is modeled with monthly means and another in which seasonality is modeled with a (0, 1, 1)12 ARIMA structure. The models are shown to be equivalent if the seasonal moving average parameter (⊖) is 1 and if the same assumptions about the 12 initial observations are made for both models. The role of the assumptions about the initial observations is analyzed, and it is argued that for…

含交易日变化的时间序列建模中的多重共线性问题AI 解读

Teresita S. Salinas, Steven C. Hillmer, Multicollinearity Problems in Modeling Time Series with Trading-Day Variation, Journal of Business & Economic Statistics, Vol. 5, No. 3 (Jul., 1987), pp. 431-436

未来需求的概率分布AI 解读

The bootstrapping technique of Efron (1979) is used to estimate the probability distribution of future electricity demand for Hydro Quebec. The application follows the regression approach of Freedman and Peters (1984a,b) but also allows for serially correlated disturbances and uncertainty in the independent variable forecasts.

组合预测：统一方法与最优性的再考察AI 解读

This article shows when the theoretical Lagrange multiplier solution for combining forecasts has a regression representation. This solution is not optimal in general because it imposes a restriction on an otherwise more general linear form. The optimal linear predictor based on N forecasts is presented. This predictor is or is not a regression function depending on whether the latter function is linear. I also show that the Lagrange multiplier solution may often be nearly…

学习曲线的新模型：移位自回归模型（DARM）AI 解读

This article introduces a new model for learning curves, a displaced autoregressive model (DARM). The model has theoretical and practical advantages over both the traditional exponential learning-curve model and the exponential leaming-curve model augmented by an autoregressive disturbance process. The DARM parameters can be related to the characteristics of the learning process and are easy to estimate. DARM appears to fit the data better than the alternative models,…

条件异方差时间序列模型AI 解读

Under the traditional linear time series or regression setting, the conditional variance of one-step-ahead prediction is time invariant. Experience in conjunction with data analysis, however, suggests that the variability of a process might well depend on the available information. This reality has motivated extensive research to relax the constant variance assumption imposed by the traditional linear time series model, and several classes of generalized parametric models…

模型季节调整的不确定性与极小极大滤波器构造AI 解读

Unobserved component autoregressive integrated moving average models are often the cornerstone of model-based seasonal adjustment procedures. Unfortunately these models are inherently underidentified and ad hoc assumptions must be made prior to the analysis. This article investigates the effect of seasonal adjustment filters on a class of observationally equivalent models. Bounds on the mean squared error (MSE) associated with arbitrary linear filters are derived. The…

模型季节调整的不确定性与极小极大滤波器构造AI 解读

Unobserved component autoregressive integrated moving average models are often the cornerstone of model-based seasonal adjustment procedures. Unfortunately these models are inherently underidentified and ad hoc assumptions must be made prior to the analysis. This article investigates the effect of seasonal adjustment filters on a class of observationally equivalent models. Bounds on the mean squared error (MSE) associated with arbitrary linear filters are derived. The…

有限观测下非平稳时间序列的信号提取AI 解读

For a signal plus noise model, where both signal and noise are generated by nonstation ary ARIMA, autoregressive integrated moving average, models, we use the transformation approach of Ansley & Kohn (1985) to construct an estimate of the signal and obtain its mean squared error given a finite number of observations. A method for efficient computation of the signal estimate and its mean squared error is also presented.

含外生变量的 AR(1) 模型中 t 与 F 统计量的有限样本分布AI 解读

The distributions of the test statistics are investigated in the context of an AR(1) model where the root is unity or near unity and where the exogenous process is a stable process, a random walk or a time trend. The finite sample distributions are estimated by Monte Carlo methods assuming normal disturbances. The sensitivity of the distributions to both the values of the parameters of the AR(1) model and the process generating the exogenous time series is examined. The…

含滞后因变量回归中的估计偏差AI 解读

We give an expression to order O( T -1 ), where T is the sample size, for bias to the estimated coefficient on a lagged dependent variable when all other regressors are exogenous. The general expression is a nonlinear function of the coefficient on the lagged dependent variable, the autoregressive structure of the exogenous variables, and the coefficients on the exogenous variables. The maximum bias that can arise is a linear function of the number of exogenous regressors in…

自回归模型中若干预测量的有限样本性质AI 解读

We compare the distributional properties of the four predictors commonly used in practice. They are based on the maximum likelihood, two types of the least squared, and the Yule-Walker estimators. The asymptotic expansions of the distribution, bias, and mean-squared error for the four predictors are derived up to O ( T −1 ), where T is the sample size. Examining the formulas of the asymptotic expansions, we find that except for the Yule-Walker type predictor, the other three…

自相关与随机性的 Durbin–Watson 及广义 Durbin–Watson 检验AI 解读

The Durbin–Watson (DW) test for lag 1 autocorrelation has been generalized (DWG) to test for autocorrelations at higher lags. This includes the Wallis test for lag 4 autocorrelation. These tests are also applicable to test for the important hypothesis of randomness. It is found that for small sample sizes a normal distribution or a scaled beta distribution by matching the first two moments approximates well the null distribution of the DW and DWG statistics. The…