论文检索

移动平均过程新息表示的收敛速度AI 解读

A moving average process of order q can be written as a linear combination of the (q+1) most recent innovations. We show that if the moving average polynomial has roots on the unit circle, then the coefficients of the innovations tend to the true moving average coefficients, the difference between the two being O (1/t), where t is the number of observations we consider. The results are extended to mixed autoregressive moving average models and multivariate models.

时间序列回归模型中的高效估计与预测AI 解读

For a time series regression model with correlated residuals we show how to compute efficiently the likelihood, predict future values and interpolate missing values. As an illustration we consider a regression model with autoregressive moving average residuals.

移动平均过程创新表示的收敛速度AI 解读

SUMMARY A moving average process of order q can be written as a linear combination of the (q+ 1) most recent innovations. We show that if the moving average polynomial has roots on the unit circle, then the coefficients of the innovations tend to the true moving average coefficients, the difference between the two being O(l/t), where t is the number of observations we consider. The results are extended to mixed autoregressive moving average models and multivariate models.

季节移动平均模型的卡尔曼滤波注记AI 解读

We show that if we apply the Kalman filter to a pure seasonal moving average model with no missing observations, then, by using a result of Ansley (1979), we can obtain significant computational savings.

摘要暂缺

《经济时间序列季节调整涉及的问题》评论：季调序列频谱中的凹陷AI 解读

Craig F. Ansley, William E. Wecker, [Issues Involved with the Seasonal Adjustment of Economic Time Series]: Comment: On Dips in the Spectrum of a Seasonally Adjusted Time Series, Journal of Business & Economic Statistics, Vol. 2, No. 4 (Oct., 1984), pp. 323-324

季节移动平均模型的卡尔曼滤波札记AI 解读

We show that if we apply the Kalman filter to a pure seasonal moving average model with no missing observations, then, by using a result of Ansley (1979), we can obtain significant computational savings.

部分数据缺失或聚合时状态空间模型的固定区间估计AI 解读

Ansley & Kohn's (1982) geomertical derivation of the Kalman filter fixed-interval smoothing algorthm is extended to the case where some of the observations are missing or aggregated and the state covariance matrix may be singular. Estimates of missing observations are also obtained. The theory is then applied to the interpolation of missing data in mulativariate autoregressive-moving average modles.

摘要暂缺

摘要暂缺

非线性回归与样条平滑的信号提取方法AI 解读

This article shows how to fit a smooth curve (polynomial spline) to pairs of data values (yi, xi ). Prior specification of a parametric functional form for the curve is not required. The resulting curve can be used to describe the pattern of the data, and to predict unknown values of y given x. Both point and interval estimates are produced. The method is easy to use, and the computational requirements are modest, even for large sample sizes. Our method is based on maximum…

部分数据缺失或聚合时状态空间模型的固定区间估计AI 解读

Ansley & Kohn's (1982) geomertical derivation of the Kalman filter fixed-interval smoothing algorthm is extended to the case where some of the observations are missing or aggregated and the state covariance matrix may be singular. Estimates of missing observations are also obtained. The theory is then applied to the interpolation of missing data in mulativariate autoregressive-moving average modles.

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.

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.

预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…

预测均方误差估计中的偏差AI 解读

We examine the forecasting problem for a finite series of observations from either a nonseasonal or a seasonal autoregressive-moving average process. Four sources of bias in the usual estimator of forecast mean squared error are identified and analyzed for particular models using maximum likelihood and least squares parameter estimates. The usual estimator is found to be biased downwards, especially near the boundary of a stationary or invertible region, and the bias is…

摘要暂缺

自回归移动平均模型中残差自相关的有限样本分布AI 解读

We examine by simulation the finite sample properties of tests of model adequacy based on individual residual autocorrelations, and of portmanteau statistics based on several residual autocorrelations, for autoregressive-moving average models. Both nonseasonal and seasonal models are considered.

摘要暂缺

ARMA 模型残差自相关的有限样本分布AI 解读

We examine by simulation the finite sample properties of tests of model adequacy based on individual residual autocorrelations, and of portmanteau statistics based on several residual autocorrelations, for autoregressive-moving average models. Both nonseasonal and seasonal models are considered.

混合自回归移动平均过程精确似然的计算算法AI 解读

The likelihood function for an autoregressive-moving average process is obtained by transforming the process to obtain a band covariance matrix whose Cholesky decomposition can be readily computed. Refinements are given for pure autoregressive and multiplicative seasonal processes. Evidence is presented to show that this approach allows more efficient computation than other methods proposed in the literature.

摘要暂缺