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时间序列非线性序列依赖的熵检验AI 解读

We propose tests for nonlinear serial dependence in time series under the null hypothesis of general linear dependence, in contrast to the more widely studied null hypothesis of independence. The approach is based on combining an entropy dependence metric, which possesses many desirable properties and is used as a test statistic, with a suitable extension of surrogate data methods, a class of Monte Carlo distribution-free tests for nonlinearity, and a smoothed sieve…

再生核希尔伯特空间中的 Henderson 平滑器AI 解读

The Henderson smoother has been traditionally applied for trend-cycle estimation in the context of nonparametric seasonal adjustment software officially adopted by statistical agencies. This study introduces a Henderson third-order kernel representation by means of the reproducing kernel Hilbert space (RKHS) methodology. Two density functions and corresponding orthonormal polynomials have been calculated. Both are shown to give excellent representations for short- and…

通过信号提取对调查数据进行基准校准的非参数方法AI 解读

This article introduces a nonparametric method to estimate the covariance matrix for the stationary part of the signal (hidden in data), to enable benchmarking via signal extraction. Some discussions and simulations are carried out to compare the proposed benchmarking method to the regression method development by Cholette and Dagum and the signal extraction method developed by Hillmer and Trabelsi suggesting autoregression integrated moving average (ARIMA) models for the…

通过信号提取对调查数据进行基准校准的非参数方法AI 解读

This article introduces a nonparametric method to estimate the covariance matrix for the stationary part of the signal (hidden in data), to enable benchmarking via signal extraction. Some discussions and simulations are carried out to compare the proposed benchmarking method to the regression method development by Cholette and Dagum and the signal extraction method developed by Hillmer and Trabelsi suggesting autoregression integrated moving average (ARIMA) models for the…

摘要暂缺

移动平均季节调整方法中趋势—周期估计量的修订AI 解读

One of the main purposes of the seasonal adjustment of economic time series is to provide information on current economic conditions, particularly to determine the state of the cycle at which the economy stands. Since seasonal adjustment means removing seasonal variations, thus leaving a seasonally adjusted series consisting of trend cycle together with the irregular fluctuations, it is often very difficult to detect cyclical turning points for series strongly contaminated…

摘要暂缺

《经济时间序列季节调整涉及的问题》评论AI 解读

Estela Bee Dagum, Normand J. D. Laniel, [Issues Involved with the Seasonal Adjustment of Economic Time Series]: Comment, Journal of Business & Economic Statistics, Vol. 2, No. 4 (Oct., 1984), pp. 328-332

加拿大消费者价格指数季节调整的基本问题AI 解读

This study is mainly concerned with basic issues that arise in connection with the seasonal: adjustment of the Canadian Consumer Price Index when used as a current indicator of inflation. It analyzes the seasonal characteristics of the series and evaluates whether a direct or an indirect adjustment is preferred from the viewpoint of the degree of smoothness of the monthly rate of change. The use of ARIMA extrapolations versus no ARIMA extrapolations and the application of…

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…