This article considers the familiar but very important problem of how to estimate the mean squared error (MSE) of seasonally adjusted and trend estimators produced by X-11-ARIMA or other decomposition methods. The MSE estimators are obtained by defining the unknown target components such as the trend and seasonal effects to be the hypothetical X-11 estimates of them that would be obtained if there were no sampling errors and the series were sufficiently long to allow the use of the symmetric filters embedded in the programme, which are time invariant. This definition of the component series conforms to the classical definition of the target parameters in design-based survey sampling theory, so that users should find it comfortable to adjust...
This is a brief survey of the existing seasonal adjustment methods. We first discuss the problem of ...
This is a brief survey of the existing seasonal adjustment methods. We first discuss the problem of ...
Modeling and accurately forecasting trend and seasonal patterns of a time series is a crucial activi...
Wayne Fuller is known for his outstanding contributions to three main areas in statistics: Sample su...
Most official seasonal adjustments are based on the X-11 method and its extensions. An important pro...
The presence of sampling error in an observed time series may obscure underlying features, such as s...
This Working Paper Series is intended to make the results of current research within the ABS availab...
Government statistical agencies are required to seasonally adjust non-stationary time series resulti...
Disclaimer: This report is released to inform interested parties of research and to encourage discus...
It is well known that the presence of error in reported time series can distort estimates of relatio...
: There are two different approaches for extracting the seasonal component from a ARIMA process. The...
In seasonal adjustment a time series is considered as a juxtaposition of several components, the tre...
There have been three primary methods in the world of seasonal adjustment. The first one is the X-12...
Present practice in applied time series work, mostly at economic policy or data producing agencies, ...
The paper considers stochastic linear trends in series with a higher than annual frequency of observ...
This is a brief survey of the existing seasonal adjustment methods. We first discuss the problem of ...
This is a brief survey of the existing seasonal adjustment methods. We first discuss the problem of ...
Modeling and accurately forecasting trend and seasonal patterns of a time series is a crucial activi...
Wayne Fuller is known for his outstanding contributions to three main areas in statistics: Sample su...
Most official seasonal adjustments are based on the X-11 method and its extensions. An important pro...
The presence of sampling error in an observed time series may obscure underlying features, such as s...
This Working Paper Series is intended to make the results of current research within the ABS availab...
Government statistical agencies are required to seasonally adjust non-stationary time series resulti...
Disclaimer: This report is released to inform interested parties of research and to encourage discus...
It is well known that the presence of error in reported time series can distort estimates of relatio...
: There are two different approaches for extracting the seasonal component from a ARIMA process. The...
In seasonal adjustment a time series is considered as a juxtaposition of several components, the tre...
There have been three primary methods in the world of seasonal adjustment. The first one is the X-12...
Present practice in applied time series work, mostly at economic policy or data producing agencies, ...
The paper considers stochastic linear trends in series with a higher than annual frequency of observ...
This is a brief survey of the existing seasonal adjustment methods. We first discuss the problem of ...
This is a brief survey of the existing seasonal adjustment methods. We first discuss the problem of ...
Modeling and accurately forecasting trend and seasonal patterns of a time series is a crucial activi...