TY - JOUR
AU1 - Dagum, Estela Bee
AB - Modelling, Forecasting and Seasonally Adjusting Economic Time Series with the X-11 ARIMA Method ESTELA BEE DAGUM Seasonal Adjustment and Time Series Analysis Staff, Statistics Canada, Ottawa, Canada KI A OT6t The majority of seasonal adjustment methods, officially adapted by government statistical agencies, belong to the category of techniques based on linear smoothing filters, usually known as moving averages of length 2m+ 1, say. These methods have often been criticized because they lack an explicit model concerning the decomposition of the original series and because their estimates, for observations in the most recent years, do not have the same degree of reliability as those of central observations. The lack of an explicit model refers to the whole length of the series, for moving averages procedures do, of course, make assumptions con­ cerning the time series components, but only for within the span of the set of weights of the moving average; that is, the assumptions are of a local character. The second limitation is inherent to all linear smoothing procedures, since the m first and last observations cannot be smoothed with the same set of symmetric weights as are applied to central observations. Because of this, the estimates for current 
TI - Modelling, Forecasting and Seasonally Adjusting Economic Time Series with the X-11 Arima Method
JF - Journal of the Royal Statistical Society Series D: The Statistician
DO - 10.2307/2988184
DA - 2018-12-05
UR - https://www.deepdyve.com/lp/oxford-university-press/modelling-forecasting-and-seasonally-adjusting-economic-time-series-3O1NXAOPe9
SP - 203
EP - 216
VL - 27
IS - 3-4
DP - DeepDyve
ER -