Grant-in-Aid for international Scientific Research
|Allocation Type||Single-year Grants|
|Section||University-to-University Cooperative Research|
|Research Institution||The Institute of Statistical Mathematics|
KITAGAWA Genshiro The Inst.of Statist.Math., Professor, 予測制御研究系, 教授 (20000218)
KAWASAKI Yoshinori The Inst.of Statist.Math.Assist.Prof., 予測制御研究系, 助手 (70249910)
HIGUCHI Tomoyuki The Inst.of Statist.Math.Assoc.Prof., 予測制御研究系, 助教授 (70202273)
TAMURA Yoshiyasu The Inst.of Statist.Math.Professor, 統計計算開発センター, 教授 (60150033)
ISHIGURO Makio The Inst.of Statist.Math.Professor, 予測制御研究系, 教授 (10000217)
OZAKI Tohru The Inst.of Statist.Math.Professor, 予測制御研究系, 教授 (00000208)
FINDLEY Davi Bureau of the Census, SRD, Principal
佐藤 整尚 統計数理研究所, 予測制御研究系, 助手 (60280525)
OTTO Mark C. Bureau of the Census, Statistical Researc, Research M
CHEN BorーChu Bureau of the Census, Statistical Researc, Research M
MONSELL Bria Bureau of the Census, Statistical Researc, Research M
KRAMER Matth Bureau of the Census, Statistical Researc, Research M
BELL William Bureau of the Census, Statistical Researc, Principal
瀧澤 由美 統計数理研究所, 予測制御研究系, 助教授 (90280528)
|Project Period (FY)
1996 – 1998
Completed(Fiscal Year 1998)
|Budget Amount *help
¥4,000,000 (Direct Cost : ¥4,000,000)
Fiscal Year 1998 : ¥1,300,000 (Direct Cost : ¥1,300,000)
Fiscal Year 1997 : ¥1,700,000 (Direct Cost : ¥1,700,000)
Fiscal Year 1996 : ¥1,000,000 (Direct Cost : ¥1,000,000)
|Keywords||Time Series Analysis / State space model / Nonlinear / Non-Gaussian model / Kalman filter / Monte Carlo filter / Smoothing / Software for Web / 平滑化 / 経済データ / モデル選択 / トレンド / 季節成分 / 情報量規準|
The objective of this research was to render the theoretical considerations to the various problems in statistical seasonal adjustment, and to develop new methods based on the state-of-art techniques in modern time series analysis. The summary of our research report follows.
(1)Development of new procedures
Seasonal adjustment methods based on Monte Carlo filter and smoother, and on dynamical system approach were proposed. Dynamic X-11 model successfully gives explicit model form to X-11, which is defined in an essentially non-parametric way. Monte Carlo filter/smoother deals with higher-dimensional seasonal adjustment problem allowing non-linearity and non-Gaussianity, which was next to impossible by the existing statistical methods. Thsi method was applied to the seasonal adjustment of small count data which seemingly contains quasi-periodic components.
(2)Enhancement and characterization of seasonal models
Macroeconomic system was estimated based on the multivariate seasonal adjustment. These kind of composite seasonal adjustment could be more important in the near future. Extension of seasonal models to time-space data was also considered. Finally, the research on the characterization of linear Gaussian model based seasonal adjustment was done in terms of the optimality of seasonal adjustment. Comparative study between DECOMP and X-12 ARTMA was also performed through simulations.
The most successfull software product is Web-DECOMP.Users are free from instaling softwares on local disks or free from recompilation of software codes, but they simply can execute seasonal adjustment program DECOMP through internet browsers. On the other hands, U.S.Bureau of the Census almost completed their new version of software, X-12-ARIMA.The most updated version and its documentation (but not the official release yet at present) can be downloaded via their ftp site (ftp.census.gov).