1994 Fiscal Year Final Research Report Summary
Statistical Methods for the Seasonal Adjustment
| Project/Area Number |
04045056
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| Research Category |
Grant-in-Aid for international Scientific Research
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| Allocation Type | Single-year Grants |
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| Section | University-to-University Cooperative Research |
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
ISHIGURO Makio The Institute of Statistical Mathematics professor, 予測制御研究系, 教授 (10000217)
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| Co-Investigator(Kenkyū-buntansha) |
OTTO Marc c. Bureau of the Census Research Mathematical Statistician, Statistical Researc, Research M
CHEN Bor chung h. Bureau of the Census Research Mathematical Statistician, Statistical Researc, Research M
BELL William r. Bureau of the Census Principal Researcher, Statistical Researc, Principal
MONSELL Brian.c. Bureau of the Census Research Mathematical Statistician, Statistical Researc, Research M
FINDLEY David f. Bureau of the Census Principal Researcher, Statistical Researc, Principal
KAWASAKI Yoshinori The Institute of Statistical Mathematics assitant professor, 予測制御研究系, 助手 (70249910)
KITAGAWA Genshiro The Institute of Statistical Mathematics professor, 予測制御研究系, 教授 (20000218)
OZAKI Tohru The Institute of Statistical Mathematics professor, 予測制御研究系, 教授 (00000208)
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| Project Period (FY) |
1992 – 1994
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| Keywords | EIC / Multivariate time series analysis / Dynamic model / X-11 / Monte Carlo filter / Genetic Algoritm / Co-integration / AIC |
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| Research Abstract |
Topics covered in this study are as follows :
1. Use of Information Criterion EIC : Use of Extended Information Criterion EIC for the seasonal adjustment is discussed. It is demonstrated that the proposed resampling scheme shows a a natural tendency for chosing better trend for the out-of-sample forcasting.
2. Multivariate Economic Series Analysis : This study intended to analyze the role of sesonal adjustment procedure as a preprocessing technique for the multivariate timeseries analysis. It is reveald that there is danger of losing information about the mutual relationship among related series, when each series is adjusted for the seasonality and detrended separately.
3. X-11 type model : We tried to reconstruct the X-11 type trend estimate by the modern model-based seasonal adjustment method. We introduced a bias correction method which is to be used with conventional additive type model-based trend estimate. We also introduced a new model which has the X-11 type trend for multiplicati
… More
ve series. With the new model we can estimate the X-11 type trend directly from the data.
4. Monte Carlo filtering : A Monte Carlo filtering and smoothing methods have been developed for state estimation of high-dimensional nonlinear non-Gaussian state space models. Based on this methods, various models for seasonal adjustment are considered, e. g. , (1) detection of jumps of trend or seasonal components (2) treatment of outliers (3) estimation of multiplicative model (4) Baysian estimation of hyper-parametrs.
5. Genetic Algoritm : We investigate the relationships between the Genetic Algoritm and Monte Calro Filter. The major objective of this paper is to cast the Genetic Algorithm into the Baysian framework by its interpretation from a viewpoint of the Monte Carlo filter.
6. Improve of DECOMP : A procedure for extracting 'stable' stationary autoregressive component is proposed. in which we consider a numerical optimization with a restriction in frequency domain. Further research remained undone, however. especially in modeling jump and kink in trend.
7. Co-integration model : State-space representation for co-integration model is proposed which enables us to estimate the unknown parameters in one-step. while traditional Engle-Granger's method needs two steps.
8. X-12-REGARIMA : To improve its forcasting ability. AIC based regression model selection procedure is incorporated in the traditional moving-avarage based X-11 seasonal adjustment program. Less
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Research Products
(56 results)
