과제정보
이 연구는 통계청 사회통계작성 일반연구 지원을 받아 수행된 연구임(3000-3032-304-260-01).
참고문헌
- Box GEP and Jenkins GM (1970). Time Series Analysis Forecasting and Control, Holden-Day, Inc., San Francisco.
- Breiman L (2001). Random forests, Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
- Duan N (1983). Smearing estimate - A nonpar ametric retransformation method, Journal of the American Statistical Association, 78, 605-610. https://doi.org/10.1080/01621459.1983.10478017
- Dunn DM, Williams WH, and DeChaine TL (1976). Aggregate versus subaggregate models in local area forecasting, Journal of the American Statistical Association, 71, 68-71 https://doi.org/10.1080/01621459.1976.10481478
- Hamilton JD (1994). Time Series Analysis. Princeton University Press, Princeton.
- Hong Y and Park M (2019). A study on the linked time series methods according to the Household Income and Expenditure Survey Reorganization, SRI Open-Access Research Reports 2019.
- Hyndman RJ and Athanasopoulos G (2018). Forecasting: Principles and Practice (2nd Ed), OTexts.
- Hyndman RJ, Ahmed RA, Athanasopoulos G, and Shang HL (2011). Optimal combination forecasts for hierarchical time series, Computational Statistics and Data Analysis, 55, 2579-2589. https://doi.org/10.1016/j.csda.2011.03.006
- Kwiatkowski D, Phillips PCB, Schmidt P, and Shin Y (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?, Journal of Econometrics, 54, 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
- Kwon S and Hong Y (2019). A study on annual statistics production plans according to the Household Income and Expenditure Survey Reorganization, SRI Open-Access Research Reports 2019-21.
- Lim K and Park S (2016), A study on ways to improve Household Income and Expenditure Survey, Research on Improvement of Household Income and Expenditure Survey, p1-51, Statistics Research Institute.
- Orcutt GH, Watts HW, and Edwards JB (1968). Data aggregation and information loss, The American Economic Review, 58, 773-787
- Park M and Nassar M (2014). Variational Bayesian inference for forecasting hierarchical time series, Divergence Methods in Probabilistic Inference (DMPI) workshop at International Conference on Machine Learning (ICML), Beijing, China.
- Shlifer E and Wolff RW (1979). Aggregation and proration in forecasting, Management Science, 25, 594-603. https://doi.org/10.1287/mnsc.25.6.594
- Wickramasuriya SL, Athanasopoulos G, and Hyndman RJ (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114, 804-819 https://doi.org/10.1080/01621459.2018.1448825