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http://dx.doi.org/10.5351/KJAS.2022.35.4.553

A study on time series linkage in the Household Income and Expenditure Survey  

Kim, Sihyeon (Department of Statistics, Chung-Ang University)
Seong, Byeongchan (Department of Statistics, Chung-Ang University)
Choi, Young-Geun (Department of Statistics, Sookmyung Women's University)
Yeo, In-kwon (Department of Statistics, Sookmyung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.4, 2022 , pp. 553-568 More about this Journal
Abstract
The Household Income and Expenditure Survey is a representative survey of Statistics Korea, which aims to measure and analyze national income and consumption levels and their changes by understanding the current state of household balances. Recently, the disconnection problem in these time series caused by the large-scale reorganization of the survey methods in 2017 and 2019 has become an issue. In this study, we model the characteristics of the time series in the Household Income and Expenditure Survey up to 2016, and use the modeling to compute forecasts for linking the expenditures in 2017 and 2018. In order to evenly reflect the characteristics across all expenditure item series and to reduce the impact of a specific forecast model, we synthesize a total of 8 models such as regression models, time series models, and machine learning techniques. In particular, the noteworthy aspect of this study is that it improves the forecast by using the optimal combination technique that can exactly reflect the hierarchical structure of the Household Income and Expenditure Survey without loss of information as in the top-down or bottom-up methods. As a result of applying the proposed method to forecast expenditure series from 2017 to 2019, it contributed to the recovery of time series linkage and improved the forecast. In addition, it was confirmed that the hierarchical time series forecasts by the optimal combination method make linkage results closer to the actual survey series.
Keywords
Household Income and Expenditure Survey; time series disconnection; hierarchical time series forecasts; optimal combination;
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  • Reference
1 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.
2 Hyndman RJ and Athanasopoulos G (2018). Forecasting: Principles and Practice (2nd Ed), OTexts.
3 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.   DOI
4 Duan N (1983). Smearing estimate - A nonpar ametric retransformation method, Journal of the American Statistical Association, 78, 605-610.   DOI
5 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   DOI
6 Orcutt GH, Watts HW, and Edwards JB (1968). Data aggregation and information loss, The American Economic Review, 58, 773-787
7 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.
8 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   DOI
9 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.
10 Breiman L (2001). Random forests, Machine Learning, 45, 5-32.   DOI
11 Shlifer E and Wolff RW (1979). Aggregation and proration in forecasting, Management Science, 25, 594-603.   DOI
12 Hamilton JD (1994). Time Series Analysis. Princeton University Press, Princeton.
13 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.   DOI
14 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.
15 Box GEP and Jenkins GM (1970). Time Series Analysis Forecasting and Control, Holden-Day, Inc., San Francisco.