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Bootstrap Prediction Intervals of Temporal Disaggregation

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  • Bu Hyoung Lee

    (Department of Mathematics and Statistics, Loyola University Maryland, 4501 N. Charles Street, Baltimore, MD 21210, USA)

Abstract

In this article, we propose an interval estimation method to trace an unknown disaggregate series within certain bandwidths. First, we consider two model-based disaggregation methods called the GLS disaggregation and the ARIMA disaggregation. Then, we develop iterative steps to construct AR-sieve bootstrap prediction intervals for model-based temporal disaggregation. As an illustration, we analyze the quarterly total balances of U.S. international trade in goods and services between the first quarter of 1992 and the fourth quarter of 2020.

Suggested Citation

  • Bu Hyoung Lee, 2022. "Bootstrap Prediction Intervals of Temporal Disaggregation," Stats, MDPI, vol. 5(1), pages 1-13, February.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:1:p:13-202:d:752373
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    References listed on IDEAS

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    1. Luiz K. Hotta & Klaus L. Vasconcellos, 1999. "Aggregation and Disaggregation of Structural Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(2), pages 155-171, March.
    2. Daniel O. Stram & William W. S. Wei, 1986. "Temporal Aggregation In The Arima Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(4), pages 279-292, July.
    3. Alonso, Andrés M. & Peña, Daniel & Romo, Juan, 2003. "On sieve bootstrap prediction intervals," Statistics & Probability Letters, Elsevier, vol. 65(1), pages 13-20, October.
    4. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    5. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    6. Chen, Cathy W.S. & Lee, Sangyeol, 2016. "Generalized Poisson autoregressive models for time series of counts," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 51-67.
    7. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-476, August.
    8. Alonso, Andres M. & Sipols, Ana E., 2008. "A time series bootstrap procedure for interpolation intervals," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1792-1805, January.
    9. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    10. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    11. Daniel O. Stram & William W. S. Wei, 1986. "A Methodological Note On The Disaggregation Of Time Series Totals," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(4), pages 293-302, July.
    12. Bu Hyoung Lee & William W. S. Wei, 2017. "The use of temporally aggregated data on detecting a mean change of a time series process," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 5851-5871, June.
    13. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, July.
    14. Feijoo, Santiago Rodriguez & Caro, Alejandro Rodriguez & Quintana, Delia Davila, 2003. "Methods for quarterly disaggregation without indicators; a comparative study using simulation," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 63-78, May.
    15. Fukang Zhu & Dehui Wang, 2011. "Estimation and testing for a Poisson autoregressive model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 211-230, March.
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