IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v28y2009i4p343-357.html
   My bibliography  Save this article

Related-variables selection in temporal disaggregation

Author

Listed:
  • Kosei Fukuda

    (College of Economics, Nihon University, Tokyo, Japan)

Abstract

Two related-variables selection methods for temporal disaggregation are proposed. In the first method, the hypothesis tests for a common feature (cointegration or serial correlation) are first performed. If there is a common feature between observed aggregated series and related variables, the conventional Chow-Lin procedure is applied. In the second method, alternative Chow-Lin disaggregating models with and without related variables are first estimated and the corresponding values of the Bayesian information criterion (BIC) are stored. It is determined on the basis of the selected model whether related variables should be included in the Chow-Lin model. The efficacy of these methods is examined via simulations and empirical applications. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Kosei Fukuda, 2009. "Related-variables selection in temporal disaggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 343-357.
    • Handle: RePEc:jof:jforec:v:28:y:2009:i:4:p:343-357
    DOI: 10.1002/for.1115
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.1115
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.1115?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nijman, T E & Palm, F C, 1986. "The Construction and Use of Approximations for Missing Quarterly Observations: A Model-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 47-58, January.
    2. Fabio H. Nieto, 2007. "Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 53-76.
    3. Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April.
    4. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    5. Zellner, Arnold & Tobias, Justin, 1998. "A Note on Aggregation, Disaggregation and Forecasting Performance," CUDARE Working Papers 198677, University of California, Berkeley, Department of Agricultural and Resource Economics.
    6. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 369-380, October.
    7. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-395, October.
    8. Gulasekaran Rajaguru & Tilak Abeysinghe, 2004. "Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 431-447.
    9. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    10. 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.
    11. J. H. C. Lisman & J. Sandee, 1964. "Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 13(2), pages 87-90, June.
    12. 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.
    13. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    14. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages 108-129, February.
    15. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    16. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    17. J. C. G. Boot & W. Feibes & J. H. C. Lisman, 1967. "Further Methods of Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 65-75, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    2. José Casals & Miguel Jerez & Sonia Sotoca, 2009. "Modelling and forecasting time series sampled at different frequencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 316-342.
    3. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    4. Willie Lahari, 2011. "Assessing Business Cycle Synchronisation - Prospects for a Pacific Islands Currency Union," Working Papers 1110, University of Otago, Department of Economics, revised Oct 2011.
    5. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
    6. Vladim r Hajko, 2015. "Energy-Gross Domestic Product Nexus: Disaggregated Analysis for the Czech Republic in the Post-Transformation Era," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 869-888.
    7. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    8. Osmani Teixeira de Carvalho de Guillén & Carlos Hamilton Vasconcelos Araújo, 2005. "O Mecanismo De Transmissão Da Taxa De Câmbio Para Índices De Preços: Uma Análise Vecm Para O Brasil," Anais do XXXIII Encontro Nacional de Economia [Proceedings of the 33rd Brazilian Economics Meeting] 034, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    9. Massimo Gerli & Giovanni Marini, 2006. "Spatial and Temporal Time Series Conversion: A Consistent Estimator of the Error Variance-Covariance Matrix," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 373-405.
    10. Chen, Li & Gao, Jiti & Vahid, Farshid, 2022. "Global temperatures and greenhouse gases: A common features approach," Journal of Econometrics, Elsevier, vol. 230(2), pages 240-254.
    11. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201316, University of Hawaii at Manoa, Department of Economics.
    12. Elizabeth Wakerly & Byron Scott & James Nason, 2006. "Common trends and common cycles in Canada: who knew so much has been going on?," Canadian Journal of Economics, Canadian Economics Association, vol. 39(1), pages 320-347, February.
    13. Knif, Johan & Pynnonen, Seppo & Luoma, Martti, 1996. "Testing for common autocorrelation features of two scandinavian stock markets," International Review of Financial Analysis, Elsevier, vol. 5(1), pages 55-64.
    14. Issler, Joao Victor & Vahid, Farshid, 2001. "Common cycles and the importance of transitory shocks to macroeconomic aggregates," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 449-475, June.
    15. Carlos Enrique Carrasco Gutiérrez & Reinaldo Castro Souza & Osmani Teixeira de Carvalho Guillén, 2007. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Working Papers Series 139, Central Bank of Brazil, Research Department.
    16. Bernardí Cabred & Jose Pavía, 1999. "EstimatingJ (>1) quarterly time series in fulfilling annual and quarterly constraints," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(3), pages 339-349, August.
    17. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    18. Carlos Enrique Carrasco Gutierrez & Fábio Augusto Reis Gomes, 2006. "Evidence About Mercosur’S Business Cycle," Anais do XXXIV Encontro Nacional de Economia [Proceedings of the 34th Brazilian Economics Meeting] 179, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    19. Paul Alagidede & Theodore Panagiotidis & Xu Zhang, 2011. "Causal relationship between stock prices and exchange rates," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 20(1), pages 67-86.
    20. Christian Caamaño-Carrillo & Sergio Contreras-Espinoza & Orietta Nicolis, 2023. "Reconstructing the Quarterly Series of the Chilean Gross Domestic Product Using a State Space Approach," Mathematics, MDPI, vol. 11(8), pages 1-14, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:28:y:2009:i:4:p:343-357. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.