IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v62y2008i4p482-508.html
   My bibliography  Save this article

Consistency and asymptotic normality of least squares estimators in generalized STAR models

Author

Listed:
  • Svetlana Borovkova
  • Hendrik P. Lopuhaä
  • Budi Nurani Ruchjana

Abstract

Space–time autoregressive (STAR) models, introduced by Cliff and Ord [Spatial autocorrelation (1973) Pioneer, London] are successfully applied in many areas of science, particularly when there is prior information about spatial dependence. These models have significantly fewer parameters than vector autoregressive models, where all information about spatial and time dependence is deduced from the data. A more flexible class of models, generalized STAR models, has been introduced in Borovkovaet al. [Proc. 17th Int. Workshop Stat. Model. (2002), Chania, Greece] where the model parameters are allowed to vary per location. This paper establishes strong consistency and asymptotic normality of the least squares estimator in generalized STAR models. These results are obtained under minimal conditions on the sequence of innovations, which are assumed to form a martingale difference array. We investigate the quality of the normal approximation for finite samples by means of a numerical simulation study, and apply a generalized STAR model to a multivariate time series of monthly tea production in west Java, Indonesia.

Suggested Citation

  • Svetlana Borovkova & Hendrik P. Lopuhaä & Budi Nurani Ruchjana, 2008. "Consistency and asymptotic normality of least squares estimators in generalized STAR models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(4), pages 482-508, November.
    • Handle: RePEc:bla:stanee:v:62:y:2008:i:4:p:482-508
    DOI: 10.1111/j.1467-9574.2008.00391.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9574.2008.00391.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9574.2008.00391.x?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. Valter Di Giacinto, 2006. "A Generalized Space-Time ARMA Model with an Application to Regional Unemployment Analysis in Italy," International Regional Science Review, , vol. 29(2), pages 159-198, April.
    2. Garrido, Rodrigo A., 2000. "Spatial interaction between the truck flows through the Mexico-Texas border," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(1), pages 23-33, January.
    3. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    4. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, vol. 90(3), pages 335-339, March.
    5. Anderson, T. W. & Kunitomo, Naoto, 1992. "Asymptotic distributions of regression and autoregression coefficients with martingale difference disturbances," Journal of Multivariate Analysis, Elsevier, vol. 40(2), pages 221-243, February.
    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. Ryan H. L. Ip & Dmitry Demskoi & Azizur Rahman & Lihong Zheng, 2021. "Evaluation of COVID-19 Mitigation Policies in Australia Using Generalised Space-Time Autoregressive Intervention Models," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    2. Gehman, Andrew & Wei, William W.S., 2020. "Optimal spatial aggregation of space–time models and applications," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).

    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. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    2. Safikhani, Abolfazl & Kamga, Camille & Mudigonda, Sandeep & Faghih, Sabiheh Sadat & Moghimi, Bahman, 2020. "Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1138-1148.
    3. Massimiliano Agovino & Antonio Garofalo, 2013. "Dipendenza spaziale contemporanea e non contemporanea nei tassi di disoccupazione: un tentativo di analisi empirica dei dati provinciali italiani," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2013(3), pages 45-82.
    4. Kristie M. Engemann & Ruben Hernandez-Murillo & Michael T. Owyang, 2011. "Regional aggregation in forecasting: an application to the Federal Reserve’s Eighth District," Review, Federal Reserve Bank of St. Louis, vol. 93(May), pages 207-222.
    5. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    6. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
    7. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.
    8. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    9. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
    10. Bokun, Kathryn O. & Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2023. "FRED-SD: A real-time database for state-level data with forecasting applications," International Journal of Forecasting, Elsevier, vol. 39(1), pages 279-297.
    11. Hengzhou Xu & Chuanrong Zhang & Weidong Li & Wenjing Zhang & Hongchun Yin, 2018. "Economic growth and carbon emission in China:a spatial econometric Kuznets curve?," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 36(1), pages 11-28.
    12. Roger Bivand, 2008. "Implementing Representations Of Space In Economic Geography," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 1-27, February.
    13. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.
    14. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941.
    15. Sergio Destefanis & Valter Di Giacinto, 2022. "EU structural funds and GDP per capita: Spatial VAR evidence for the European regions," Discussion Paper series in Regional Science & Economic Geography 2022-09, Gran Sasso Science Institute, Social Sciences, revised Oct 2024.
    16. Auffhammer, Maximilian & Carson, Richard T., 2006. "Forecasting the Path of China's CO2 Emissions: Offsetting Kyoto - and Then Some," CUDARE Working Papers 7197, University of California, Berkeley, Department of Agricultural and Resource Economics.
    17. Hensher, David A. & Teye, Collins, 2019. "Commodity interaction in freight movement models for New South Wales," Journal of Transport Geography, Elsevier, vol. 80(C).
    18. Pesaran, M. Hashem & Chudik, Alexander, 2014. "Aggregation in large dynamic panels," Journal of Econometrics, Elsevier, vol. 178(P2), pages 273-285.
    19. Doupe, Patrick, 2014. "The costs of error in setting reference rates for reduced deforestation," Working Papers 249497, Australian National University, Centre for Climate Economics & Policy.
    20. Arnab Bhattacharjee & Sean Holly, 2013. "Understanding Interactions in Social Networks and Committees," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(1), pages 23-53, March.

    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:bla:stanee:v:62:y:2008:i:4:p:482-508. 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 Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.