IDEAS home Printed from https://ideas.repec.org/p/han/dpaper/dp-712.html
   My bibliography  Save this paper

Spatial autoregressive fractionally integrated moving average model

Author

Listed:
  • Otto, Philipp
  • Sibbertsen, Philipp

Abstract

In this paper, we introduce the concept of fractional integration for spatial autoregressive models. We show that the range of the dependence can be spatially extended or diminished by introducing a further fractional integration parameter to spatial autoregressive moving average models (SARMA). This new model is called the spatial autoregressive fractionally integrated moving average model, briefly sp-ARFIMA. We show the relation to time-series ARFIMA models and also to (higher-order) spatial autoregressive models. Moreover, an estimation procedure based on the maximum-likelihood principle is introduced and analysed in a series of simulation studies. Eventually, the use of the model is illustrated by an empirical example of atmospheric fine particles, so-called aerosol optical thickness, which is important in weather, climate and environmental science.

Suggested Citation

  • Otto, Philipp & Sibbertsen, Philipp, 2023. "Spatial autoregressive fractionally integrated moving average model," Hannover Economic Papers (HEP) dp-712, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-712
    as

    Download full text from publisher

    File URL: https://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-712.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen Gibbons & Henry G. Overman, 2012. "Mostly Pointless Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 172-191, May.
    2. Doğan, Osman & Taşpınar, Süleyman, 2013. "GMM estimation of spatial autoregressive models with moving average disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 903-926.
    3. Robinson, P.M. & Vidal Sanz, J., 2006. "Modified Whittle estimation of multilateral models on a lattice," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1090-1120, May.
    4. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 2, City University of New York Graduate Center, Ph.D. Program in Economics.
    5. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
    6. 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.
    7. Osman Dogan & Suleyman Taspinar, 2013. "GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," Working Papers 1, City University of New York Graduate Center, Ph.D. Program in Economics.
    8. Lahiri, S.N. & Robinson, Peter M., 2016. "Central limit theorems for long range dependent spatial linear processes," LSE Research Online Documents on Economics 65331, London School of Economics and Political Science, LSE Library.
    9. Beran, Jan & Ghosh, Sucharita & Schell, Dieter, 2009. "On least squares estimation for long-memory lattice processes," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2178-2194, November.
    10. Clifford Lam & Pedro C. L. Souza, 2016. "Detection and Estimation of Block Structure in Spatial Weight Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1347-1376, December.
    Full references (including those not matched with items on IDEAS)

    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. Doğan, Osman & Taşpınar, Süleyman, 2013. "GMM estimation of spatial autoregressive models with moving average disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 903-926.
    2. Robinson, Peter, 2019. "Spatial long memory," LSE Research Online Documents on Economics 102182, London School of Economics and Political Science, LSE Library.
    3. Arnab Bhattacharjee & Sudipto Roy, 2019. "Abnormal Returns or Mismeasured Risk? Network Effects and Risk Spillover in Stock Returns," JRFM, MDPI, vol. 12(2), pages 1-13, March.
    4. Rosa Espejo & Nikolai Leonenko & Andriy Olenko & María Ruiz-Medina, 2015. "On a class of minimum contrast estimators for Gegenbauer random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 657-680, December.
    5. Bera, Anil K. & Doğan, Osman & Taşpınar, Süleyman, 2018. "Simple tests for endogeneity of spatial weights matrices," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 130-142.
    6. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
    7. Miryam S. Merk & Philipp Otto, 2022. "Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross‐sectional resampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    8. Neumayer, Eric & Plümper, Thomas, 2016. "W," Political Science Research and Methods, Cambridge University Press, vol. 4(1), pages 175-193, January.
    9. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 2, City University of New York Graduate Center, Ph.D. Program in Economics.
    10. Fedorets, Alexandra & Lottmann, Franziska & Stops, Michael, 2019. "Job matching in connected regional and occupational labour markets," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 53(8), pages 1085-1098.
    11. An Liu & Henk Folmer & Johan H L Oud, 2014. "Estimation of Autoregressive Models with Two Types of Weak Spatial Dependence by Means of the W-Based and the Latent Variables Approach: Evidence from Monte Carlo Simulations," Environment and Planning A, , vol. 46(1), pages 186-202, January.
    12. Sandy Fréret & Denis Maguain, 2017. "The effects of agglomeration on tax competition: evidence from a two-regime spatial panel model on French data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(6), pages 1100-1140, December.
    13. Galletta, Sergio, 2017. "Law enforcement, municipal budgets and spillover effects: Evidence from a quasi-experiment in Italy," Journal of Urban Economics, Elsevier, vol. 101(C), pages 90-105.
    14. Armbruster, Ginger & Endicott-Popovsky, Barbara & Whittington, Jan, 2012. "Are we prepared for the economic risk resulting from telecom hotel disruptions?," International Journal of Critical Infrastructure Protection, Elsevier, vol. 5(2), pages 55-65.
    15. Matthias Firgo & Peter Mayerhofer, 2015. "Wissens-Spillovers und regionale Entwicklung - welche strukturpolitische Ausrichtung optimiert des Wachstum?," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 144, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.
    16. Wang, Xiong & Wang, Xiao & Ren, Xiaohang & Wen, Fenghua, 2022. "Can digital financial inclusion affect CO2 emissions of China at the prefecture level? Evidence from a spatial econometric approach," Energy Economics, Elsevier, vol. 109(C).
    17. Lim, C.Y. & Meerschaert, M.M. & Scheffler, H.-P., 2014. "Parameter estimation for operator scaling random fields," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 172-183.
    18. Hidalgo, Javier, 2009. "Goodness of fit for lattice processes," Journal of Econometrics, Elsevier, vol. 151(2), pages 113-128, August.
    19. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2016. "A spatial autoregressive panel model to analyze road network spillovers on production," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 83-92.
    20. Meyer, Maximilian & Hulke, Carolin & Kamwi, Jonathan & Kolem, Hannah & Börner, Jan, 2022. "Spatially heterogeneous effects of collective action on environmental dependence in Namibia’s Zambezi region," World Development, Elsevier, vol. 159(C).

    More about this item

    Keywords

    Spatial ARFIMA; spatial fractional integration; long-range dependence; aerosol optical depth;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:han:dpaper:dp-712. 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: Heidrich, Christian (email available below). General contact details of provider: https://edirc.repec.org/data/fwhande.html .

    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.