IDEAS home Printed from https://ideas.repec.org/a/sae/envira/v11y1979i1p51-58.html
   My bibliography  Save this article

A Biparametric Approach to Spatial Autocorrelation

Author

Listed:
  • A S Brandsma
  • R H Ketellapper

Abstract

In spatial econometric models, autocorrelation among error terms is usually incorporated by means of the so-called contiguity matrix W, determining the interdependence between the spatial observations on the dependent variable. In this paper, the analysis is generalized by introducing two contiguity matrices, related to two autocorrelation parameters. This may be useful when dealing with variables representing flows between regions, where both the origin and the destination regions have a different impact on the autocorrelation scheme. It is shown analytically and illustrated empirically that the presence of such autocorrelation can be tested with the likelihood-ratio test, whereas the parameters can be estimated by the maximum-likelihood approach.

Suggested Citation

  • A S Brandsma & R H Ketellapper, 1979. "A Biparametric Approach to Spatial Autocorrelation," Environment and Planning A, , vol. 11(1), pages 51-58, January.
    • Handle: RePEc:sae:envira:v:11:y:1979:i:1:p:51-58
    DOI: 10.1068/a110051
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/a110051
    Download Restriction: no
    File URL: https://libkey.io/10.1068/a110051?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. Magnus, Jan R., 1978. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," Journal of Econometrics, Elsevier, vol. 7(3), pages 281-312, April.
    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. Julie Le Gallo, 2002. "Économétrie spatiale : l'autocorrélation spatiale dans les modèles de régression linéaire," Économie et Prévision, Programme National Persée, vol. 155(4), pages 139-157.
    2. L W Hepple, 1995. "Bayesian Techniques in Spatial and Network Econometrics: 2. Computational Methods and Algorithms," Environment and Planning A, , vol. 27(4), pages 615-644, April.
    3. Gupta, Abhimanyu & Robinson, Peter M., 2018. "Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension," Journal of Econometrics, Elsevier, vol. 202(1), pages 92-107.
    4. Bolduc, Denis & Laferrière, Richard & Santarossa, Gino, 1993. "Modèle d’explication de flux à composantes d’erreurs spatialement corrélées," L'Actualité Economique, Société Canadienne de Science Economique, vol. 69(3), pages 193-201, septembre.
    5. Burridge, Peter, 2011. "A research agenda on general-to-specific spatial model search," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 71-90.
    6. Patrick Doreian, 1982. "Maximum Likelihood Methods for Linear Models," Sociological Methods & Research, , vol. 10(3), pages 243-269, February.
    7. Malcolm M. Dow, 1986. "Model Selection Procedures for Network Autocorrelated Disturbances Models," Sociological Methods & Research, , vol. 14(4), pages 403-422, May.
    8. repec:asg:wpaper:1013 is not listed on IDEAS
    9. Georgios Fotopoulos & Helen Louri, 2011. "On the geography of international banking: the role of third-country effects," Working Papers 125, Bank of Greece.
    10. Julie Le Gallo, 2000. "Spatial econometrics (1, Spatial autocorrelation) [Econométrie spatiale (1, Autocorrélation spatiale)]," Working Papers hal-01527290, HAL.
    11. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    12. 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.
    13. Georgios Fotopoulos & Helen Louri, 2011. "On the Geography of International Banking: a case for spatial econometrics?," ERSA conference papers ersa10p1081, European Regional Science Association.
    14. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.
    15. J. Paul Elhorst & Katarina Zigova, 2011. "Evidence of Competition in Research Activity among Economic Department using Spatial Econometric Techniques," Working Paper Series of the Department of Economics, University of Konstanz 2011-04, Department of Economics, University of Konstanz.
    16. F. Bavaud & M. Kordi & C. Kaiser, 2018. "Flow autocorrelation: a dyadic approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 95-111, July.
    17. L Anselin, 1982. "A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model," Environment and Planning A, , vol. 14(8), pages 1023-1030, August.
    18. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    19. López-Hernández, Fernando A., 2013. "Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia," Economic Modelling, Elsevier, vol. 33(C), pages 270-279.
    20. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    21. Manfred M. Fischer & Daniel A. Griffith, 2008. "Modeling Spatial Autocorrelation In Spatial Interaction Data: An Application To Patent Citation Data In The European Union," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 969-989, December.
    22. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.

    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. Antle, John M. & Aitah, Ali S., 1984. "Egypt'S Multiproduct Agricultural Technology And Agricultural Policy," Working Papers 225790, University of California, Davis, Department of Agricultural and Resource Economics.
    2. Palm, F.C. & Winder, C.C.A., 1986. "The stochastic life cycle consumption model : theoretical results and empirical evidence," Serie Research Memoranda 0016, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    3. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2006. "Joint LM test for homoskedasticity in a one-way error component model," Journal of Econometrics, Elsevier, vol. 134(2), pages 401-417, October.
    4. Antle, John M., 1983. "The Structure Of U.S. Agricultural Technology, 1910-1978," Working Papers 225709, University of California, Davis, Department of Agricultural and Resource Economics.
    5. Chu, Fong-Lin, 2011. "A piecewise linear approach to modeling and forecasting demand for Macau tourism," Tourism Management, Elsevier, vol. 32(6), pages 1414-1420.
    6. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    7. Antle, John M. & Aitah, Ali S., 1982. "The Structure of Rice Technology, Farmer Rationality, and Agricultural Policy in Egypt," Working Papers 233042, University of California, Davis, Agricultural Development Systems: Egypt Project.
    8. repec:ags:ucdegw:233042 is not listed on IDEAS
    9. van der Leeuw, J.L., 1994. "The second derivative of the likelihood of an exact ARMA model," Research Memorandum FEW 661, Tilburg University, School of Economics and Management.
    10. Adriaan Kalwij, 2000. "Estimating the economic return to schooling on the basis of panel data," Applied Economics, Taylor & Francis Journals, vol. 32(1), pages 61-71.
    11. Andrews, Donald W K, 1986. "A Note on the Unbiasedness of Feasible GLS, Quasi-maximum Likelihood, Robust, Adaptive, and Spectral Estimators of the Linear Model," Econometrica, Econometric Society, vol. 54(3), pages 687-698, May.
    12. Raats, V.M. & van der Genugten, B.B. & Moors, J.J.A., 2004. "Asymptotics of Multivariate Regression with Consecutively Added Dependent Varibles," Other publications TiSEM ec69ffa7-ec42-4ca3-910b-c, Tilburg University, School of Economics and Management.
    13. Banerjee, Anurag N. & Magnus, Jan R., 2000. "On the sensitivity of the usual t- and F-tests to covariance misspecification," Journal of Econometrics, Elsevier, vol. 95(1), pages 157-176, March.
    14. Atanu Saha & Arthur Havenner & Hovav Talpaz, 1997. "Stochastic production function estimation: small sample properties of ML versus FGLS," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 459-469.
    15. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    16. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    17. Kruiniger, Hugo, 2008. "Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model," Journal of Econometrics, Elsevier, vol. 144(2), pages 447-464, June.
    18. Jaime Martínez-Martín, 2011. "General equilibrium long-run determinants for Spanish FDI: a spatial panel data approach," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(3), pages 305-333, September.
    19. Bilsborrow, Richard E. & Guilkey, David K., 1987. "Community and institutional influence on fertility: analytical issues," ILO Working Papers 992544533402676, International Labour Organization.
    20. Anne-Sophie Krah & Zoran Nikolić & Ralf Korn, 2020. "Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies," Risks, MDPI, vol. 8(1), pages 1-79, February.
    21. Shin, Dong Wan & Park, Chul Gyu & Park, Taesung, 2001. "Testing for one-sided group effects in repeated measures study," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 233-247, August.

    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:sae:envira:v:11:y:1979:i:1:p:51-58. 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: SAGE Publications (email available below). General contact details of provider: .

    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.