IDEAS home Printed from https://ideas.repec.org/p/trn/utwpem/2014-08.html
   My bibliography  Save this paper

Fitting Spatial Econometric Models through the Unilateral Approximation

Author

Listed:
  • Giuseppe Arbia
  • Marco Bee
  • Giuseppe Espa
  • Flavio Santi

Abstract

Maximum likelihood estimation of spatial models based on weight matrices typically requires a sizeable computational capacity, even in rel- atively small samples. The unilateral approximation approach to spatial models estimation has been suggested in Besag (1974) as a viable alternat- ive to MLE for conditionally specified processes. In this paper we revisit the method, extend it to simultaneous spatial processes and study the finite-sample properties of the resulting estimators by means of Monte Carlo simulations, using several Conditional Autoregressive Models. Ac- cording to the results, the performance of the unilateral estimators is very good, both in terms of statistical properties (accuracy and precision) and in terms of computing time.

Suggested Citation

  • Giuseppe Arbia & Marco Bee & Giuseppe Espa & Flavio Santi, 2014. "Fitting Spatial Econometric Models through the Unilateral Approximation," DEM Discussion Papers 2014/08, Department of Economics and Management.
    • Handle: RePEc:trn:utwpem:2014/08
    as

    Download full text from publisher

    File URL: http://www.unitn.it/files/download/27419/demdp2014_08.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bee, Marco & Riccaboni, Massimo & Schiavo, Stefano, 2017. "Where Gibrat meets Zipf: Scale and scope of French firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 265-275.
    2. LeSage, James P. & Kelley Pace, R., 2007. "A matrix exponential spatial specification," Journal of Econometrics, Elsevier, vol. 140(1), pages 190-214, September.
    3. Hien Tran & Enrico Santarelli & Enrico Zaninotto, 2015. "Efficiency or bounded rationality? Drivers of firm diversification strategies in Vietnam," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 983-1010, November.
    4. Giuseppe Arbia & Marco Bee & Giuseppe Espa, 2013. "Testing Isotropy in Spatial Econometric Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 228-240, September.
    5. Pace, R. Kelley & LeSage, James P., 2004. "Chebyshev approximation of log-determinants of spatial weight matrices," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 179-196, March.
    6. Flavio Bazzana & Anna Zadorozhnaya & Roberto Gabriele, 2014. "The role of covenants in bond issue and investment policy. The case of Russian companies," DEM Discussion Papers 2014/05, Department of Economics and Management.
    7. Yongtao Guan & Michael Sherman & James A. Calvin, 2004. "A Nonparametric Test for Spatial Isotropy Using Subsampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 810-821, January.
    8. Edoardo Gaffeo & Ronny Mazzocchi, 2014. "Competition in the banking sector and economic growth: panel-based international evidence," DEM Discussion Papers 2014/02, Department of Economics and Management.
    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. Mariasole Banno' & Elisa Pozza & Sandro Trento, 2015. "La famiglia fa male all'internazionalizzazione dell'impresa?," DEM Discussion Papers 2015/03, Department of Economics and Management.
    2. Mariasole Bann√ö & Diego Giuliani & Enrico Zaninotto, 2014. "Going abroad on regional shoulders: The role of spillovers on the composition of regional exports," DEM Discussion Papers 2014/06, Department of Economics and Management.
    3. Flavio Bazzana & Anna Zadorozhnaya & Roberto Gabriele, 2014. "The role of covenants in bond issue and investment policy. The case of Russian companies," DEM Discussion Papers 2014/05, Department of Economics and Management.
    4. Yan Chen & Youran Qi & Qing Liu & Peter Chien, 2018. "Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 409-440, December.
    5. repec:asg:wpaper:1013 is not listed on IDEAS
    6. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    7. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    8. Philipp Piribauer & Jesús Crespo Cuaresma, 2016. "Bayesian Variable Selection in Spatial Autoregressive Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(4), pages 457-479, October.
    9. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    10. Debarsy, Nicolas & Jin, Fei & Lee, Lung-fei, 2015. "Large sample properties of the matrix exponential spatial specification with an application to FDI," Journal of Econometrics, Elsevier, vol. 188(1), pages 1-21.
    11. Ishihara, Tsunehiro & Omori, Yasuhiro & Asai, Manabu, 2016. "Matrix exponential stochastic volatility with cross leverage," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 331-350.
    12. Bansal, Prateek & Krueger, Rico & Graham, Daniel J., 2021. "Fast Bayesian estimation of spatial count data models," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    13. Calebe Figueiredo & Alan Silva, 2015. "A matrix exponential spatial specification approach to panel data models," Empirical Economics, Springer, vol. 49(1), pages 115-129, August.
    14. Dai, Lu & Zhang, Jiajun & Luo, Shougui, 2022. "Effective R&D capital and total factor productivity: Evidence using spatial panel data models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    15. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    16. Tamás Krisztin & Philipp Piribauer, 2021. "A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows," Empirical Economics, Springer, vol. 61(1), pages 231-257, July.
    17. Dyogo Lesniewski Ribeiro & Tamara Cantú Maltauro & Luciana Pagliosa Carvalho Guedes & Miguel Angel Uribe-Opazo & Gustavo Henrique Dalposso, 2024. "Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy," Stats, MDPI, vol. 7(1), pages 1-14, January.
    18. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312.
    19. Pradhan, Rudra P. & Arvin, Mak B. & Nair, Mahendhiran & Bennett, Sara E., 2020. "Unveiling the causal relationships among banking competition, stock and insurance market development, and economic growth in Europe," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 74-87.
    20. Marco H. Benedetti & Veronica J. Berrocal & Naveen N. Narisetty, 2022. "Identifying regions of inhomogeneities in spatial processes via an M‐RA and mixture priors," Biometrics, The International Biometric Society, vol. 78(2), pages 798-811, June.
    21. Jin, Fei & Lee, Lung-fei, 2018. "Irregular N2SLS and LASSO estimation of the matrix exponential spatial specification model," Journal of Econometrics, Elsevier, vol. 206(2), pages 336-358.

    More about this item

    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:trn:utwpem:2014/08. 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: roberto.gabriele@unitn.it (email available below). General contact details of provider: https://edirc.repec.org/data/detreit.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.