IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v111y2011i2p135-137.html
   My bibliography  Save this article

Instrumental variable estimation of a spatial autoregressive panel model with random effects

Author

Listed:
  • Baltagi, Badi H.
  • Liu, Long

Abstract

This paper extends the instrumental variable estimators of Kelejian and Prucha (1998) and Lee (2003) proposed for the cross-sectional spatial autoregressive model to the random effects spatial autoregressive panel data model. It also suggests an extension of the Baltagi (1981) error component 2SLS estimator to this spatial panel model.

Suggested Citation

  • Baltagi, Badi H. & Liu, Long, 2011. "Instrumental variable estimation of a spatial autoregressive panel model with random effects," Economics Letters, Elsevier, vol. 111(2), pages 135-137, May.
    • Handle: RePEc:eee:ecolet:v:111:y:2011:i:2:p:135-137
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(11)00017-6
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Baltagi, Badi H., 1981. "Simultaneous equations with error components," Journal of Econometrics, Elsevier, vol. 17(2), pages 189-200, November.
    2. Cornwell, Christopher & Schmidt, Peter & Wyhowski, Donald, 1992. "Simultaneous equations and panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 151-181.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    4. Baltagi, Badi H. & Liu, Long, 2009. "A note on the application of EC2SLS and EC3SLS estimators in panel data models," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2189-2192, October.
    5. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
    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. Badi H. BALTAGI & Bernard FINGLETON & Alain PIROTTE, 2014. "Multilevel And Spillover Effects Estimated For Spatial Panel Data, With Application To English House Prices," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 25-36.
    2. Paelinck, Jean & Mur, Jesús & Trivez, F. Javier, 2015. "Modelos para datos espaciales con estructura transversal o de panel. Una revisión/Models for Spatial Data with Panel or Cross-Sectional Structure. A Review," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 33, pages 7-30, Enero.
    3. cyrine hannafi & Christophe Muller, 2016. "The Poverty-Economic Growth-Health Triangle," EcoMod2016 9587, EcoMod.
    4. Álvarez, Inmaculada & Barbero, Javier, 2013. "Knowledge Spillovers in Neoclassical Growth Model: an extension with Public Sector," Working Papers in Economic Theory 2013/07, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).
    5. Maani, Sholeh A. & Wang, Xingang & Rogers, Alan, 2015. "Network Effects, Ethnic Capital and Immigrants' Earnings Assimilation: Evidence from a Spatial, Hausman-Taylor Estimation," IZA Discussion Papers 9308, Institute for the Study of Labor (IZA).
    6. Arbués, Pelayo & Baños, José F. & Mayor, Matías, 2015. "The spatial productivity of transportation infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 166-177.
    7. 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.
    8. Kim, Kijin, 2013. "The Effects of the Clean Air Act on Local Industrial Wages," 6th Annual CRAE, April 5-6, 2013, Columbus, Ohio 147489, Midwest Graduate Student Conference on Regional and Applied Economics (CRAE), The Ohio State University, Department of Agricultural, Environmental and Development Economics.
    9. Orea, L. & Álvarez, I & Jamasb, T., 2016. "Using a spatial econometric approach to mitigate omitted variables in stochastic frontier models: An application to Norwegian electricity distribution networks," Cambridge Working Papers in Economics 1673, Faculty of Economics, University of Cambridge.
    10. repec:jss:jstsof:v:076:i06 is not listed on IDEAS
    11. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2014. "Spatial lag models with nested random effects: An instrumental variable procedure with an application to English house prices," Journal of Urban Economics, Elsevier, vol. 80(C), pages 76-86.
    12. Kyriakos Drivas & Claire Economidou & Sotiris Karkalakos, 2014. "Spatial Aspects of Innovation Activity in the US," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 5(3), pages 464-480, September.
    13. Zaghdoudi, Taha, 2016. "spanel: le package R pour l’estimation des données de panel spatiale
      [spanel: an R package to estimate the spatial panel data]      ," MPRA Paper 72673, University Library of Munich, Germany.
    14. Badi H. Baltagi & Long Liu, 2014. "Random Effects, Fixed Effects and Hausman’s Test for the Generalized Mixed Regressive Spatial Autoregressive Panel," Center for Policy Research Working Papers 174, Center for Policy Research, Maxwell School, Syracuse University.
    15. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2017. "A Panel Data Toolbox for MATLAB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i06).

    More about this item

    Keywords

    Panel data Spatial model Two stage least squares Error components;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    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:eee:ecolet:v:111:y:2011:i:2:p:135-137. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.