Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/tse/wpaper/124242.html
   My bibliography  Save this paper

Spatial simultaneous autoregressive models for compositional data: Application to land use

Author

Listed:
  • Thomas-Agnan, Christine
  • Laurent, Thibault
  • Ruiz-Gazen, Anne
  • Nguyen, T.H.A
  • Chakir, Raja
  • Lungarska, Anna
Abstract
Econometric land use models study determinants of land-use-shares of different classes: ``agriculture'', ``forest'', ``urban'' and ``other'' for example. Land-use-shares have a compositional nature as well as an important spatial dimension. We compare two compositional regression models with a spatial autoregressive nature in the framework of land use. We study the impact of the choice of coordinate space. We discuss parameters interpretation taking into account the non linear structure as well as the spatial dimension. We compute and interpret the semi-elasticities of the shares with respect to the explanatory variables and the spatial impact summary measures.

Suggested Citation

  • Thomas-Agnan, Christine & Laurent, Thibault & Ruiz-Gazen, Anne & Nguyen, T.H.A & Chakir, Raja & Lungarska, Anna, 2020. "Spatial simultaneous autoregressive models for compositional data: Application to land use," TSE Working Papers 20-1098, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:124242
    as

    Download full text from publisher

    File URL: https://www.tse-fr.eu/sites/default/files/TSE/documents/doc/wp/2020/wp_tse_1098.pdf
    File Function: Full Text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    2. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    3. Michel Goulard & Thibault Laurent & Christine Thomas-Agnan, 2017. "About predictions in spatial autoregressive models: optimal and almost optimal strategies," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 304-325, July.
    4. Lungarska, Anna & Chakir, Raja, 2018. "Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation," Ecological Economics, Elsevier, vol. 147(C), pages 134-154.
    5. Raja Chakir & Anna Lungarska, 2017. "Agricultural rent in land-use models: comparison of frequently used proxies," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 279-303, July.
    6. Jiajia Chen & Xiaoqin Zhang & Shengjia Li, 2017. "Multiple linear regression with compositional response and covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2270-2285, September.
    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. Dargel, Lukas & Thomas-Agnan, Christine, 2023. "Share-ratio interpretations of compositional regression models," TSE Working Papers 23-1456, Toulouse School of Economics (TSE), revised 20 Sep 2023.
    2. Thibault Laurent & Christine Thomas-Agnan & Anne Ruiz-Gazen, 2023. "Covariates impacts in spatial autoregressive models for compositional data," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-23, December.

    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. Pierre-Alain Jayet & Athanasios Petsakos & Raja Chakir & Anna Lungarska & Stéphane De Cara & Elvire Petel & Pierre Humblot & Caroline Godard & David Leclère & Pierre Cantelaube & Cyril Bourgeois & Mél, 2023. "The European agro-economic model AROPAj," Working Papers hal-04109872, HAL.
    2. Lukas Dargel, 2021. "Revisiting estimation methods for spatial econometric interaction models," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-41, December.
    3. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    4. Basak Bayramoglu & Raja CHAKIR & Anna LUNGARSKA, 2016. "Land Use and Freshwater Ecosystems in France," EcoMod2016 9420, EcoMod.
    5. Rémi Prudhomme & Raja Chakir & Anna Lungarska & Thierry Brunelle & Narayanappa Devaraju & Nathalie de Noblet & Pierre-Alain Jayet & Stéphane De Cara & Jean-Christophe Bureau, 2022. "Food, climate and biodiversity: a trilemma of mineral nitrogen use in European agriculture," Review of Agricultural, Food and Environmental Studies, Springer, vol. 103(3), pages 271-299, September.
    6. Anna Lungarska & Raja Chakir & Nosra Ben Fradj & Pierre Alain Jayet & Laure Bamière & Ancuta Isbasoiu & Maxime Ollier & Eva Gossiaux & Ines Chiadmi & Stéphane de Cara & Salomé Kahindo, 2020. "Integrated approach to animal and plant production in an economic model for the analysis of agriculture-environment interactions [Approche intégrée des productions animales et végétales dans un mod," Post-Print hal-03161714, HAL.
    7. Karina Acosta & Hengyu Gu, 2022. "Locked up? The development and internal migration nexus in Colombia," Documentos de Trabajo Sobre Economía Regional y Urbana 19931, Banco de la República, Economía Regional.
    8. Alexander Klemm & Stefan Parys, 2012. "Empirical evidence on the effects of tax incentives," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(3), pages 393-423, June.
    9. repec:rri:wpaper:200711 is not listed on IDEAS
    10. Borsky, Stefan & Kalkschmied, Katja, 2019. "Corruption in space: A closer look at the world's subnations," European Journal of Political Economy, Elsevier, vol. 59(C), pages 400-422.
    11. Shaoling Chen & Susheng Wang & Haisheng Yang, 2015. "Spatial Competition and Interdependence in Strategic Decisions: Empirical Evidence from Franchising," Economic Geography, Clark University, vol. 91(2), pages 165-204, April.
    12. Ricardo B. Politi & Enlinson Mattos & Eric Picin, 2021. "Public input and business tax competition in local communities in Brazil," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(3), pages 799-824, December.
    13. Jorge Luis Casanova Ferrando, 2019. "The Airbnb Effect on theRental Market: the Case of Madrid," Studies on the Spanish Economy eee2019-34, FEDEA.
    14. Thomas de Graaff & Frank G. van Oort & Raymond J.G.M. Florax, 2012. "Regional Population–Employment Dynamics Across Different Sectors Of The Economy," Journal of Regional Science, Wiley Blackwell, vol. 52(1), pages 60-84, February.
    15. Takafumi Kato, 2020. "Likelihood-based strategies for estimating unknown parameters and predicting missing data in the simultaneous autoregressive model," Journal of Geographical Systems, Springer, vol. 22(1), pages 143-176, January.
    16. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C. & Weyman-Jones, Thomas, 2018. "The Spatial Efficiency Multiplier and Common Correlated Effects in a Spatial Autoregressive Stochastic Frontier Model," Working Papers 18-003, Rice University, Department of Economics.
    17. T. H. A. Nguyen & T. Laurent & C. Thomas-Agnan & A. Ruiz-Gazen, 2022. "Analyzing the impacts of socio-economic factors on French departmental elections with CoDa methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(5), pages 1235-1251, April.
    18. 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).
    19. Thibault Laurent & Christine Thomas-Agnan & Anne Ruiz-Gazen, 2023. "Covariates impacts in spatial autoregressive models for compositional data," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-23, December.
    20. Liu, Xiaodong & Saraiva, Paulo, 2015. "GMM estimation of SAR models with endogenous regressors," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 68-79.
    21. Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.

    More about this item

    Keywords

    compositional regression model; marginal effects; simplicial derivative; elasticity; semi-elasticity.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment

    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:tse:wpaper:124242. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/tsetofr.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.