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Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects

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  • Gianfranco Piras

Abstract

The present paper suggests an estimation procedure for a Cliff and Ord type spatial panel data model with random effects. Building on existing literature, the paper suggests an estimation procedure that (i) considers all the moment conditions in Kapoor et al. (2007) and (ii) allows for the presence of explanatory variables that do not vary over time. Our Monte Carlo results demonstrate that the estimation procedure proposed in this paper is very effective.

Suggested Citation

  • Gianfranco Piras, 2013. "Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 370-388, September.
    • Handle: RePEc:taf:specan:v:8:y:2013:i:3:p:370-388
    DOI: 10.1080/17421772.2013.804628
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    1. Parent, Olivier & LeSage, James P., 2010. "A spatial dynamic panel model with random effects applied to commuting times," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 633-645, June.
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    2. Giovanni Millo, 2022. "The generalized spatial random effects model in R," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-18, December.
    3. Harry H. Kelejian & Gianfranco Piras, 2013. "A J-Test for Panel Models with Fixed Effects, Spatial and Time," Working Papers Working Paper 2013-03, Regional Research Institute, West Virginia University.
    4. 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.
    5. Bao Hoang Nguyen & Zhichao Wang & Valentin Zelenyuk, 2023. "Efficiency of Queensland Public Hospitals via Spatial Panel Stochastic Frontier Models," CEPA Working Papers Series WP102023, School of Economics, University of Queensland, Australia.
    6. Croonenbroeck, Carsten & Palm, Marcel, 2020. "A spatio-temporal Durbin fixed effects IV-Model for ENTSO-E electricity flows analysis," Renewable Energy, Elsevier, vol. 148(C), pages 205-213.
    7. Bernard Fingleton & Silvia Palombi, 2016. "Bootstrap J -Test for Panel Data Models with Spatially Dependent Error Components, a Spatial Lag and Additional Endogenous Variables," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(1), pages 7-26, March.
    8. 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).
    9. 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.
    10. Harry H. Kelejian & Gianfranco Piras, 2016. "A J test for dynamic panel model with fixed effects, and nonparametric spatial and time dependence," Empirical Economics, Springer, vol. 51(4), pages 1581-1605, December.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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