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Forecasting with dynamic spatial panel data: practical implementation methods

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  • Bernard Fingleton

Abstract

This paper addresses the issue of how in practice one might predict given that the data generating process is a dynamic spatial panel model. One period ahead predictions are calculated using various alternative predictors, some of which are misspecified, and their relative performance evaluated.

Suggested Citation

  • Bernard Fingleton, 2014. "Forecasting with dynamic spatial panel data: practical implementation methods," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 194-207.
    • Handle: RePEc:ove:journl:aid:10430
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    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/10430
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    3. Nicolas DEBARSY (CERPE De Namur) & Cem ERTUR & James P. LeSAGE, 2010. "Interpreting Dynamic Space-Time Panel Data Models," LEO Working Papers / DR LEO 800, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Bernard Fingleton, 2009. "Prediction Using Panel Data Regression with Spatial Random Effects," International Regional Science Review, , vol. 32(2), pages 195-220, April.
    5. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    6. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    7. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    8. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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    Cited by:

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    2. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
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    4. 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.
    5. Justin Doran & Bernard Fingleton, 2018. "US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation," Environment and Planning A, , vol. 50(1), pages 111-132, February.
    6. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    7. Christian Glocker & Matteo Iacopini & Tam'as Krisztin & Philipp Piribauer, 2023. "A Bayesian Markov-switching SAR model for time-varying cross-price spillovers," Papers 2310.19557, arXiv.org.
    8. Burridge, Peter & Iacone, Fabrizio & Lazarová, Štěpána, 2015. "Spatial effects in a common trend model of US city-level CPI," Regional Science and Urban Economics, Elsevier, vol. 54(C), pages 87-98.

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