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A time-space dynamic panel data model with spatial moving average errors

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journal contribution
posted on 08.02.2019, 10:45 by BH Baltagi, B Fingleton, A Pirotte
This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi et al. (2014) and Fingleton (2008a,b). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001–2012, with the last two years reserved for prediction.

History

Citation

Regional Science and Urban Economics, 2018, in press

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Business

Version

AM (Accepted Manuscript)

Published in

Regional Science and Urban Economics

Publisher

Elsevier

issn

0166-0462

eissn

1879-2308

Acceptance date

27/04/2018

Copyright date

2018

Publisher version

https://www.sciencedirect.com/science/article/pii/S0166046217303599?via=ihub

Notes

The file associated with this record is under embargo until 18 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

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