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Dynamic panel estimation of a regional adjustment model with spatial-temporal robust covariance

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  • Dayton M. Lambert

    (Oklahoma State University)

Abstract

This paper extends the Arellano-Bond dynamic panel regression model’s covariance estimator to one robust to spatial autocovariance and heteroskedasticity. We apply Kelejian and Prucha’s semi-parametric procedure for estimating covariance matrices that accommodate spatial heteroskedastic autocovariance processes to augment the usual Arellano-Bond covariance estimator. The empirical application examines the jobs-to-people/people-to-jobs conundrum with Carlino and Mill’s regional adjustment model using employment and population data from 2001 to 2018 for Oklahoma’s 77 counties.

Suggested Citation

  • Dayton M. Lambert, 2020. "Dynamic panel estimation of a regional adjustment model with spatial-temporal robust covariance," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 245-265, December.
    • Handle: RePEc:spr:lsprsc:v:13:y:2020:i:3:d:10.1007_s12076-020-00257-y
    DOI: 10.1007/s12076-020-00257-y
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    References listed on IDEAS

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    1. Daniel C. Monchuk & Dermot J. Hayes & John A. Miranowski & Dayton M. Lambert, 2011. "Inference Based On Alternative Bootstrapping Methods In Spatial Models With An Application To County Income Growth In The United States," Journal of Regional Science, Wiley Blackwell, vol. 51(5), pages 880-896, December.
    2. Jason P. Brown & Dayton M. Lambert & Raymond J. G. M. Florax, 2013. "The Birth, Death, and Persistence of Firms: Creative Destruction and the Spatial Distribution of U.S. Manufacturing Establishments, 2000–2006," Economic Geography, Clark University, vol. 89(3), pages 203-226, July.
    3. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    4. G F Mulligan & A C Vias & S M Glavac, 1999. "Initial Diagnostics of a Regional Adjustment Model," Environment and Planning A, , vol. 31(5), pages 855-876, May.
    5. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    7. Joris Pinkse & Margaret E. Slade, 2010. "The Future Of Spatial Econometrics," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 103-117, February.
    8. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    9. Kim, Min Seong & Sun, Yixiao, 2013. "Heteroskedasticity and spatiotemporal dependence robust inference for linear panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 177(1), pages 85-108.
    10. 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.
    11. D. M. Lambert & C. N. Boyer & L. He, 2016. "Spatial-temporal heteroskedastic robust covariance estimation for Markov transition probabilities: an application examining land use change," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 353-362, October.
    12. Marlon G. Boarnet & Saksith Chalermpong & Elizabeth Geho, 2005. "Specification issues in models of population and employment growth," Papers in Regional Science, Wiley Blackwell, vol. 84(1), pages 21-46, March.
    13. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    14. Pede, Valerien O. & Florax, Raymond J.G.M. & de Groot, Henri L.F., 2006. "The Role of Knowledge Externalities in the Spatial Distribution of Economic Growth: A Spatial Econometric Analysis for US Counties, 1969-2003," 2006 Annual meeting, July 23-26, Long Beach, CA 21157, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    15. Gebremeskel H. Gebremariam & Tesfa G. Gebremedhin & Peter V. Schaeffer, 2011. "Employment, Income, And Migration In Appalachia: A Spatial Simultaneous Equations Approach," Journal of Regional Science, Wiley Blackwell, vol. 51(1), pages 102-120, February.
    16. Giuseppe Arbia & Bernard Fingleton, 2008. "New spatial econometric techniques and applications in regional science," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 311-317, August.
    17. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    18. Lambert, Dayton M. & Boyer, Christopher N. & He, Lixia L., 2016. "Markov Transition Probabilities and Robust Spatial-temporal Covariance Estimation," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229863, Southern Agricultural Economics Association.
    19. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    20. David A. McGranahan & Timothy R. Wojan & Dayton M. Lambert, 2011. "The rural growth trifecta: outdoor amenities, creative class and entrepreneurial context -super-§," Journal of Economic Geography, Oxford University Press, vol. 11(3), pages 529-557, May.
    21. 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.
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    More about this item

    Keywords

    Dynamic panel; Spatial-temporal covariance; Regional adjustment model; Instrumental variables;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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