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Analyzing repeated-game economics experiments: robust standard errors for panel data with serial correlation

In: Handbook on Experimental Economics and the Environment

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  • Christian A. Vossler

Abstract

Laboratory and field experiments have grown significantly in prominence over the past decade. The experimental method provides randomization in key variables therefore permitting a deeper understanding of important economic phenomena. This path-breaking volume provides a valuable collection of experimental work within the area of environmental and resource economics and showcases how laboratory and field experiments can be used for both positive and normative purposes.

Suggested Citation

  • Christian A. Vossler, 2013. "Analyzing repeated-game economics experiments: robust standard errors for panel data with serial correlation," Chapters, in: John A. List & Michael K. Price (ed.),Handbook on Experimental Economics and the Environment, chapter 3, pages 89-112, Edward Elgar Publishing.
  • Handle: RePEc:elg:eechap:12964_3
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    References listed on IDEAS

    as
    1. Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, University Library of Munich, Germany.
    2. RobertS. Shupp & ArlingtonW. Williams, 2008. "Risk preference differentials of small groups and individuals," Economic Journal, Royal Economic Society, vol. 118(525), pages 258-283, January.
    3. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    4. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    5. Davis, Douglas D. & Holt, Charles a., 1993. "Experimental economics: Methods, problems and promise," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 8(2), pages 179-212.
    6. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    7. Robert P. Flood & Andrew K. Rose, 2002. "Uncovered Interest Parity in Crisis," IMF Staff Papers, Palgrave Macmillan, vol. 49(2), pages 1-6.
    8. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2002. "Heteroskedasticity-Autocorrelation Robust Testing Using Bandwidth Equal To Sample Size," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1350-1366, December.
    9. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    10. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    11. 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.
    12. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    13. Nava Ashraf & Iris Bohnet & Nikita Piankov, 2006. "Decomposing trust and trustworthiness," Experimental Economics, Springer;Economic Science Association, vol. 9(3), pages 193-208, September.
    14. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    15. Baker II, Ronald J. & Walker, James M. & Williams, Arlington W., 2009. "Matching contributions and the voluntary provision of a pure public good: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 122-134, May.
    16. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 249-275.
    17. David M. Drukker, 2003. "Testing for serial correlation in linear panel-data models," Stata Journal, StataCorp LP, vol. 3(2), pages 168-177, June.
    18. Badi H. Baltagi & Chihwa Kao, 2000. "Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey," Center for Policy Research Working Papers 16, Center for Policy Research, Maxwell School, Syracuse University.
    19. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    20. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Citations

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    Cited by:

    1. Jordan F. Suter & Kathleen Segerson & Christian A. Vossler & Gregory L. Poe, 2010. "Voluntary-Threat Approaches to Reduce Ambient Water Pollution," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(4), pages 1195-1213.
    2. Suter, Jordan F. & Shammin, Md Rumi, 2013. "Returns to residential energy efficiency and conservation measures: A field experiment," Energy Policy, Elsevier, vol. 59(C), pages 551-561.
    3. Vossler, Christian A. & Suter, Jordan F. & Poe, Gregory L., 2013. "Experimental evidence on dynamic pollution tax policies," Journal of Economic Behavior & Organization, Elsevier, vol. 93(C), pages 101-115.

    More about this item

    Keywords

    Economics and Finance; Environment;

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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