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Pairwise difference estimation of linear panel data

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  • Aquaro, M.

    (Tilburg University)

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    Abstract

    Panel data sets, also called longitudinal data sets, are sets of data where the same units (for instance individuals, firms, or countries) are observed more than one time. Models that exploit the specific structure of these data sets are called panel data models. One of the main advantage of using these models is the possibility of appropriately including unobserved variables characterizing individual heterogeneity and heterogeneity of individual decisions. In the last sixty years, panel data and methods of econometric analysis appropriate to such data have become increasingly important in the discipline. Unfortunately, almost all related literature focuses on models assuming that data are free of outlying or aberrant observations. This is often not the case in reality. The majority of the regression methods used in linear panel data models are very sensitive to data contamination and outliers. This doctoral thesis focuses on the estimation of linear panel data models with and without outliers. It consists of two parts. In the first part, some new estimation methods are proposed for static (Chapter 2) and dynamic (Chapter 3) models when data sets are assumed to be contaminated by outlying or aberrant observations. The second part (Chapter 4) is a contribution to the theory of estimation of dynamic models when data are assumed not to be contaminated.

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    Bibliographic Info

    Paper provided by Tilburg University in its series Open Access publications from Tilburg University with number urn:nbn:nl:ui:12-5904974.

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    Length: 119
    Date of creation: 2013
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    Publication status: Published
    Handle: RePEc:ner:tilbur:urn:nbn:nl:ui:12-5904974

    Note: Dissertation
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    Web page: http://www.tilburguniversity.edu/

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. Nerlove,Marc, 2005. "Essays in Panel Data Econometrics," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521022460.
    3. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, Elsevier, vol. 71(1), pages 1-8, April.
    4. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, Econometric Society, vol. 72(1), pages 219-255, 01.
    5. Ronchetti, Elvezio & Trojani, Fabio, 2001. "Robust inference with GMM estimators," Journal of Econometrics, Elsevier, Elsevier, vol. 101(1), pages 37-69, March.
    6. Hayakawa, Kazuhiko, 2009. "On the effect of mean-nonstationarity in dynamic panel data models," Journal of Econometrics, Elsevier, Elsevier, vol. 153(2), pages 133-135, December.
    7. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 91(1), pages 74-89, October.
    8. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, Econometric Society, vol. 56(6), pages 1371-95, November.
    9. Wagenvoort, Rien & Waldmann, Robert, 2002. "On B-robust instrumental variable estimation of the linear model with panel data," Journal of Econometrics, Elsevier, Elsevier, vol. 106(2), pages 297-324, February.
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