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Description
  • In this paper we show behaviour of some estimators for linear panel data models with autocorrelated idiosyncratic noise. This contribution then discusses properties of widely used estimators as ordinary least square, and Prais-Winsten estimator, respectively, in case of temporally correlated panel data. By a Monte Carlo study we assess the bias and efficiency of the correction methods under different data generating processes. Using Monte Carlo simulation we investigate how the performance of the OLS and GLS estimators varies according to increasing dependence in time dimension, i.e. we expect an increasing bias of the OLS estimator as temporal correlation augments.
  • In this paper we show behaviour of some estimators for linear panel data models with autocorrelated idiosyncratic noise. This contribution then discusses properties of widely used estimators as ordinary least square, and Prais-Winsten estimator, respectively, in case of temporally correlated panel data. By a Monte Carlo study we assess the bias and efficiency of the correction methods under different data generating processes. Using Monte Carlo simulation we investigate how the performance of the OLS and GLS estimators varies according to increasing dependence in time dimension, i.e. we expect an increasing bias of the OLS estimator as temporal correlation augments. (en)
Title
  • Performance of the OLS estimator in presence of autocorrelation
  • Performance of the OLS estimator in presence of autocorrelation (en)
skos:prefLabel
  • Performance of the OLS estimator in presence of autocorrelation
  • Performance of the OLS estimator in presence of autocorrelation (en)
skos:notation
  • RIV/62156489:43110/13:00205538!RIV14-MSM-43110___
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  • P(7E12049)
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  • 95784
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  • RIV/62156489:43110/13:00205538
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  • GLS; Efficiency; OLS; Monte Carlo simulation; Autocorrelation; Panel data; Biasedness; Prais-Winsten estimator (en)
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  • [98FDFA583CEC]
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  • Rhodes, Greece
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  • Melville, New York
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  • 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013, AIP Conference Proceedings
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  • Střelec, Luboš
  • Issever Grochová, Ladislava
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number of pages
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  • 10.1063/1.4825890
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  • American Institute of Physics
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  • 978-0-7354-1184-5
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  • 43110
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