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Is the Use of Autocovariances in Level the Best in Estimating the Income Processes? A Simulation Study

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  • Chau, Tak Wai

Abstract

In this simulation study, I compare the efficiency and finite sample bias of parameter estimators for popular income dynamic models using various forms of autocovariances. The dynamic models have a random walk or a heterogeneous growth permanent component, a persistent autoregressive component and a white noise transitory component. I compare the estimators using autocovariances in level, first differences (FD), and autocovariances between level and future first differences (LD), where the last one is new in the literature of income dynamics. To maintain the same information used as in using level covariances, I also augment the FD and LD covariances with level variances in the estimation. The results show that using level covariances can give rise to larger finite sample biases and larger standard errors than using covariances in FD and LD augmented by level variance. Without augmenting the level variances, LD provides more efficient estimators than FD in estimating the non-permanent components. I also show that LD provides a convenient test between random walk and heterogeneous growth models with good power.

Suggested Citation

  • Chau, Tak Wai, 2013. "Is the Use of Autocovariances in Level the Best in Estimating the Income Processes? A Simulation Study," MPRA Paper 44106, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:44106
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    File URL: https://mpra.ub.uni-muenchen.de/44106/1/MPRA_paper_44106.pdf
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    References listed on IDEAS

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    1. Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
    2. Baker, Michael, 1997. "Growth-Rate Heterogeneity and the Covariance Structure of Life-Cycle Earnings," Journal of Labor Economics, University of Chicago Press, vol. 15(2), pages 338-375, April.
    3. Richard Blundell & Luigi Pistaferri & Ian Preston, 2008. "Consumption Inequality and Partial Insurance," American Economic Review, American Economic Association, vol. 98(5), pages 1887-1921, December.
    4. Dmytro Hryshko, 2012. "Labor income profiles are not heterogeneous: Evidence from income growth rates," Quantitative Economics, Econometric Society, vol. 3(2), pages 177-209, July.
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    More about this item

    Keywords

    covariance structure; income dynamics; random walk; heterogeneous growth profi le; finite sample bias; efficiency;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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