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Distribution dynamics and measurement error

In: Measurement Error: Consequences, Applications and Solutions

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  • Ole Rummel

Abstract

This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally ignore the fact that per capita income data from the Penn World Table (PWT) are not only continuous variables but also measured with error. Together with short-time scale fluctuations, measurement error makes inferences potentially unreliable. When first-order, time-homogeneous Markov models are fitted to continuous data with measurement error, a bias towards excess mobility is introduced into the estimated transition probability matrix. This chapter evaluates different methods of accounting for this error. An EM algorithm is used for parameter estimation, and the methods are illustrated using data from the PWT Mark 6.1. Measurement error in income data is found to have quantitatively important effects on distribution dynamics. For instance, purging the data of measurement error reduces estimated transition intensities by between one- and four-fifths and more than halves the observed mobility of countries.

Suggested Citation

  • Ole Rummel, 2009. "Distribution dynamics and measurement error," Advances in Econometrics, in: Measurement Error: Consequences, Applications and Solutions, pages 251-279, Emerald Group Publishing Limited.
  • Handle: RePEc:eme:aecozz:s0731-9053(2009)0000024014
    DOI: 10.1108/S0731-9053(2009)0000024014
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