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Some consequences of using "measurement error shocks" when estimating time series models

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  • Adrian Pagan

Abstract

In a number of time times models there are I(1) variables that appear in data sets in differenced from. This note shows that an emerging practice of assuming that observed data relates to model variables through the use of “measurement error shocks” when estimating these models can imply that there is a lack of co-integration between model and data variables, and also between data variables themselves. An analysis is provided of what the nature of the measurement error would need to be if it was desired to reproduce the same co-integration information as seen in the data. Sometimes this adjustment can be complex. It is very unlikely that measurement error can be described properly with the white noise shocks that are commonly used for measurement error.

Suggested Citation

  • Adrian Pagan, 2017. "Some consequences of using "measurement error shocks" when estimating time series models," CAMA Working Papers 2017-12, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2017-12
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-02/12_2017_pagan.pdf
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    Cited by:

    1. Alban Moura, 2020. "LED: An estimated DSGE model of the Luxembourg economy for policy analysis," BCL working papers 147, Central Bank of Luxembourg.
    2. Adrian Pagan & Tim Robinson, 2019. "Implications of Partial Information for Applied Macroeconomic Modelling," Melbourne Institute Working Paper Series wp2019n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    3. Xianglong Liu & Adrian R. Pagan & Tim Robinson, 2018. "Critically Assessing Estimated DSGE Models: A Case Study of a Multi‐sector Model," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 349-371, December.

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