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Measurement Errors In Dynamic Models

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  • Komunjer, Ivana
  • Ng, Serena

Abstract

Static models that are not identifiable in the presence of white noise measurement errors are known to be potentially identifiable when the model has dynamics. However, few results are available for the plausible case of serially correlated measurement errors. This paper provides order and rank conditions for “limited information” identification of parameters in dynamic models with measurement errors where some aspects of the probability model are not fully specified or utilized. The key is to consider a model for the contaminated data that has richer dynamics than the model for the correctly observed data. Simply counting the total number of unknown parameters in the true model relative to the estimable model will not yield an informative order condition for identification. Implications for single-equation, vector autoregressive, and panel data models are studied.

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  • Komunjer, Ivana & Ng, Serena, 2014. "Measurement Errors In Dynamic Models," Econometric Theory, Cambridge University Press, vol. 30(1), pages 150-175, February.
    • Handle: RePEc:cup:etheor:v:30:y:2014:i:01:p:150-175_00
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    Citations

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    Cited by:

    1. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2018. "Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions," Journal of Econometrics, Elsevier, vol. 207(1), pages 71-91.
    2. Daniel Kaufmann, 2020. "Is deflation costly after all? The perils of erroneous historical classifications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 614-628, August.
    3. Gospodinov, Nikolay & Komunjer, Ivana & Ng, Serena, 2017. "Simulated minimum distance estimation of dynamic models with errors-in-variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 181-193.
    4. Hilbert, Martin, 2016. "The bad news is that the digital access divide is here to stay: Domestically installed bandwidths among 172 countries for 1986–2014," Telecommunications Policy, Elsevier, vol. 40(6), pages 567-581.
    5. repec:clr:wugarc:y:2016v:42i:03p:431 is not listed on IDEAS
    6. Thomas von Brasch & Diana‐Cristina Iancu & Terje Skjerpen, 2020. "Productivity Dispersion and Measurement Error," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(4), pages 985-996, December.
    7. Edith Kitzmantel, 2016. "EU-Fiskalregeln - Anker oder Mühlstein der europäischen Wirtschaftspolitik?," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 42(3), pages 431-450.
    8. Nikolay Gospodinov & Ivana Komunjer & Serena Ng, 2014. "Minimum Distance Estimation of Dynamic Models with Errors-In-Variables," FRB Atlanta Working Paper 2014-11, Federal Reserve Bank of Atlanta.
    9. Alicia N. Rambaldi & Ryan R. J. McAllister & Cameron S. Fletcher, 2015. "Decoupling land values in residential property prices: smoothing methods for hedonic imputed price indices," Discussion Papers Series 549, School of Economics, University of Queensland, Australia.
    10. Jiahe Lin & George Michailidis, 2019. "Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models," Papers 1912.04146, arXiv.org, revised May 2020.

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