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Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error

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Abstract

We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies, or have other irregularities that cause the econometrician to observe them with mildly nonstationary noise. Least squares estimation of the cointegrating vector is consistent. Existing prototypical variancebased estimation techniques, such as canonical cointegrating regression (CCR), are both consistent and asymptotically mixed normal. This result is robust to weakly dependent but possibly nonstationary disturbances.

Suggested Citation

  • J. Isaac Miller, 2007. "Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error," Working Papers 0722, Department of Economics, University of Missouri, revised 15 Apr 2009.
  • Handle: RePEc:umc:wpaper:0722
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    File URL: https://economics.missouri.edu/working-papers/2007/wp0722_zmiller.pdf
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    References listed on IDEAS

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    Cited by:

    1. Mishra, Vinod & Smyth, Russell, 2010. "Female labor force participation and total fertility rates in the OECD: New evidence from panel cointegration and Granger causality testing," Journal of Economics and Business, Elsevier, vol. 62(1), pages 48-64, January.

    More about this item

    Keywords

    cointegration; canonical cointegrating regression; near-epoch dependence; messy data; missing data; mixed-frequency data; measurement error; interpolation;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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