Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error
AbstractWe 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Missouri in its series Working Papers with number 0722.
Length: 30 pgs.
Date of creation: 27 Nov 2007
Date of revision: 15 Apr 2009
cointegration; canonical cointegrating regression; near-epoch dependence; messy data; missing data; mixed-frequency data; measurement error; interpolation;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-02-02 (All new papers)
- NEP-ECM-2008-02-02 (Econometrics)
- NEP-ETS-2008-02-02 (Econometric Time Series)
- NEP-MST-2008-02-02 (Market Microstructure)
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