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

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

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/2541

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Title: Cointegrating Regressions with Messy Regressors: Missingness, Mixed Frequency, and Measurement Error
Author: Miller, J. Isaac
Keywords: messy data
missing data
Date: 2009-04-14
Publisher: Department of Economics
Citation: Department of Economics, 2009
Series/Report no.: Working papers (Department of Economics);WP 07-22
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 variance-based 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.
URI: http://hdl.handle.net/10355/2541

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  • Economics publications (MU) [120]
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