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Conditionally Efficient Estimation of Long-run Relationships Using Mixed-frequency Time Series

J. Miller ()

No 1103, Working Papers from Department of Economics, University of Missouri

Abstract: I analyze efficient estimation of a cointegrating vector when the regressand is observed at a lower frequency than the regressors. Previous authors have examined the effects of specific temporal aggregation or sampling schemes, finding conventionally efficient techniques to be efficient only when both the regressand and the regressors are average sampled. Using an alternative method for analyzing aggregation under more general weighting schemes, I derive an efficiency bound that is conditional on the type of aggregation used on the regressand and differs from the unconditional bound defined by the infeasible full-information high-frequency data-generating process. I modify a conventional estimator, canonical cointegrating regression (CCR), to accommodate cases in which the aggregation weights are either unknown or known. In the unknown case, the correlation structure of the error term generally confounds identification of the conditionally efficient weights. In the known case, the correlation structure may be utilized to offset the potential information loss from aggregation, resulting in a conditionally efficient estimator. Efficiency is illustrated using a simulation study and an application to estimating a gasoline demand equation.

Keywords: cointegration; canonical cointegrating regression; temporal aggregation; mixed-frequency series; mixed data sampling; price elasticity of gasoline demand (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2011-05-19, Revised 2012-05-30
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Forthcoming in Econometric Reviews

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Journal Article: Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series (2016) Downloads
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