Conditionally Efficient Estimation of Long-run Relationships Using Mixed-frequency Time Series
AbstractI 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Missouri in its series Working Papers with number 1103.
Length: 27 pgs.
Date of creation: 19 May 2011
Date of revision: 30 May 2012
cointegration; canonical cointegrating regression; temporal aggregation; mixed-frequency series; mixed data sampling; price elasticity of gasoline demand;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-30 (All new papers)
- NEP-ECM-2011-05-30 (Econometrics)
- NEP-ETS-2011-05-30 (Econometric Time Series)
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- Eric Ghysels & J. Isaac Miller, 2013.
"Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series,"
1307, Department of Economics, University of Missouri, revised 07 May 2014.
- Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
- J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.
- Götz Thomas & Hecq Alain & Urbain Jean-Pierre, 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
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