Model averaging with covariates that are missing completely at random
Missing data is a common problem in economics studies. We propose using Mallows model averaging (MMA) to deal with this problem, which has an important advantage over its competitors in that it asymptotically achieves the lowest possible squared error. A simulation study in comparison with existing methods strongly favors the MMA estimator.
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- repec:taf:jnlbes:v:30:y:2012:i:1:p:132-142 is not listed on IDEAS
- Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, 07.
- Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2011.
"Regression with imputed covariates: A generalized missing-indicator approach,"
- Dardanoni, Valentino & Modica, Salvatore & Peracchi, Franco, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Journal of Econometrics, Elsevier, vol. 162(2), pages 362-368, June.
- Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Post-Print peer-00815561, HAL.
- Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2009. "Regression with Imputed Covariates:a Generalized Missing Indicator Approach," CEIS Research Paper 150, Tor Vergata University, CEIS, revised 08 Oct 2009.
- Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
- Schomaker, Michael & Wan, Alan T.K. & Heumann, Christian, 2010. "Frequentist Model Averaging with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3336-3347, December.
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