Robust estimation of moment condition models with weakly dependent data
This paper considers robust estimation of moment condition models with time series data. Researchers frequently use moment condition models in dynamic econometric analysis. These models are particularly useful when one wishes to avoid fully parameterizing the dynamics in the data. It is nevertheless desirable to use an estimation method that is robust against deviations from the model assumptions. For example, measurement errors can contaminate observations and thereby lead to such deviations. This is an important issue for time series data: in addition to conventional sources of mismeasurement, it is known that an inappropriate treatment of seasonality can cause serially correlated measurement errors. Efficiency is also a critical issue since time series sample sizes are often limited. This paper addresses these problems. Our estimator has three features: (i) it achieves an asymptotic optimal robust property, (ii) it treats time series dependence nonparametrically by a data blocking technique, and (iii) it is asymptotically as efficient as the optimally weighted GMM if indeed the model assumptions hold. A small scale simulation experiment suggests that our estimator performs favorably compared to other estimators including GMM, thereby supporting our theoretical findings.
|Date of creation:||Dec 2014|
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References listed on IDEAS
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- Yuichi Kitamura & Taisuke Otsu & Kirill Evdokimov, 2013.
"Robustness, Infinitesimal Neighborhoods, and Moment Restrictions,"
Econometric Society, vol. 81(3), pages 1185-1201, 05.
- Yuichi Kitamura & Taisuke Otsu & Kirill Evdokimov, 2009. "Robustness, Infinitesimal Neighborhoods, and Moment Restrictions," Cowles Foundation Discussion Papers 1720, Cowles Foundation for Research in Economics, Yale University.
- Potscher, Benedikt M & Prucha, Ingmar R, 1989. "A Uniform Law of Large Numbers for Dependent and Heterogeneous Data Processes," Econometrica, Econometric Society, vol. 57(3), pages 675-683, May.
- Potscher, Benedikt M. & Prucha, Ingmar R., 1987. "A Uniform Law of Large Numbers for Dependent and Heterogeneous Data Process," Working Papers 87-26, C.V. Starr Center for Applied Economics, New York University.
- Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-708, May.
- Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
- Susanne M. Schennach, 2007. "Point estimation with exponentially tilted empirical likelihood," Papers 0708.1874, arXiv.org.
- Ashley, Richard & Vaughan, David, 1986. "Measuring Measurement Error in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 95-103, January. Full references (including those not matched with items on IDEAS)
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