A general method for third-order bias and variance corrections on a nonlinear estimator
By Zhenlin Yang. 2015
47 citations recorded by the source corpus.
Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated by extensive Monte Carlo experiments. Compared with the analytical approach, the proposed approach is much simpler and has a much wider applicability. The validity of the bootstrap procedure is formally established. The proposed method is then extended to the case of more than one nonlinear estimator.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/c189a7cd05da1734184af4105965a03eb3196286
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In Economics and Econometrics
Citations in the corpus
- Unified M-estimation of fixed-effects spatial dynamic models with short panels (2018, methodology): “…this problem is to predict these quantities using the observed values of the regressors (Anderson and Hisao, 1981, 1982; Bhargava and Sargan, 1983; Hsiao et al. 2002; Elhorst, 2010; Su and Yang, 2015).1 However, the model for the initial differences involves the unknown process starting time.”