Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models
By Liangjun Su, Sainan Jin. 2010
171 citations recorded by the source corpus.
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are partially linear. The rate of convergence of the spatial parameter estimator depends on some general features of the spatial weight matrix of the model. The estimators of other finite-dimensional parameters in the model have the regular -rate of convergence and the estimator of the nonparametric component is consistent but with different restrictions on the choice of bandwidth parameter associated with different natures of the spatial weights. Monte Carlo simulations verify our theory and indicate that our estimators perform reasonably well in finite samples.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/c8ef9cc64418dd223396e13eb9dc89dbe13ed8c5
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In Economics and Econometrics
Citations in the corpus
- GENERALIZED ADDITIVE PARTIAL LINEAR MODELS WITH HIGH-DIMENSIONAL COVARIATES (2013, background): “…partially linear models (PLM), for which there is much work in the literature (Chen, 1988; Engle, Granger, Rice, and Weiss, 1986; Härdle, Liang, and Gao, 2000; Heckman, 1986; Juhl and Xiao, 2005; Li and Stengos, 1996; Li and Wooldridge, 2002; Robinson, 1988; Speckman, 1988; Su and Jin, 2010).”
- A general method for third-order bias and variance corrections on a nonlinear estimator (2015, unknown): “…et al. (1993), Besley and Case (1995), Brueckner (1998), Anselin and Bera (1998), Kelejian and Prucha (1998, 1999, 2001), Bell and Bockstael (2000), Bertrand, et al. (2000), Topa (2001), Lee (2002, 2003, 2004a, 2007a,b), Mynbaev and Ullah (2008), Robinson (2010), Su and Jin (2010), Su (2012), etc..”
- Semiparametric GMM estimation of spatial autoregressive models (2012, methodology): “Recently, Su and Jin (2010) propose a quasi-maximum-likelihood-based estimator of partially linear spatial autoregressive models and demonstrate that the rates of consistency for the finitedimensional parameters in the model depend on some general features of the spatial weight matrix.”
- Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, background): “At the same time, like in all these studies as well as many others (e.g., Kelejian & Prucha, 1998, 1999, 2010; Lee, 2004, 2007; Su & Jin, 2010; Su, 2012), the consistency of our estimator rests on an admittedly rather restrictive assumption of a correctly pre-specified spatial weighting matrix.”
- Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling (2013, methodology): “To this goal, one can follow the idea of profile least squares or likelihood (e.g., Fan and Huang 2005; Su and Jin 2010) and extend it to our local linear GMM framework.”
- Spatial weights matrix selection and model averaging for spatial autoregressive models (2017, background): “Also, the current paper considers only the SAR structure in a linear model with cross sectional data, and extensions of weights matrix selection to consider nonlinear covariate in an SAR model (see Su and Jin, 2010; Sun et al., 2014) or panel data setting would be of interest.”
- A semiparametric spatial dynamic model (2014, methodology): “Such method to define spatial neighboring effect is common; see Ord [12], Anselin [1], Su and Jin [13].”
- Functional-coefficient spatial autoregressive models with nonparametric spatial weights (2016, methodology): “For example, in the spatial autoregressive framework, Su and Jin (2010) and Zhang (2013) assume that Xi enters into the reaction function in a partially linear form, while Su (2012) considers a reaction function with a nonparamet- ric curve of Xi.”