Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models

• Main Authors: SU, Liangjun, JIN, Sainan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Profile likelihood; Partially linear models; Quasi-maximum likelihood estimation; Spatial autoregression; Spatial dependence; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1278; https://ink.library.smu.edu.sg/context/soe_research/article/2277/viewcontent/ProfileQuasi_Max_2007_wp.pdf
• Institution: Singapore Management University

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 √n-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.