Functional form and spatial dependence in dynamic panels

132

a b s t r a c tWe 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.

Section: Introductionmentioning

confidence: 98%

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

Jin

2010

132

“…Our semiparametric treatment of nonlinearities is also relatively more flexible than pioneer nonlinear modeling approaches in spatial dependence models put forward by van Gastel & Paelinck (1995), Baltagi & Li (2001), Pace, Barry, Slawson & Sirmans (2004) and Yang, Li & Tse (2006). At the same time, like in all these studies as well as many others (e.g., Kelejian & Prucha, 1998, 1999Lee, 2004Lee, , 2007Su & Jin, 2010;Su, 2012), the consistency of our estimator rests on an admittedly rather restrictive assumption of a correctly pre-specified spatial weighting matrix.…”

Section: Introductionmentioning

confidence: 91%

Semiparametric estimation and testing of smooth coefficient spatial autoregressive models

Malikov

Sun²

2017

This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship. Unlike other semiparametric spatial dependence models, ours permits the spatial autoregressive parameter to meaningfully vary across units and thus allows the identification of a neighborhood-specific spatial dependence measure conditional on the vector of contextual variables. We propose several (locally) nonparametric GMM estimators for our model. The developed two-stage estimators incorporate both the linear and quadratic orthogonality conditions and are capable of accommodating a variety of data generating processes, including the instance of a pure spatially autoregressive semiparametric model with no relevant regressors as well as multiple partially linear specifications. All proposed estimators are shown to be consistent and asymptotically normal. We also contribute to the literature by putting forward two test statistics to test for parameter constancy in our model. Both tests are consistent.

“…The former has been studied by Yu et al (2008), Yu and Lee (2010), Lee and Yu (2010b), and Elhorst (2010), and the latter by Elhorst (2005), Yang et al (2006), Mutl (2006), and Su and Yang (2007). Lee and Yu (2010c) provide an excellent survey on the spatial panel data models (static and dynamic) and report some recent developments.…”

Section: Introductionmentioning

confidence: 97%

QML estimation of dynamic panel data models with spatial errors

Yang

2015

Self Cite

We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators.Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when this assumption is not met.