Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth

Abstract

This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS) estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries' openness to trade, respectively.

Putting MARS into space. Non‐linearities and spatial effects in hedonic models

“…There are alternative approaches to the MARS algorithm that allow spatial heterogeneity in spatial econometric models, such as the mixture model (Cornwall & Parent, 2017), quantile regression (Seya et al, 2020), functional‐coefficient models (Koroglu & Sun, 2016), the penalized spline combined with spatial error model (Łaszkiewicz et al, 2022), and SAR‐(M)GWR (Geniaux & Martinetti, 2018). Each of these approaches has its own strengths and weaknesses, and the choice of method will depend on the specific research question and the characteristics of the data.…”

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“…14 This method has been extensively applied in the estimation of the spatial panel model (see, for instance, Bao and Ullah (2007); Elhorst and Fréret (2009); Koroglu and Sun (2016); Lee (2004); Lee and Yu (2016); Ord (1975)). known to overcome the problem of the imprecise and inappropriate coefficients produced by the OLS method while estimating the (dynamic) spatial panel model (see, for instance, Anselin, 1988;Anselin and Hudak, 1992;Elhorst, 2003;Lee, 2004;Yu et al, 2008, among others).…”

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