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.
Highlights
Ever since the seminal work of [1], there has been a significant amount of empirical work studying variation in economic growth rates across countries
As for the average direct impact (ADI) and average indirect impact (AII), we see an overall decreasing pattern in the RMSEs as the sample size increases, where the AII is less accurately estimated than the ADI, as the former is calculated from n (n − 1) terms and the latter is calculated from n elements only
For the economies with a trade openness higher than 15% of GDP, our result indicates that the nonparametric model sees stronger positive impact of the investment rate on the real GDP per capita growth rate than the parametric model does
Summary
Ever since the seminal work of [1], there has been a significant amount of empirical work studying variation in economic growth rates across countries. Ertur and Koch [4] derive a theoretical Solow economic growth model augmented with global technological interdependence They approximate their theoretical model by a parametric spatial Durbin model via a linearization procedure and calculate the spatial weights from both the inverse power function and the exponential function of geographic distances with α = 2 for the sake of robustness, as the true spatial weighting function is unknown. As the linearization and selected parametric spatial weights may result in a model misspecification problem, in this paper, in order to better approximate [4]’s theoretical model, we propose a semiparametric growth model that extends [4]’s parametric model by allowing nonparametric spatial weights, as well as varying Solow coefficients.
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