Some recent developments in spatial panel data models

By Lung-fei Lee, Jihai Yu. 2010

431 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f2dcef50ad3b8cfb359c7cb95d6d0953b8d49837

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In Economics and Econometrics

Citations in the corpus

On spatial econometric models, spillover effects, and W (2013, methodology): “Using Monte Carlo simulation experiments, Lee and Yu (2010) report that ignoring time-period fixed effects may lead to large upward biases (up to 0.45) in the coefficient of the spatial lag.”
Dynamic spatial panels: models, methods, and inferences (2012, methodology): “The main message of these studies seems to be that the bias Lee and Yu (2010c) have recently found to occur in theory may reduce so strongly that they become acceptable in practice.”
Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models (2016, background): “Lee and Yu (2010) examine recent developments in spatial panel data models for both static and dynamic cases that consider fixed effects, spatial lags and spatial disturbance specifications [for other surveys, see also Elhorst (2010, 2012)].”
Unified M-estimation of fixed-effects spatial dynamic models with short panels (2018, background): “…al., 2008), where the spatial effects may appear in the model either in the form of spatial lag(s) of the response variable (Yu et al., 2008; Yu and Lee, 2010; Lee and Yu, 2010a; Korniotis, 2010; Elhorst, 2010), or in the form of spatial errors (Elhorst, 2005; Yang et al., 2006; Mutl, 2006; Su and…”
Matlab Software for Spatial Panels (2014, methodology): “This normalization procedure is left aside in this article because of both assumption 1’ and footnote 21 in Lee and Yu (2010a).”
Tourism and regional income inequality: Evidence from China (2016, methodology): “This is also an extension from the SAR-based spatiotemporal model proposed by Lee and Yu (2010) and Elhorst et al. (2013) by incorporating spatial lags of explanatory variables.”
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models (2016, methodology): “For a survey on the panel data spatial lag model and parameter estimation, see Lee and Yu (2010).”
Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model (2010, background): “They show that even though the direct (within) approach developed in Elhorst (2003) provides consistent ρ and β coefficients, the variance of the disturbances is not consistently estimated when T is small but n is large (see Lee and Yu, 2008, 2009).”
The Slx Model (2015, methodology): “…all states that occurred during the period (e.g., policy changes and health warnings) are controlled for.30 Using Monte Carlo simulation experiments, Lee and Yu (2010) show that ignoring time-period fixed effects may lead to large upward biases (up to 0.45) in the coefficient of the spatial lag.”
A spatial panel data approach to estimating U.S. state-level energy emissions (2013, methodology): “TheHausman specification test statistic can be used to determine which estimator provides a better fit of the data (Hausman, 1978; Lee and Yu, 2010b).”