On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors

By D. Drukker, Peter H. Egger, Ingmar R. Prucha. 2013

229 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d4ae24d8d700e5f59d9d52436a0c07ece6d9d3c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Global and Planetary Change

Citations in the corpus

The Slx Model (2015, background): “Liu and Lee (2013) consider the IV estimator of the SAR model and Drukker et al. (2013) the GMM estimator of the SAC model in the presence of additional endogenous regressors besides spatial interaction effects among the dependent variable.”
Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments (2019, unknown): “…(quasi-) maximum likelihood (e.g., Anselin 1988; Lee 2004; Ord 1975) or instrumental variable approaches using generalized methods of moments (e.g., Drukker, Egger, and Prucha, 2013; Kelejian and Prucha 1998, 2010), in which the endogenous lagged variables can be instrumented by q higher order…”
Creating and Managing Spatial-Weighting Matrices with the Spmat Command (2013, background): “…space is not restricted to geographic space, and many recent applications use spatial techniques in other situations of cross-unit interactions, such as social-interaction models and network models; see, for example, Kelejian and Prucha (2010) and Drukker, Egger, and Prucha (2013) for references.”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, methodology): “R> library("rgdal") R> county <- readOGR("tl_2008_us_county00", "tl_2008_us_county00") (8)We were perplexed to find that the results from fitting the ML SARAR model given by Drukker et al. (2013d), did not match those we obtained in R or Stata.”
Linear Spatial Dependence Models for Cross-Section Data (2014, methodology): “In an overview paper, Drukker et al. (2013) consider the GMM estimation of the Kelejian-Prucha model with endogenous regressors.”
US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation (2017, methodology): “Following Arraiz et al. (2010) and Drukker et al. (2013), via the use of instrumental variables and GMM, we obtain similar estimates to those of Tables 3 and 4, with no evidence of significant residual autocorrelation.”
A Command for Estimating Spatial-Autoregressive Models with Spatial-Autoregressive Disturbances and Additional Endogenous Variables (2013, methodology): “…space is not restricted to geographic space, and many recent applications employ these techniques in other situations of cross-unit dependence, such as socialinteraction models and network models; see, for example, Kelejian and Prucha (2010) and Drukker, Egger, and Prucha (2013) for references.”
Maximum Likelihood and Generalized Spatial Two-Stage Least-Squares Estimators for a Spatial-Autoregressive Model with Spatial-Autoregressive Disturbances (2013, methodology): “For a discussion of the estimation theory for the implemented GS2SLS estimator, see Arraiz et al. (2010) and Drukker, Egger, and Prucha (2013), which build on Kelejian and Prucha (1998, 1999, 2010) and the references cited therein.”
On the determinants of Airbnb location and its spatial distribution (2019, methodology): “Thus, dy/dx depends on W structure and direct and indirect effects can be disentangled (Drukker et al., 2013b).”