HAC estimation in a spatial framework

By Harry H. Kelejian, Ingmar R. Prucha. 2007

440 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/97188d55da460896bca71ae6bdd5f6d6796cfcc3

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In Economics and Econometrics

Citations in the corpus

Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2010, background): “However, explicit proofs are given in the underlyingworking paper, Kelejian and Prucha (2007b), which can be obtained from the authors.”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, background): “Kelejian and Prucha (2007) suggest estimating the individual r, s elements of Ψ as where the subscripts refer to the elements of the matrix of instruments H and the vector of estimated residuals ˆ ε .”
Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria (2014, methodology): “Homoskedasticity, as stipulated in both the OLS and the SAR model, is a strong and restrictive assumption (Kelejian and Prucha, 2007).”
PySAL: A Python Library of Spatial Analytical Methods (2010, background): “In addition to the traditional estimates of standard errors and a heteroskedastic robust form (White 1980; Anselin 1988), this also implements the recently suggested heteroskedastic and spatial autocorrelation robust form, or HAC estimator (Kelejian and Prucha 2007).”
Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models (2009, methodology): “Kelejian and Prucha (2008) and Arraiz, Drukker, Kelejian and Prucha (2007) use the untransformed instrument matrix NH in the FGTSLS estimation of cross-section SARAR(1,1) models.”
Estimating peer effects in networks with peer encouragement designs (2016, methodology): “(18) randomize the number of personalized social cues in advertisements.”
Large Panels with Common Factors and Spatial Correlation (2011, methodology): “To further investigate this issue, we have run some additional experiments using Kelejian and Prucha (2007) Monte Carlo design, with T = 1, N = 100; 200; 400; 1024.”
EFFICIENT GMM ESTIMATION OF HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS WITH AUTOREGRESSIVE DISTURBANCES (2009, methodology): “Kelejian and Prucha (2007a) considered the IV estimation of the firstorder MRSAR model allowing the disturbance process for general patterns of correlation and heteroskedasticity, and proposed a spatial heteroskedasticity and autocorrelation consistent (HAC) estimator for the variance–covariance…”