2SLS and OLS in a spatial autoregressive model with equal spatial weights

By Harry H. Kelejian, Ingmar R. Prucha. 2002

110 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f0648778e71082105771d02c20bdd90a02a4fd50

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In Economics and Econometrics

Citations in the corpus

Estimation Problems in Models with Spatial Weighting Matrices Which Have Blocks of Equal Elements (2006, unknown): “Kelejian and Prucha (2002) demonstrated that if aN = 1N− 1 , then p limN→∞|̂N| = ∞, and hence they noted that the 2SLS estimator in this case is inconsistent.5 Their result is clearly consistent with Theorem 1 since |̂N| = 1/aN = N − 1.”
On model specification and parameter space definitions in higher order spatial econometric models (2012, background): “4 See also Kelejian and Prucha (2002), Kelejian et al. (2004), Lee (2002, 2003) among others.”
Estimation of simultaneous systems of spatially interrelated cross sectional equations (2004, background): “5 See also Kelejian and Prucha (2002) for another case in which part (b) of Assumption 6 is violated.”
Does industrial upgrading promote eco-efficiency? ─A panel space estimation based on Chinese evidence (2021, background): “Kelejian and Prucha (2002), Kelejian et al. (2006), and Lee and Yu 1 Industrial rationalization and industrial supererogation are two dimensions to measure the quality of the industrial structure of an economy.”
Thirty years of spatial econometrics (2010, background): “Some specifications are a special case of a standard model, such as the spatial lag model with equal weights (i.e., where all observations are neighbours of each other) considered by Kelejian and Prucha (2002); and Lee (2002).”
Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling (2016, methodology): “SEDAC (see table 1 and figure 2). In other words, Eq.(1) is estimated while assuming t = 1995. Since the sample size N is very large (67,934), Eq.(1) is estimated by the spatial 2-step least squares (Kelejian and Prucha, 2002), which is a computationally efficient estimation method for spatial econo”
Specification and Estimation of Spatial Panel Data Models (2003, background): “Finally, it is possible that these conditions are not sufficient unless panel data are available (Kelejian and Prucha 2002).”
Dynamic spatial panels: models, methods, and inferences (2012, background): “By contrast, in a panel data setting, this spatial weights matrix causes no problems, provided that time-period effects are not included (see Kelejian and Prucha 2002; Kelejian et al. 2006).”
Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2010, unknown): “…2001a,b), Baltagi et al. (2007b, 2003), Bao and Ullah (2007), Conley (1999), Das et al. (2003), Driscoll and Kraay (1998), Kapoor et al. (2007), Kelejian and Prucha (2007a, 2004, 2002, 2001, 1999, 1998), Korniotis (2005), Lee (2007a,b, 2004, 2003, 2002), Pinkse and Slade (1998), Pinkse et al.…”
A spatial analysis of international stock market linkages (2013, background): “Ma and Kao (1990) find a positive relationship between currency and stock price movements, whereas Friberg and Nydahl (1999) document a negative relationship.”
Small Sample Properties of Maximum Likelihood Versus Generalized Method of Moments Based Tests for Spatially Autocorrelated Errors (2009, background): “…with spatially autocorrelated errors distinguishes between two estimation principles: maximum likelihood estimation (henceforth MLE; Cliff and Ord, 1981; Anselin, 1988; Lee, 2004) and estimation by the generalized method of moments (henceforth GMM; Conley, 1999; Kelejian and Prucha, 1999, 2002).”
FDI determination and corporate tax competition in a volatile world (2010, methodology): “In all cases, the exogenous determinants of corporate tax rates (vector x � jt ) weighted by matrix Wt ={ wij t } and their lags are used as instruments for the endogenous average tax n =1 wij t τ jt ( Kelejian and Prucha 2002 ).”