Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming

By Viliam Druska, W. Horrace. American Journal of Agricultural Economics, 2004

177 citations recorded by the source corpus.

We consider estimation of a panel data model where disturbances are spatially correlated in the crosssectional dimension, based on geographic or economic proximity. When the time dimension of the data is large, spatial correlation parameters may be consistently estimated. When the time dimension is small (the usual panel data case), we develop an estimator that extends the cross‐sectional model of Kelejian and Prucha. This approach is applied in a stochastic frontier framework to a panel of Indonesian rice farms where spatial correlations represent productivity shock spillovers, based on geographic proximity and weather. These spillovers affect farm‐level efficiency estimation and ranking.

Subfields: Economics and Econometrics, General Agricultural and Biological Sciences, Management Science and Operations Research

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Citations in the corpus

Some recent developments in spatial panel data models (2010, background): “They can be applied to agricultural economics (Druska and Horrace, 2004), transportation research (Frazier and Kockelman, 2005), public economics (Egger et al., 2005), and good demand (Baltagi and Li, 2006), to name a few.”
Integrating spatial dependence into stochastic frontier analysis (2012, methodology): “Anselin 1988; LeSage 1999) very little research can be found in the literature on how to incorporate spatial dependence into technical efficiency analysis (Druska and Torrace 2004; Schmidt et al. 2009).”
A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors (2008, unknown): “…literature is Anselin (1988), and among some of the more recent contributions, such as Conley (1999), Chen & Conley (2001), Baltagi et al. (2003), Druska & Horrace (2003), Elhorst (2003), Baltagi (2005), Baltagi & Li (2006) and Pinkse et al. (2006), we highlight the work of Kapoor et al. (2007),…”
Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models (2009, methodology): “Recent applications of spatial panel data models include Druska and Horrace (2004), Arbia, Basile, and Piras (2005), Egger, Pfaffermayr, and Winner (2005), Baltagi, Egger, and Pfaffermayr (2007), and Badinger and Egger (2008a).”
A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers (2015, methodology): “Glass et al. (2013) adopt a similar approach by following Cornwell et al. (1990) (CSS from hereon) which involves using the cross-sectional speci c e¤ects from a SAR stochastic frontier model to estimate time-variant e¢ ciency.”