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Spatial Economic Analysis Volume 3, 2008 - Issue 1

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Original Articles

A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors

Bernard FingletonCorrespondence[email protected]

Pages 27-44 | Received 01 Jan 2006, Published online: 18 Feb 2008

Cite this article
https://doi.org/10.1080/17421770701774922

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Table 1 . Estimated parameter distributions

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Table 2 . Estimated parameter distributions

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Table 3 . GMM estimates for the real estate price panel data with spatial moving average errors

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Table A1 . Commuting distances to work in England and Wales

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Table A2 . Bias and RMSE with increasing lattice size, positive dependence, Rook's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Display Table

Table A3 . Bias and RMSE with increasing lattice size, with negative dependence, Rook's case. ρ=0.5, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10 , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

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Table A4 . Bias with increases in the time dimension, positive dependence, Rook's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Rook's case contiguity matrix for a 7×7 square, hence the number of locations is N=49, and W is of dimension 49×49. The number of layers is from 3 up to 10

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Table A5 . Bias with increasing positive dependence, Rook's case. λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , , T=2. Matrix W is a Rook's case contiguity matrix for a 7×7 square, hence the number of locations is N=49, and W is of dimension 49×49. There are two layers (T=2)

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Table A6 . Bias with increasing positive dependence, Queen's case. λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , , T=2. Matrix W is a Queen's case contiguity matrix for a 9×9 square, hence the number of locations is N=81, and W is of dimension 81×81. There are two layers (T=2)

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Table A7 . Bias and RMSE with increasing lattice size, with positive dependence, Queen's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Queen's case contiguity matrix. There are two layers (T=2)

Display Table

Table A8 . Bias with increasing lattice size, with positive dependence, torus. ρ=−0.75, λ=0.25, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a torus, in other words a Rook's case contiguity matrix with no edges. There are three layers (T=3)

Display Table

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A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors

Table 1 . Estimated parameter distributions

Table 2 . Estimated parameter distributions

Table 3 . GMM estimates for the real estate price panel data with spatial moving average errors

Table A1 . Commuting distances to work in England and Wales

Table A2 . Bias and RMSE with increasing lattice size, positive dependence, Rook's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Table A3 . Bias and RMSE with increasing lattice size, with negative dependence, Rook's case. ρ=0.5, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10 , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Table A5 . Bias with increasing positive dependence, Rook's case. λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , , T=2. Matrix W is a Rook's case contiguity matrix for a 7×7 square, hence the number of locations is N=49, and W is of dimension 49×49. There are two layers (T=2)

Table A6 . Bias with increasing positive dependence, Queen's case. λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , , T=2. Matrix W is a Queen's case contiguity matrix for a 9×9 square, hence the number of locations is N=81, and W is of dimension 81×81. There are two layers (T=2)

Table A7 . Bias and RMSE with increasing lattice size, with positive dependence, Queen's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Queen's case contiguity matrix. There are two layers (T=2)

Table A8 . Bias with increasing lattice size, with positive dependence, torus. ρ=−0.75, λ=0.25, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a torus, in other words a Rook's case contiguity matrix with no edges. There are three layers (T=3)

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A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors

Figures & data

Table 1 . Estimated parameter distributions

Table 2 . Estimated parameter distributions

Table 3 . GMM estimates for the real estate price panel data with spatial moving average errors

Table A1 . Commuting distances to work in England and Wales

Table A2 . Bias and RMSE with increasing lattice size, positive dependence, Rook's case. ρ=−0.25, λ=0.75, γ 0=1, γ 1=10, γ 2=10, γ 3=10, , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Table A3 . Bias and RMSE with increasing lattice size, with negative dependence, Rook's case. ρ=0.5, λ=0.75, γ 0=1, γ 1=10, γ 2=10, γ 3=10 , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Table A5 . Bias with increasing positive dependence, Rook's case. λ=0.75, γ 0=1, γ 1=10, γ 2=10, γ 3=10, , , T=2. Matrix W is a Rook's case contiguity matrix for a 7×7 square, hence the number of locations is N=49, and W is of dimension 49×49. There are two layers (T=2)

Table A6 . Bias with increasing positive dependence, Queen's case. λ=0.75, γ 0=1, γ 1=10, γ 2=10, γ 3=10, , , T=2. Matrix W is a Queen's case contiguity matrix for a 9×9 square, hence the number of locations is N=81, and W is of dimension 81×81. There are two layers (T=2)

Table A7 . Bias and RMSE with increasing lattice size, with positive dependence, Queen's case. ρ=−0.25, λ=0.75, γ 0=1, γ 1=10, γ 2=10, γ 3=10, , . Matrix W is a Queen's case contiguity matrix. There are two layers (T=2)

Table A8 . Bias with increasing lattice size, with positive dependence, torus. ρ=−0.75, λ=0.25, γ 0=1, γ 1=10, γ 2=10, γ 3=10, , . Matrix W is a torus, in other words a Rook's case contiguity matrix with no edges. There are three layers (T=3)

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Table A2 . Bias and RMSE with increasing lattice size, positive dependence, Rook's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Table A3 . Bias and RMSE with increasing lattice size, with negative dependence, Rook's case. ρ=0.5, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10 , . Matrix W is a Rook's case contiguity matrix. There are two layers (T=2)

Table A5 . Bias with increasing positive dependence, Rook's case. λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , , T=2. Matrix W is a Rook's case contiguity matrix for a 7×7 square, hence the number of locations is N=49, and W is of dimension 49×49. There are two layers (T=2)

Table A6 . Bias with increasing positive dependence, Queen's case. λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , , T=2. Matrix W is a Queen's case contiguity matrix for a 9×9 square, hence the number of locations is N=81, and W is of dimension 81×81. There are two layers (T=2)

Table A7 . Bias and RMSE with increasing lattice size, with positive dependence, Queen's case. ρ=−0.25, λ=0.75, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a Queen's case contiguity matrix. There are two layers (T=2)

Table A8 . Bias with increasing lattice size, with positive dependence, torus. ρ=−0.75, λ=0.25, γ ₀=1, γ ₁=10, γ ₂=10, γ ₃=10, , . Matrix W is a torus, in other words a Rook's case contiguity matrix with no edges. There are three layers (T=3)