Hi,
I am trying to run a Spatial Durbin Model but I need to incorporate a robust
standard error in it since the data have heteroskedasticity, I don?t know how to
return the results with robust standard error. One other thing I would like to
do is to also return the Direct and Indirect effects with the robust standard
error. I managed to get R to show the Direct and Indirect coeffients but could
not see their standard error. If you have some insight about this, I would be
really thankful. Below are the commands I used:
#LOADING DATA FILE> nonadj <-
read.csv("C:/Users/Chi/OneDrive/Thesis/R/nonadj-percent.csv", 1)
> attach(nonadj)
#CREATING DEPENDENT AND INDEPENDENT MATRICES> Y1 <- cbind(growth)
> X1 <- cbind(pop, Net.Migra, FDI.capita, Retail.sale.thou, Vol.Freight.N,
Labor.in.business, Turnover.of.biz, Coll.Stu.pcnt, highschool, secondprim,
Hospital, cereal, fishaqua, cattlepoul)
#CREATING THE WEIGHT MATRIX (376x376)> weight1 <-
read.csv("C:/Users/Chi/OneDrive/Thesis/R/nonadjW.csv", 1)
> attach(weight1)
> W1 <- cbind(A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14,
A15, A16, A17, A18, A19, A20, A21, A22, A23, A24, A25, A26, A27, A28, A29, A30,
A31, A32, A33, A34, A35, A36, A37, A38, A39, A40, A41, A42, A43, A44, A45, A46,
A47, A48, A49, A50, A51, A52, A53, A54, A55, A56, A57, A58, A59, A60, A61, A62,
A63, A64, A65, A66, A67, A68, A69, A70, A71, A72, A73, A74, A75, A76, A77, A78,
A79, A80, A81, A82, A83, A84, A85, A86, A87, A88, A89, A90, A91, A92, A93, A94,
A95, A96, A97, A98, A99, A100, A101, A102, A103, A104, A105, A106, A107, A108,
A109, A110, A111, A112, A113, A114, A115, A116, A117, A118, A119, A120, A121,
A122, A123, A124, A125, A126, A127, A128, A129, A130, A131, A132, A133, A134,
A135, A136, A137, A138, A139, A140, A141, A142, A143, A144, A145, A146, A147,
A148, A149, A150, A151, A152, A153, A154, A155, A156, A157, A158, A159, A160,
A161, A162, A163, A164, A165, A166, A167, A168, A169, A170, A171, A172, A173,
A174, A175, A176, A177, A178, A179, A180, A181, A182, A183, A184, A185, A186,
A187, A188, A189, A190, A191, A192, A193, A194, A195, A196, A197, A198, A199,
A200, A201, A202, A203, A204, A205, A206, A207, A208, A209, A210, A211, A212,
A213, A214, A215, A216, A217, A218, A219, A220, A221, A222, A223, A224, A225,
A226, A227, A228, A229, A230, A231, A232, A233, A234, A235, A236, A237, A238,
A239, A240, A241, A242, A243, A244, A245, A246, A247, A248, A249, A250, A251,
A252, A253, A254, A255, A256, A257, A258, A259, A260, A261, A262, A263, A264,
A265, A266, A267, A268, A269, A270, A271, A272, A273, A274, A275, A276, A277,
A278, A279, A280, A281, A282, A283, A284, A285, A286, A287, A288, A289, A290,
A291, A292, A293, A294, A295, A296, A297, A298, A299, A300, A301, A302, A303,
A304, A305, A306, A307, A308, A309, A310, A311, A312, A313, A314, A315, A316,
A317, A318, A319, A320, A321, A322, A323, A324, A325, A326, A327, A328, A329,
A330, A331, A332, A333, A334, A335, A336, A337, A338, A339, A340, A341, A342,
A343, A344, A345, A346, A347, A348, A349, A350, A351, A352, A353, A354, A355,
A356, A357, A358, A359, A360, A361, A362, A363, A364, A365, A366, A367, A368,
A369, A370, A371, A372, A373, A374, A375, A376)
> W1 <- mat2listw(W1)
#RUNNING SPATIAL DURBIN REGRESSION> library(spdep)
> SDM1 <- lagsarlm(Y1~X1,data=nonadj,W1, type="mixed")
> summary (SDM1)
Call:lagsarlm(formula = Y1 ~ X1, data = nonadj, listw = W1, type =
"mixed")
Residuals:
Min 1Q Median 3Q Max
-0.245203 -0.054391 -0.010243 0.037691 0.650065
Type: mixed
Coefficients: (asymptotic standard errors)
(1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.4752e-02 1.0352e-01 0.8187 0.4129595
X1pop 8.1888e-01 8.0222e-01 1.0208 0.3073616
X1Net.Migra 1.4557e-04 5.4679e-04 0.2662 0.7900713
X1FDI.capita 1.0825e-04 7.9579e-05 1.3603 0.1737211
X1Retail.sale.thou 3.5921e-01 4.7799e-02 7.5150 5.684e-14
X1Vol.Freight.N -1.5598e-01 2.1753e-02 -7.1705 7.472e-13
X1Labor.in.business -6.2889e-01 5.2969e-02 -11.8728 < 2.2e-16
X1Turnover.of.biz -1.0088e-01 2.8196e-02 -3.5778 0.0003465
X1Coll.Stu.pcnt -2.3874e-03 1.6242e-03 -1.4699 0.1415996
X1highschool 9.7207e-02 6.0762e-02 1.5998 0.1096474
X1secondprim -2.0255e-02 1.2884e-01 -0.1572 0.8750760
X1Hospital 2.5882e-03 6.3570e-02 0.0407 0.9675240
X1cereal -3.2210e-03 6.1418e-02 -0.0524 0.9581746
X1fishaqua 2.4810e-02 3.2659e-02 0.7596 0.4474674
X1cattlepoul 4.9122e-02 3.9470e-02 1.2445 0.2133005
lag.(Intercept) NA NA NA NA
lag.X1pop 3.1168e-02 2.1213e-01 0.1469 0.8831907
lag.X1Net.Migra -1.0739e-04 3.3696e-04 -0.3187 0.7499517
lag.X1FDI.capita 2.8408e-05 9.2314e-05 0.3077 0.7582897
lag.X1Retail.sale.thou -6.2861e-02 5.8775e-02 -1.0695 0.2848380
lag.X1Vol.Freight.N 2.6938e-02 2.5154e-02 1.0709 0.2842134
lag.X1Labor.in.business -4.2901e-03 4.2330e-02 -0.1013 0.9192736
lag.X1Turnover.of.biz -5.3610e-02 2.7632e-02 -1.9401 0.0523647
lag.X1Coll.Stu.pcnt -2.6502e-03 1.5856e-03 -1.6714 0.0946416
lag.X1highschool -4.9215e-03 7.5851e-02 -0.0649 0.9482666
lag.X1secondprim -2.2008e-01 1.5629e-01 -1.4082 0.1590763
lag.X1Hospital 1.0957e-02 6.4151e-02 0.1708 0.8643748
lag.X1cereal -1.9200e-02 4.8177e-02 -0.3985 0.6902360
lag.X1fishaqua 5.3577e-02 2.4049e-02 2.2279 0.0258902
lag.X1cattlepoul -9.2296e-03 5.1168e-02 -0.1804 0.8568547
Rho: 0.021541, LR test value: 0.64222, p-value: 0.42291
Asymptotic standard error: 0.020742
z-value: 1.0385, p-value: 0.29903
Wald statistic: 1.0785, p-value: 0.29903
Log likelihood: 362.6504 for mixed model
ML residual variance (sigma squared): 0.0084969, (sigma: 0.092178)
Number of observations: 376
Number of parameters estimated: 31
AIC: -663.3, (AIC for lm: -664.66)
LM test for residual autocorrelation
test value: 1.0739, p-value: 0.30008
#DIRECT AND INDIRECT EFFECT> DirectIndirect <- impacts(SDM1, listw=W1)
> print(DirectIndirect, zstats=T)
Impact measures (mixed, exact):
Direct Indirect Total
X1pop 0.8216413667 0.4689926598 1.2906340265
X1Net.Migra 0.0001396760 -0.0010017906 -0.0008621146
X1FDI.capita 0.0001099911 0.0002953802 0.0004053713
X1Retail.sale.thou 0.3560995192 -0.5296852825 -0.1735857633
X1Vol.Freight.N -0.1546482688 0.2265607787 0.0719125099
X1Labor.in.business -0.6298927708 -0.1713948301 -0.8012876009
X1Turnover.of.biz -0.1040310437 -0.5360237512 -0.6400547949
X1Coll.Stu.pcnt -0.0025399984 -0.0259598778 -0.0284998762
X1highschool 0.0970468278 -0.0271707229 0.0698761049
X1secondprim -0.0327121352 -2.1189667965 -2.1516789317
X1Hospital 0.0032102908 0.1058274395 0.1090377303
X1cereal -0.0043095229 -0.1851649223 -0.1894744453
X1fishaqua 0.0278662464 0.5199654674 0.5478317137
X1cattlepoul 0.0486605830 -0.0785212636 -0.0298606805
Thank you for any information that you have on this!
Chi Pham
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