SLX模型-基于splm软件包和渣函数的R数据面板空间计量方法

Question

我需要估计空间经济计量模型的空间滞后X (SLX)，或单独结合空间自回归模型(SAR)或空间误差模型(SEM)。当它们结合在一起时，它们被称为空间Durbin模型(SDM)或空间Durbin错误模型(SDEM)，这是根据Vega &Elhorst (2015)的论文“SLX模型”进行的。

我打算使用苏丹人民解放运动软件包估计R中的所有空间面板模型，这也需要spdep函数。在这个意义上，我从一个形状文件中创建了类型为Queen和k=4的邻居列表：

> TCAL <- readOGR(dsn = ".", "Municipios_csv")
> coords <- coordinates(TCAL)

> contnbQueen <- poly2nb(TCAL, queen = TRUE)
> enter code herecontnbk4    <- knn2nb(knearneigh(coords, k = 4, RANN = FALSE))

然后，我在一个权重矩阵中转换了这个邻居列表：

> W <- nb2listw(contnbk4, glist = NULL, style = "W")

attributes(W)
$names
[1] "style"      "neighbours" "weights"   

$class
[1] "listw" "nb"   

$region.id
 [1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"   "9"   "10"  "11"  "12"  "13"  "14"  "15"  "16"  "17"  "18"  "19"  "20" 
 [21] "21"  "22"  "23"  "24"  "25"  "26"  "27"  "28"  "29"  "30"  "31"  "32"  "33"  "34"  "35"  "36"  "37"  "38"  "39"  "40" 
 [41] "41"  "42"  "43"  "44"  "45"  "46"  "47"  "48"  "49"  "50"  "51"  "52"  "53"  "54"  "55"  "56"  "57"  "58"  "59"  "60" 
 [61] "61"  "62"  "63"  "64"  "65"  "66"  "67"  "68"  "69"  "70"  "71"  "72"  "73"  "74"  "75"  "76"  "77"  "78"  "79"  "80" 
 [81] "81"  "82"  "83"  "84"  "85"  "86"  "87"  "88"  "89"  "90"  "91"  "92"  "93"  "94"  "95"  "96"  "97"  "98"  "99"  "100"
[101] "101" "102" "103" "104" "105" "106" "107" "108" "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
[121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133" "134" "135" "136" "137" "138" "139" "140"
[141] "141" "142" "143" "144" "145" "146" "147" "148" "149" "150" "151" "152" "153" "154" "155" "156" "157" "158" "159" "160"
[161] "161" "162" "163" "164" "165" "166" "167" "168" "169" "170" "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
[181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190" "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
[201] "201" "202" "203" "204" "205" "206" "207" "208" "209" "210" "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
[221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230" "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
[241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250" "251" "252" "253" "254" "255" "256" "257" "258" "259" "260"
[261] "261" "262" "263" "264" "265" "266" "267" "268" "269" "270" "271" "272" "273" "274" "275" "276"

    $call
    nb2listw(neighbours = contnbk7, glist = NULL, style = "W")

下一步，我为面板SAR和SEM模型创建了一个公式，该公式运行良好，并产生了估计值：

> fmPanel <- Area ~ Dist + Land + CredAg
> vegSAR <- spml(fmPanel, data = veg, index = c("Mun","Year"), listw = W, model = "within", effect = "twoways", spatial.error = "none", lag = TRUE)
> vegSEM <- spml(fmPanel, data = veg, index = c("Mun","Year"), listw = W, model = "within", effect = "twoways", spatial.error = "b", lag = FALSE)

然后，我试图通过创建协变量X的空间滞后来估计SLX、SDM和SDEM模型：

> vegX <- pdata.frame(veg, index = c("Mun","Year")); class(vegX)

    [1] "pdata.frame" "data.frame"

然后我创建了pseries值：

> DistX <- vegX$Dist; class(DistX)
    [1] "pseries" "numeric"
> LandX <- vegX$Land; class(LandX)
    [1] "pseries" "numeric"
> CredAgX <- vegX$CredAg; class(CredAgX)
    [1] "pseries" "numeric"

但是，当我应用渣函数时，出现了错误：

DistX <- slag(agSPX$Dist, listw = W)

    Error in lag.listw(listw, xt) : object lengths differ

我的面板数据有5年276个区域。因此，对象的特征是：

> length(DistX)
[1] 1380

> length(W)
[1] 3

> length(W$weights)
[1] 276

所以，我想知道，如果我能在一个矩阵中转换W$权值，比如usaww作为渣函数的例子，我可以应用函数mat2listw，然后在X上使用渣。

有人能告诉我我哪里错了吗？

Answer 1

还可以将滞后解释变量添加到数据中。

可复制的例子：

library(plm)
library(spatialreg)
library(splm)

# load data
data(Produc, package = "plm")
data(usaww, package = "splm")

d <- pdata.frame(Produc, index = c("state","year"), drop.index = FALSE)

# create a spatial explanatory variable
d$unemp_l <- slag(d$unemp, usaww)

# run model
m <- splm::spml(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp + unemp_l,
                  data = d, listw = mat2listw(usaww) , model="within")
summary(m)
Spatial panel fixed effects error model


Call:
splm::spml(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + 
    unemp + unemp_l, data = d, listw = mat2listw(usaww), model = "within")

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-0.1211492 -0.0234013 -0.0040218  0.0167919  0.1787587 

Spatial error parameter:
    Estimate Std. Error t-value  Pr(>|t|)    
rho 0.542254   0.033772  16.056 < 2.2e-16 ***

Coefficients:
            Estimate Std. Error t-value Pr(>|t|)    
log(pcap)  0.0090575  0.0251036  0.3608  0.71824    
log(pc)    0.2152367  0.0234077  9.1951  < 2e-16 ***
log(emp)   0.7833003  0.0277672 28.2096  < 2e-16 ***
unemp     -0.0014795  0.0011443 -1.2930  0.19603    
unemp_l   -0.0031210  0.0015790 -1.9766  0.04808 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

您可以验证空间解释是否正确。例如：

a <- usaww["ALABAMA",]
a <- a[a!=0]
a
    FLORIDA     GEORGIA MISSISSIPPI    TENNESSE 
       0.25        0.25        0.25        0.25 
mean(d[d$year=="1970" & d$state %in% names(a) , "unemp"])
[1] 4.525

d[d$state=="ALABAMA" & d$year=="1970", "unemp_l"]
ALABAMA-1970 
   4.525 

Answer 2

也许这不是最好的解决方案，但我使用以下步骤计算了SLX、SDM和SDEM模型

1)通过以下方式加载形状文件：

TCAL <- readOGR(dsn = ".", "Municipios_csv_BIO")
coords <- coordinates(TCAL)

2)通过以下方法创建加权矩阵W：

contnbQueen  <- poly2nb(TCAL, queen = TRUE)
contnbk4     <- knn2nb(knearneigh(coords, k = 4,  RANN = FALSE))

3)选择要应用的矩阵：

W <- nb2listw(contnbk4, glist = NULL, style = "W")

4)在pdata.frame中转换data.frame：

vegSPX  <- pdata.frame(vegPainel, index = c("ID","Ano"))

5)创建特定的pdata.frame空间向量，例如：

vegIDDX  <- vegSPX$IDD
vegSoilX <- vegSPX$Soil
vegQAIX  <- vegSPX$QAI

6)为这些模型指定了一个公式：

fmSPvegX <- Area ~ IDD + Soil + QAI + slag(vegIDDX, listw = W) + slag(vegSoilX, listw = W) + slag(vegQAIX, listw = W)

7)在原始数据(data.table data.frame对象)上应用plm和splm函数的：

vegSLX<-plm(fmSPvegX,data=vegPainel,listw=W,index=c("ID","Ano"),model="within",effect="twoways",spatial.error="none",lag=F)
vegSARX<-spml(fmSPvegX,data=vegPainel,listw=W,index=c("ID","Ano"),model="within",effect="twoways",spatial.error="none",lag=T)
vegSEMX<-spml(fmSPvegX,data=vegPainel,listw=W,index=c("ID","Ano"),model="within",effect="twoways",spatial.error="b",lag=F)

我希望这能帮上忙。