Locally robust panel Lagrange Multiplier tests for spatial dependence
Locally robust LM tests for spatial lag (error) correlation sub spatial error (lag) correlation in panel models
slmtest(x,...)
## S3 method for class 'formula'
slmtest(formula, data, listw, model="pooling",
test=c("lme","lml","rlme","rlml"), index=NULL, ...)
## S3 method for class 'plm'
slmtest(x, listw,
test=c("lme","lml","rlme","rlml"), ...)formula |
an object of class |
data |
a |
x |
an object of class |
listw |
either a |
model |
a character value specifying the transformation to be applied to the data. |
test |
one of |
index |
either NULL (default) or a character vector to identify the indexes among the columns of the |
... |
additional arguments to be passed |
This tests are panel versions of the locally robust LM tests of Anselin et al. (1996), based on a pooling assumption: i.e., they do not allow for any kind of individual effect. Therefore it is advisable to employ a within transformation whenever individual effects cannot be ruled out.
It must be kept in mind that these locally robust procedures have been designed for situations in which the "other" effect is not of substantial magnitude, and can behave suboptimally otherwise.
Four tests are available to be chosen through the test
argument: "lml" for "LM lag" and, respectively, "lme"
for "LM error" are the standard, non-robust versions, obtained simply
pooling the cross-sectional versions; "rlml" and "rlme"
are, respectively, the locally robust test for lag, allowing for a
spatial error; and for error, allowing for a spatial lag.
The model argument, specified according to the standards of
plm, is passed on internally and employed to determine the panel
data transformation to be applied before calculating the test. Defaults
to "pooling" (no transformation).
an object of class htest
Giovanni Millo
Anselin, L., Bera, A.K., Florax, R. and Yoon, M.J. (1996) Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77-104. Elhorst, J.P. (2014) Spatial Panel data Models, in Spatial Econometrics (Springer) 37-93.
data(Produc, package="plm") data(usaww) fm <- log(gsp)~log(pcap)+log(pc)+log(emp)+unemp ## robust LM test for spatial error sub spatial lag ## model on original data, pooling hypothesis slmtest(fm, data=Produc, listw = usaww, test="rlme") ## model on within-transformed (time-demeaned) data, ## eliminates individual effects slmtest(fm, data=Produc, listw = usaww, test="rlme", model="within")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.