ncd_gen2: Cross-sectional dependence in models
In estsyawo/RpacSPD: Spatial Panel Data models

Description Usage Arguments Value Examples

This routine computes all models considered for cross-sectional dependence using endogenously generated weight matrices. Currently "lmcd" for linear regression, "logitcd" (logit model), "poiscd" (poisson model), "probitcd" (probit model), "nbcd" (negative binomial) are supported. Note: the data should first be sorted by unit ID Pid, then by time ID Tid

1 2	ncd_gen2(pars, Y, X, Xm = NULL, Tid, Pid, wfun, k = 1, nt, utid, modclass = "lmcd", rval = "crit", eta = 0.5, dWzero = FALSE)

`pars`	vector of parameter values at which to evaluate the function
`Y`	outcome variable
`X`	matrix of covariates that generate spillovers or cross-sectional dependence
`Xm`	matrix of other control variables
`Tid`	time ID
`Pid`	unit ID
`wfun`	a user-supplied function of indices i,j, t, and δ that directly models the (i,j)'th i \not = j entry (without normalisation) of the spatial matrix at period t. This is a more flexible option vis-a-vis `fun` argument in `ncd_gen`. See example below.
`k`	order of polynomial approximation for `fun()`; this can be set as a dummy for non-polynomial forms of `fun`
`nt`	number of terms in approximation; should be of length equal to output of `fun()`
`utid`	unique time IDs to be used in the estimation; should leave at least 1 period for lagged Xi
`modclass`	the class of model. See description above for classes supported.
`rval`	character string naming the output to return; "crit" - value of criterion, "critobs" - observation-specific criterion values, squared residuals for "lmcd", negative log-likelihoods for MLE methods, etc., or "Xweight" vector X*W where W collects weight matrices through time.
`eta`	extra parameter to be passed, eg. θ for negative binomial,τ for quantile regression.
`dWzero`	logical. Should the diagonal elements of the weight matrix be set to zero? Defaults to `FALSE`. Choose option `TRUE` if own drivers `Xi` should affect the outcome

criterion value or vector of observation-specific criterion values if crit_obs=TRUE

pars=c(1.0,0.5,0.8); N = 5; Tp = 6; fnp<- function(x,y,k){-(0.5*y^4+(x-y)^4)^.25} #dummy k
datpois=gdat_cd(pars=pars,N=N,Tp=Tp,seed=2,fun=fnp,eta = 200,modclass="poiscd") #poisson data 
k=1; lp=k*(k+1)/2; startp = rep(0.2,(lp+2)) 
wfn = function(i,j,t,del){ Xi=datpois$X; xit1 = datpois$X[((i-1)*Tp + t-1)]; 
xjt1 = datpois$X[((j-1)*Tp + t-1)]; return(exp(sum(c(fnp(xit1,xjt1,k))*del)))} #weight function
wfn(2,3,4,0.5) #test the weight function
RpacSPD::ncd_gen2(pars=startp,Y=datpois$Y,X=datpois$X,Xm=NULL,Tid=datpois$tpID,
Pid=datpois$psID,wfun=wfn,k=k,nt=lp,utid=c(2:Tp),modclass="poiscd") #return function value
RpacSPD::ncd_gen2(pars=startp,Y=datpois$Y,X=datpois$X,Xm=NULL,Tid=datpois$tpID,
Pid=datpois$psID,wfun=wfn,k=k,nt=lp,utid=c(2:Tp),modclass="poiscd",rval="Xw") #return Xw