# demo/logRV.R In midasr: Mixed Data Sampling Regression

```##More details about the models can be found in the article
##"The statistical content and empirical testing of the MIDAS restrictions"
##by Virmantas Kvedaras and Vaidotas Zemlys

library(midasr)
data(rvsp500)

ii <- which(rvsp500\$DateID=="20120522")
y <- log(as.numeric(rvsp500[1:ii,2]))

nlmn <- function(p,d,m) {
i <- 1:d/100
plc <- poly(i,degree=length(p)-1,raw=TRUE) %*% p[-1]
as.vector(p[1] * exp(plc)/sum(exp(plc)))
}

##Convergence is harder to achieve, so try to pick better starting values
prestart <- function(start,cfur,k) {
fn0 <- function(p) {sum((cfur-nlmn(p,k))^2)}
optim(start,fn0,method="BFGS",control=list(maxit=1000))\$par
}

rvhk <- function(h,k){
rvh <- filter(c(rep(0,h),y),c(rep(1,h),rep(0,h+1)))
rvh <- rvh[-h:-1]
y <- y[1:length(rvh)]
mu <- midas_u(rvh~fmls(y,k,1))
cfur <- coef(mu)[grep("fmls",names(coef(mu)))]
update(midas_r(rvh~fmls(y,k,1,nlmn),start=list(y=prestart(c(0.2,-1,1),cfur,k+1))),Ofunction="nls")
}

allh <- lapply(c(5,10,20,40),rvhk,k=69)

####Compute the derivative test
dtest <- lapply(allh,deriv_tests,tol=0.5)

###The first derivative tests, gradient is zero
sapply(dtest,with,first)

###The second derivative tests, hessian is positive definite
sapply(dtest,with,second)

###View summaries
lapply(allh,summary)

##Precompute the meat matrix for robust testing. Takes some time to compute!!!
PHI <- lapply(allh,function(x)meatHAC(x\$unrestricted,prewhite=TRUE,weights=weightsAndrews))

###Apply hAh test
lapply(allh,hAh_test)

##Apply robust hAh test with precomputed PHI
mapply(hAhr_test,allh,PHI,SIMPLIFY=FALSE)

##Parameter j is superfluous, j=0 means no logarithm transformation was
##applied, j=1 means that logarithm transformation was applied. The graph
##is made to be the same as in the aforementioned article.

graph <- function(x,phi,j,h) {
k <- length(coef(x, midas=TRUE, term_names = "y"))
pv0hac <- hAhr_test(x,PHI=phi)\$p.value
ttl <- sprintf("k(H=%.0f,j=%.0f) = %.0f: p-val.(hAh_HAC) < %.2f", h, j, k, max(pv0hac, 0.01))
plot_midas_coef(x, title = ttl, term_name = "y")
}

dev.new()
par(mfrow=c(2,2))

plot_info <- mapply(graph,allh,PHI,as.list(rep(1,4)),as.list(c(5,10,20,40)),SIMPLIFY=FALSE)
```

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midasr documentation built on May 29, 2017, 4:12 p.m.