Description Usage Arguments Value Author(s) References See Also Examples
This routine obtains a confidence interval for the value a^T * β, by asymptotic distribution and bootstrap, from a sample (Y_i, X_{i1},...,X_{ip}): i=1,...,n, where:
a = (a_1,...,a_p)^T
is an unknown vector,
β = (β_1,...,β_p)^T
is an unknown vector parameter and
Y_i = X_{i1}*β_1+ ... + X_{ip}*β_p + ε_i.
The random errors, ε_i, are allowed to be time series.
1 2 3 
data 

seed 
the considered seed. 
CI 
method to obtain the confidence interval. It allows us to choose between: “AD” (asymptotic distribution), “B” (bootstrap) or “all” (both). The default is “AD”. 
B 
number of bootstrap replications. The default is 1000. 
N 
Truncation parameter used in the finite approximation of the MA(infinite) expression of ε. 
a 
Vector which, multiplied by 
p.arima 
the considered p to fit the model ARMA(p,q). 
q.arima 
the considered q to fit the model ARMA(p,q). 
p.max 
if 
q.max 
if 
alpha 
1  
alpha2 
significance level used to check (if needed) the ARMA model fitted to the residuals. The default is 0.05. 
num.lb 
if 
ic 
if 
Var.Cov.eps 

A list containing:
Bootstrap 
a dataframe containing 
AD 
a dataframe containing 
pv.Box.test 
pvalues of the LjungBox test for the model fitted to the residuals. 
pv.t.test 
pvalues of the t.test for the model fitted to the residuals. 
German Aneiros Perez [email protected]
Ana Lopez Cheda [email protected]
Liang, H., Hardle, W., Sommerfeld, V. (2000) Bootstrap approximation in a partially linear regression model. Journal of Statistical Planning and Inference 91, 413426.
You, J., Zhou, X. (2005) Bootstrap of a semiparametric partially linear model with autoregressive errors. Statistica Sinica 15, 117133.
A related function is plrm.ci
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  # EXAMPLE 1: REAL DATA
data(barnacles1)
data < as.matrix(barnacles1)
data < diff(data, 12)
data < cbind(data[,1],1,data[,1])
## Not run: par.ci(data, a=c(1,0,0), CI="all")
## Not run: par.ci(data, a=c(0,1,0), CI="all")
## Not run: par.ci(data, a=c(0,0,1), CI="all")
# EXAMPLE 2: SIMULATED DATA
## Example 2a: dependent data
set.seed(123)
# We generate the data
n < 100
beta < c(0.5, 2)
x < matrix(rnorm(200,0,3), nrow=n)
sum < x%*%beta
sum < as.matrix(sum)
eps < arima.sim(list(order = c(1,0,0), ar=0.7), sd = 0.1, n = n)
eps < as.matrix(eps)
y < sum + eps
data_parci < cbind(y,x)
# We estimate the confidence interval of a^T * beta in the PLR model
## Not run: par.ci(data, a=c(1,0), CI="all")
## Not run: par.ci(data, a=c(0,1), CI="all")

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