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 ganeiros@udc.es
Ana Lopez Cheda ana.lopez.cheda@udc.es
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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