In msperlin/afedR: Data and Functions for Book "Analyzing Financial and Economical Data with R"

set.seed(5)

# number of obs
n_row <- 100

# set x as Normal (0, 1)
x <- rnorm(n_row)

# set coefficients
my_alpha <- 1.5
my_beta <- 0.5

# build y
y <- my_alpha + my_beta*x + rnorm(n_row)

library(tidyverse)

my_lm <- lm(formula = y ~ x, data = tibble(x, y))

summary(my_lm)

library(car)

# set test matrix
test_matrix <- matrix(c(my_alpha,  # alpha test value
                        my_beta))  # beta test value

# hypothesis matrix 
hyp_mat <- matrix(c(1.5, 0,
                    0  , 0.5),
                  nrow = 2)

# do test
my_waldtest <- linearHypothesis(my_lm, 
                                hypothesis.matrix = hyp_mat, 
                                rhs = test_matrix)

# print result
my_sol <- my_waldtest$F[2]

# none
my_answers <- make_random_answers(my_sol)

Question

Using the car package, test the joint hypothesis that the value of alpha is equal to 1.5 and the value of beta is equal to 0.5. What is the value of the resulting F test?

exams::answerlist(my_answers, markup = "markdown")

Solution

Meta-information

extype: schoice exsolution: r mchoice2string(c(TRUE, FALSE, FALSE, FALSE, FALSE), single = TRUE) exname: "function 01" exshuffle: TRUE

msperlin/afedR documentation built on Sept. 11, 2022, 9:49 a.m.