# `wald` The function `wald` does a Wald test. You supply the constraints. Implementation follows Cameron and Trivedi (2005, page 136). The constraints take the form $R \widehat{\theta} - q = 0$. ## Signature `wald <- function(R, q, theta, V)` ## Arguments `R` and `q` describe the constraints. `theta` is the estimated coefficient vector of the unrestricted model. `V` is the variance-covariance matrix of the unrestricted model. You can either send `vcov(fit)` where `fit` is the output of a call to `lm` or a similar regression function, or send your own variance-covariance matrix, which would be the case if you correct for heteroskedasticity or serial correlation. Normally you would use `lmtest::waldtest` if you have a single regression. This function would help if you don't want to write out a formula for your regression. ## Test Case This is the code I used to test it: ``` y <- rnorm(100) x <- matrix(rnorm(600), ncol=6) x1 <- x[,1:2] x2 <- x[,3:6] fit1 <- lm(y~x2) fit2 <- lm(y~x1+x2) lmtest::waldtest(fit2, fit1, test="Chisq") R <- matrix(0.0, ncol=7, nrow=2) R[1,2] <- 1 R[2,3] <- 1 q <- c(0.0, 0.0) tstools::wald(R, q, fit2$coef, vcov(fit2)) ``` They return the same result. There may be edge cases that have not been tested.
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