diff --git a/R/var.R b/R/var.R
index b7c67e30..615ce57a 100644
--- a/R/var.R
+++ b/R/var.R
@@ -132,7 +132,7 @@ specials_var <- new_specials(
 #'
 #' @section Specials:
 #'
-#' \subsection{pdq}{
+#' \subsection{AR}{
 #' The `AR` special is used to specify the lag order for the auto-regression.
 #' \preformatted{
 #' AR(p = 0:5)
@@ -438,8 +438,6 @@ generate.VAR <- function(x, new_data, specials, ...){
 #' @param impulse A character string specifying the name of the variable that is shocked (the impulse variable).
 #' @param orthogonal If TRUE, orthogonalised impulse responses will be computed.
 #'
-#' @seealso [`fabletools::IRF.mdl_df`]
-#'
 #' @export
 IRF.VAR <- function(x, new_data, specials, impulse = NULL, orthogonal = FALSE, ...) {
   # Zero out end of data
diff --git a/R/vecm.R b/R/vecm.R
index 94a50601..b9264ea8 100644
--- a/R/vecm.R
+++ b/R/vecm.R
@@ -177,7 +177,7 @@ specials_vecm <- new_specials(
 #'
 #' @section Specials:
 #'
-#' \subsection{pdq}{
+#' \subsection{AR}{
 #' The `AR` special is used to specify the lag order for the auto-regression.
 #' \preformatted{
 #' AR(p = 0:5)
@@ -189,7 +189,7 @@ specials_vecm <- new_specials(
 #' }
 #'
 #' \subsection{xreg}{
-#' Exogenous regressors can be included in an VAR model without explicitly using the `xreg()` special. Common exogenous regressor specials as specified in [`common_xregs`] can also be used. These regressors are handled using [stats::model.frame()], and so interactions and other functionality behaves similarly to [stats::lm()].
+#' Exogenous regressors can be included in an VECM model without explicitly using the `xreg()` special. Common exogenous regressor specials as specified in [`common_xregs`] can also be used. These regressors are handled using [stats::model.frame()], and so interactions and other functionality behaves similarly to [stats::lm()].
 #'
 #' The inclusion of a constant in the model follows the similar rules to [`stats::lm()`], where including `1` will add a constant and `0` or `-1` will remove the constant. If left out, the inclusion of a constant will be determined by minimising `ic`.
 #'
@@ -415,8 +415,6 @@ generate.VECM <- function(x, new_data, specials, ...){
 #' @param impulse A character string specifying the name of the variable that is shocked (the impulse variable).
 #' @param orthogonal If TRUE, orthogonalised impulse responses will be computed.
 #'
-#' @seealso [`fabletools::IRF.mdl_df`]
-#'
 #' @export
 IRF.VECM <- function(x, new_data, specials, impulse = NULL, orthogonal = FALSE, ...) {
   # Zero out end of data
diff --git a/man/VECM.Rd b/man/VECM.Rd
index fc986fc8..295f6b88 100644
--- a/man/VECM.Rd
+++ b/man/VECM.Rd
@@ -4,7 +4,7 @@
 \alias{VECM}
 \title{Estimate a VECM model}
 \usage{
-VECM(formula, ic = c("aicc", "aic", "bic"), ...)
+VECM(formula, ic = c("aicc", "aic", "bic"), r = 1L, ...)
 }
 \arguments{
 \item{formula}{Model specification (see "Specials" section).}
@@ -18,7 +18,8 @@ A model specification.
 }
 \description{
 Searches through the vector of lag orders to find the best VECM model which
-has lowest AIC, AICc or BIC value. It is implemented using OLS per equation.
+has lowest AIC, AICc or BIC value. The model is estimated using the Johansen
+procedure (maximum likelihood).
 }
 \details{
 Exogenous regressors and \code{\link{common_xregs}} can be specified in the model
@@ -67,6 +68,3 @@ fit \%>\%
   forecast() \%>\%
   autoplot(lung_deaths)
 }
-\seealso{
-\href{https://otexts.com/fpp2/VAR.html}{Forecasting: Principles and Practices, Vector autoregressions (section 11.2)}
-}
diff --git a/man/fitted.VECM.Rd b/man/fitted.VECM.Rd
deleted file mode 100644
index 0b7056cb..00000000
--- a/man/fitted.VECM.Rd
+++ /dev/null
@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/vecm.R
-\name{fitted.VECM}
-\alias{fitted.VECM}
-\title{Extract fitted values from a fable model}
-\usage{
-\method{fitted}{VECM}(object, ...)
-}
-\arguments{
-\item{object}{A model for which forecasts are required.}
-
-\item{...}{Other arguments passed to methods}
-}
-\value{
-A vector of fitted values.
-}
-\description{
-Extracts the fitted values.
-}
-\examples{
-lung_deaths <- cbind(mdeaths, fdeaths) \%>\%
-  as_tsibble(pivot_longer = FALSE)
-
-lung_deaths \%>\%
-  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) \%>\%
-  fitted()
-}
diff --git a/man/forecast.VECM.Rd b/man/forecast.VECM.Rd
deleted file mode 100644
index 1ac5407a..00000000
--- a/man/forecast.VECM.Rd
+++ /dev/null
@@ -1,42 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/vecm.R
-\name{forecast.VECM}
-\alias{forecast.VECM}
-\title{Forecast a model from the fable package}
-\usage{
-\method{forecast}{VECM}(
-  object,
-  new_data = NULL,
-  specials = NULL,
-  bootstrap = FALSE,
-  times = 5000,
-  ...
-)
-}
-\arguments{
-\item{object}{A model for which forecasts are required.}
-
-\item{new_data}{A tsibble containing the time points and exogenous regressors to produce forecasts for.}
-
-\item{specials}{(passed by \code{\link[fabletools:forecast]{fabletools::forecast.mdl_df()}}).}
-
-\item{bootstrap}{If \code{TRUE}, then forecast distributions are computed using simulation with resampled errors.}
-
-\item{times}{The number of sample paths to use in estimating the forecast distribution when \code{bootstrap = TRUE}.}
-
-\item{...}{Other arguments passed to methods}
-}
-\value{
-A list of forecasts.
-}
-\description{
-Produces forecasts from a trained model.
-}
-\examples{
-lung_deaths <- cbind(mdeaths, fdeaths) \%>\%
-  as_tsibble(pivot_longer = FALSE)
-
-lung_deaths \%>\%
-  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) \%>\%
-  forecast()
-}
diff --git a/man/glance.VECM.Rd b/man/glance.VECM.Rd
index 9044cb71..b423a571 100644
--- a/man/glance.VECM.Rd
+++ b/man/glance.VECM.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/vecm.R
 \name{glance.VECM}
 \alias{glance.VECM}
-\title{Glance a VAR}
+\title{Glance a VECM}
 \usage{
 \method{glance}{VECM}(x, ...)
 }
@@ -15,17 +15,10 @@
 A one row tibble summarising the model's fit.
 }
 \description{
-Construct a single row summary of the VAR model.
+Construct a single row summary of the VECM model.
 }
 \details{
-Contains the variance of residuals (\code{sigma2}), the log-likelihood (\code{log_lik}),
-and information criterion (\code{AIC}, \code{AICc}, \code{BIC}).
-}
-\examples{
-lung_deaths <- cbind(mdeaths, fdeaths) \%>\%
-  as_tsibble(pivot_longer = FALSE)
-
-lung_deaths \%>\%
-  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) \%>\%
-  glance()
+Contains the variance of residuals (\code{sigma2}), the log-likelihood
+(\code{log_lik}), the cointegrating vector (\code{beta}) and information criterion
+(\code{AIC}, \code{AICc}, \code{BIC}).
 }
diff --git a/man/residuals.VECM.Rd b/man/residuals.VECM.Rd
deleted file mode 100644
index 0b4e0e98..00000000
--- a/man/residuals.VECM.Rd
+++ /dev/null
@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/vecm.R
-\name{residuals.VECM}
-\alias{residuals.VECM}
-\title{Extract residuals from a fable model}
-\usage{
-\method{residuals}{VECM}(object, ...)
-}
-\arguments{
-\item{object}{A model for which forecasts are required.}
-
-\item{...}{Other arguments passed to methods}
-}
-\value{
-A vector of fitted residuals.
-}
-\description{
-Extracts the residuals.
-}
-\examples{
-lung_deaths <- cbind(mdeaths, fdeaths) \%>\%
-  as_tsibble(pivot_longer = FALSE)
-
-lung_deaths \%>\%
-  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) \%>\%
-  residuals()
-}
diff --git a/man/tidy.VECM.Rd b/man/tidy.VECM.Rd
deleted file mode 100644
index 77363e54..00000000
--- a/man/tidy.VECM.Rd
+++ /dev/null
@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/vecm.R
-\name{tidy.VECM}
-\alias{tidy.VECM}
-\title{Tidy a fable model}
-\usage{
-\method{tidy}{VECM}(x, ...)
-}
-\arguments{
-\item{x}{An object to be converted into a tidy \code{\link[tibble:tibble]{tibble::tibble()}}.}
-
-\item{...}{Additional arguments to tidying method.}
-}
-\value{
-The model's coefficients in a \code{tibble}.
-}
-\description{
-Returns the coefficients from the model in a \code{tibble} format.
-}
-\examples{
-lung_deaths <- cbind(mdeaths, fdeaths) \%>\%
-  as_tsibble(pivot_longer = FALSE)
-
-lung_deaths \%>\%
-  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) \%>\%
-  tidy()
-}