diff --git a/doc/dynare.texi b/doc/dynare.texi
index 02622a0772c922f0800776b12cbfcf56e2ea2454..d24b78fff89fe703c478d4f8d675f8ece7bcb848 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -9846,7 +9846,7 @@ ans is a dseries object:
 
 @deftypefn{dseries} {@var{C} =} minus (@var{A}, @var{B})
 
-Overloads the @code{minus} (@code{-}) operator for @dseries objects, element by element substraction. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)-B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{minus} operator will substract the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{minus} will substract @var{B} to all the observations/variables in @var{A}.
+Overloads the @code{minus} (@code{-}) operator for @dseries objects, element by element substraction. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)-B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{minus} operator will substract the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{minus} will substract @var{B} to all the observations/variables in @var{A}. If @var{B} is a row vector of length @math{N_A}, then the @code{minus} method will substract @code{B(i)} to all the observations of variable @code{i}, for @math{i=1,...,N_A}. If @var{B} is a column vector of length @math{T_A}, then the @code{minus} method will substract @code{B} to all the variables.
 
 @examplehead
 @example
@@ -9925,7 +9925,7 @@ ts2 is a dseries object:
 
 @deftypefn{dseries} {@var{C} =} mrdivide (@var{A}, @var{B})
 
-Overloads the @code{mrdivide} (@code{/}) operator for @dseries objects, element by element division (like the @code{./} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mrdivide} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mrdivides} operator will divide each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mrdivide} will divide all the observations/variables in @var{A} by @var{B}.
+Overloads the @code{mrdivide} (@code{/}) operator for @dseries objects, element by element division (like the @code{./} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mrdivide} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mrdivides} operator will divide each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mrdivide} will divide all the observations/variables in @var{A} by @var{B}. If @var{B} is a row vector of length @math{N_A}, then the @code{mrdivide} method will divide all the observations of variable @code{i} by @code{B(i)}, for @math{i=1,...,N_A}. If @var{B} is a column vector of length @math{T_A}, then the @code{mrdivide} method will perform a division of all the variables by @code{B}, element by element. 
 
 @examplehead
 @example
@@ -9955,7 +9955,7 @@ ans is a dseries object:
 
 @deftypefn{dseries} {@var{C} =} mtimes (@var{A}, @var{B})
 
-Overloads the @code{mtimes} (@code{*}) operator for @dseries objects, Hadammard product (the @code{.*} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mtimes} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)*B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mtimes} operator will multiply each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mtimes} will multiply all the observations/variables in @var{A} by @var{B}.
+Overloads the @code{mtimes} (@code{*}) operator for @dseries objects, Hadammard product (the @code{.*} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mtimes} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)*B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mtimes} operator will multiply each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mtimes} will multiply all the observations/variables in @var{A} by @var{B}. If @var{B} is a row vector of length @math{N_A}, then the @code{mtimes} method will multiply all the observations of variable @code{i} by @code{B(i)}, for @math{i=1,...,N_A}. If @var{B} is a column vector of length @math{T_A}, then the @code{mtimes} method will perform a multiplication of all the variables by @code{B}, element by element.
 
 @end deftypefn
 
@@ -10036,7 +10036,7 @@ Overloads Matlab/Octave's @code{plot} function for @dseries objects. Returns a M
 
 @deftypefn{dseries} {@var{C} =} plus (@var{A}, @var{B})
 
-Overloads the @code{plus} (@code{+}) operator for @dseries objects, element by element addition. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)+B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{plus} operator will add the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{plus} will add @var{B} to all the observations/variables in @var{A}.
+Overloads the @code{plus} (@code{+}) operator for @dseries objects, element by element addition. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{plus} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)+B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{plus} operator will add the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{plus} will add @var{B} to all the observations/variables in @var{A}. If @var{B} is a row vector of length @math{N_A}, then the @code{plus} method will add @code{B(i)} to all the observations of variable @code{i}, for @math{i=1,...,N_A}. If @var{B} is a column vector of length @math{T_A}, then the @code{plus} method will add @code{B} to all the variables. 
 
 @end deftypefn