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function afunc A reset beta_q time
	type vec[rec] 	A
	type series	  	reset
	type vector		beta_q
	type integer  	time
	type rect	  	afunc
	local integer k i j
	local rec temp
	*
	dim afunc(%rows(A)*%rows(A(1))+2,%rows(A)*%rows(A(1))+2)
	comp afunc = %const(0.0)
	do k=1,%rows(A)
		do i=1,%rows(A(1))
			do j=1,%cols(A(1))
				comp afunc(i,(k-1)*%rows(A(1))+j) = A(k)(i,j)
			end do j
		end do i
		if k>1 {
			do i=1,%rows(A(1))
				do j=1,%cols(A(1))
					comp afunc((k-1)*%rows(A(1))+i,(k-2)*%rows(A(1))+j) = i==j
				end do j
			end do i
		}
		do i=1,%cols(A(1))
			comp temp = tr(beta_q)*%xsubmat(A(k),1,%rows(A(k)),i,i)
			comp afunc(%rows(afunc)-1,(k-1)*%rows(A(1))+i) = temp(1,1)
			comp afunc(%rows(afunc),(k-1)*%rows(A(1))+i) = temp(1,1)
		end do i
	end do k
	comp afunc(%rows(afunc),%cols(afunc)) = reset(time)
end function afunc
*
function yfunc monthly quarterly time
	type vec[int]	monthly
	type series		quarterly
	type integer	time
	type vec		yfunc
    local integer	k
	*
	dim yfunc(%rows(monthly)+1)
	do k=1,%rows(monthly)
		comp yfunc(k) = ([series] monthly(k))(time)
	end do k
	comp yfunc(%rows(yfunc)) = quarterly(time)
end function yfunc
*
function cfunc Lambda p
	type rec 		Lambda
	type integer	p
	type rec 		cfunc
	local integer	i j
	*
	dim cfunc(%rows(Lambda)+1,%cols(Lambda)*p+2)
	comp cfunc = %const(0.0)
	do i=1,%rows(Lambda)
		do j=1,%cols(Lambda)
			comp cfunc(i,j) = Lambda(i,j)
		end do j
	end do i
	comp cfunc(%rows(cfunc),%cols(cfunc)) = 1
	comp cfunc = tr(cfunc)
end function cfunc
*
function zfunc mu_q r p
	type real 	mu_q
	type int	r
	type int	p
	type vect	zfunc
	*
	comp zfunc = %zeros(r*p+2,1)
	comp zfunc(%rows(zfunc)-1) = (1./3)*mu_q
	comp zfunc(%rows(zfunc)) = (1./3)*mu_q
end function zfunc
*
function ffunc B p
	type rect	B
	type int	p
	type rec	ffunc
	*
	comp ffunc = B~~%zeros(%rows(B)*(p-1)+2,%cols(B))
end function ffunc


PROCEDURE NOWCAST QUARTERLY START END
	*
	type series	quarterly
	type int		start end
	*
	option choice	method	1 factor
	option switch	print 	0
	option switch	intres	0
	option switch	graph	0
	option integer	fsteps 	0
	option integer	ftime
	option integer	p		1; * lag order for factor VAR
	option integer	r		3; * number of factors
	option integer	q		2; * number of shocks
	*
	local integer	nind lstart lend i time
	local vec[int]	monthly
	local real		mu_q Sigma_q
	local vec[rea]	mu beta_q
	local rec[rea]	z x D V F Lambda Psi Sigma B
	local vec[rec]	A
	local vec[ser]	F_q F_m
	local series	reset
	local vec[str]	F_lab
	local ser[vec]	st_vec
	local ser[sym]	st_var
	*
	if method==1 {
      *
      * Here follows some other code...
		*
		*----------------------------------------------------------------------------------------------
		* Implementation of Kalman filter
		if intres; disp "ESTIMATES OF THE VARIOUS MATRICES SUCCESSFULLY ESTIMATED! GOING ON WITH KALMAN FILTER..."
		*
		* The state vector, kf_X_t, includes the following [f_t f_t-1 ... f_t-p+1 y_hat y_hat_q]'
		* The vector of observables, kf_Y_t, includes the following [z1_t z2_t ... zn_t y_q]'
		* The matrix that relates the state vector to the observables, kf_c, is constant and includes the factor loadings
		* The transition matrix for the state vector, kf_A_t, includes the coefficients from the factor VAR, beta_q, and an indicator to "re-set" the quarterly series (y_hat_q)
		* The shift vector for the state, kf_z, includes the unconditional mean of y_q

		set reset lstart lend = %mod(t,3)<>1

		*disp yfunc(monthly,quarterly,lend)
		disp afunc(A,reset,beta_q,lend) 
		*disp cfunc(Lambda,p) 
		*disp zfunc(mu_q,r,p) 
		*disp ffunc(B,p) 
                * Checking dimensions of the matrices
		if intres; disp "kf_Y:" @10 %rows(yfunc(monthly,quarterly,lend)) "by" %cols(yfunc(monthly,quarterly,lend))
		if intres; disp "kf_A:" @10 %rows(afunc(A,reset,beta_q,lend)) "by" %cols(afunc(A,reset,beta_q,lend))
		if intres; disp "kf_c:" @10 %rows(cfunc(Lambda,p)) "by" %cols(cfunc(Lambda,p))
		if intres; disp "kf_Z:" @10 %rows(zfunc(mu_q,r,p)) "by" %cols(zfunc(mu_q,r,p))
		if intres; disp "kf_F:" @10 %rows(ffunc(B,p)) "by" %cols(ffunc(B,p))


		* Estimation of states (factors and monthly GDP) by Kalman filter
		dlm(var=known,presample=ergodic,method=bfgs,a=afunc(A,reset,beta_q,t),y=yfunc(monthly,quarterly,t),c=cfunc(Lambda,p),z=zfunc(mu_q,r,p),f=ffunc(B,p),	$
			sw=%identity(q),sv=Sigma~\%zeros(1,1)~\Sigma_q,type=filter) lstart lend st_vec st_var
	}
	*
end procedure 

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## SX22. Expected Type SERIES, Got REAL Instead
>>>>gs,a=afunc(A,reset,<<<<