Fix typo in manual
(cherry picked from commit 52935c82dc8544d2c5f661e0c5d6ac896ebe62ac)
Add reordering of second order derivatives to stochastic_solvers.m

It was erroneously completely moved to dyn_second_order_solver.m in the 4.3 branch when cleaning up dr1.m. Without this reordering, decision rules for exogenous deterministic variables at second order are wrong
(cherry picked from commit 6bc9b8840198ff830478525d2cd73d0fc65724ee)
Add unit test for correctness of forecasting with exogenous variables
(cherry picked from commit 8204be67d03515bcf6a055e63313f7083a410f2a)
Fix computation of objective function in OSR if covariances are specified

Due to the preprocessor adding entries for all variable combination into i_var, variables contained in covariances obtained a wrong weighting. By only selecting the unique entries, this cannot happen anymore. Moreover, the computation intensity of the objective does not increase quadratically in the objective anymore.
(cherry picked from commit ecbe428bd77d3d641789af11d5fb518ea5eb7f19)
Add unit test to check whether objective function matches and whether covariances actually work
(cherry picked from commit 4aaebfbe4669303dc3be039c54fdd8cc1f53f874)

Conflicts:
	tests/Makefile.am
Transform objective function in osr to full matrix

If the objective function only has one element, the scalar product of a sparse matrix and a full matrix is sparse (in contrast to two or more elements). This crashes the osr code subsequently.
(cherry picked from commit 7b1663281bc101b86d0c48bc1563208693ab08fe)
Fix bug in osr where moments were not based on optimal parameter draw

M_.params is global and was set by csminwel1 to the last value tested; this does not necessarily coincide with the optimal parameter vector, which may have occurred earlier. This commit explicitly sets the optimal parameter vector.
(cherry picked from commit 669016167e66992e676b7a06a2a88b7b34b67248)

Conflicts:
	matlab/osr1.m
Computation of the loss function in the presence of unit root variables was incorrect

Some elements were simply left out. This commit now mimics the computations as in th_autocovariances.m and makes sure the loss function coincides with the one that is obtained by reading out the covariance from oo_.var
(cherry picked from commit cb5d3df6a089de3a033889d2184a2f117abb21a7)
Add unit test for correct computation of loss function in the presence of non-stationary variables
(cherry picked from commit e94056a765ce8459dfab916909455e35ab9826bd)