- New perfect foresight simulation with expectation errors. In such a scenario, agents make expectation errors, in the sense that the path they had anticipated in period 1 does not realize exactly. More precisely, in some simulation periods, they may receive new information that makes them revise their anticipation for the path of future shocks. Also, under this scenario, it is assumed that agents behave as under perfect foresight, *i.e.* they take their decisions as if there was no uncertainty and they knew exactly the path of future shocks; the new information that they may receive comes as a total surprise to them. Implemented by new `perfect_foresight_with_expectation_errors_setup` and `perfect_foresight_with_expectation_errors_solver` commands, and `shocks(learnt_in=…)` and `endval(learnt_in=…)` blocks.
- New perfect foresight simulation with expectation errors. In such a scenario, agents make expectation errors, in the sense that the path they had anticipated in period 1 does not realize exactly. More precisely, in some simulation periods, they may receive new information that makes them revise their anticipation for the path of future shocks. Also, under this scenario, it is assumed that agents behave as under perfect foresight, *i.e.* they take their decisions as if there was no uncertainty and they knew exactly the path of future shocks; the new information that they may receive comes as a total surprise to them. Implemented by new `perfect_foresight_with_expectation_errors_setup` and `perfect_foresight_with_expectation_errors_solver` commands, and `shocks(learnt_in=…)` and `endval(learnt_in=…)` blocks.
- Pruning à la Andreasen et al. (2018) is now available at an arbitrary approximation order when performing stochastic simulations with `stoch_simul` (!2147)
- New `conditional_likelihood` option to the `estimation` command. When the option is set, instead of using the Kalman filter to evaluate the likelihood, Dynare will evaluate the conditional likelihood based on the first order reduced form of the model, by assuming that the initial state vector is 0 for all the endogenous variables. (b7693c32, f7694208)
- New `conditional_likelihood` option to the `estimation` command. When the option is set, instead of using the Kalman filter to evaluate the likelihood, Dynare will evaluate the conditional likelihood based on the first order reduced form of the model, by assuming that the initial state vector is 0 for all the endogenous variables. (b7693c32, f7694208)
- Steady state computation now allows accounting for `MCP`-tags (https://git.dynare.org/Dynare/dynare/-/merge_requests/1877)
- Steady state computation now allows accounting for `MCP`-tags (https://git.dynare.org/Dynare/dynare/-/merge_requests/1877)
