NewFeatures.md

@@ -140,10 +140,10 @@ steady state or the dynamic residuals when the nonlinear solver is used. (#349)
 
  - Implementation of the Sequential Monte-Carlo sampler (new value `hssmc` for option `posterior_sampling_method`) as described by Herbst and Schorfheide (JAE, 2014).
 
-- Implementation of IRF matching with stochastic simulations. Dynare supports both Frequentist (as in Christiano, Eichenbaum, and Evans (2005, JPE)) as well as Bayesian IRF matching approaches (as in Christiano, Trabandt, and Walentin (2010, Handbook)). The core idea of IRF matching is to treat empirical impulse responses (e.g. given from a SVAR or local projection estimation outside) as data
-and select model parameters that align the model's IRFs closely with their empirical counterparts.
-New option `mom_method=IRF_MATCHING` to `method_of_moments` command and new blocks `matched_irfs` and matched_irfs_weights` to specify the values and weights of the empirical impulse response functions.
-(preprocessor!85, preprocessor#86, preprocessor#124, !2191)
+- IRF matching with stochastic simulations:
+   - Dynare supports both Frequentist (as in Christiano, Eichenbaum, and Evans (2005, JPE)) as well as Bayesian IRF matching approaches (as in Christiano, Trabandt, and Walentin (2010, Handbook)). The core idea of IRF matching is to treat empirical impulse responses (e.g. given from a SVAR or local projection estimation outside) as data and select model parameters that align the model's IRFs closely with their empirical counterparts. (!2191)
+   - New options to `method_of_moments` command. (preprocessor!85)
+   - New blocks `matched_irfs` and matched_irfs_weights` to specify the values and weights of the empirical impulse response functions. (preprocessor#124)
 
 Dynare 5
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