Concerning the first question on filter_algorithm=sis and resampling=systematic the answer is yes. And it is the default.
resampling_threshold only works with generic and is useless with systematic or none. With apf it induces a second resampling step on current particles which is not necessary. The literature then recommands resampling=none for apf.
Keep in mind that maximum likelihood will not function in presence of resampling since it generates a discontinuity of the criterion with respect to the structural parameters. In that case, avoid gradient-based methods.
Concerning mode_compute=11 it is an auxiliary particle filter and it overrides the other options that control other particle filters. It is useful to obtain fast estimation on parameters by including them as extra states but suffers from a severe lack of theoretical foundations.
I learn that dynare 4.6.1 is able to do estimation for order higher than 1. While I try to implement the estimation which I have specified as log-linear model. However, the dynare reports that higher order estimation cannot deal with log-lineared model.
error: non_linear_dsge_likelihood (line 59) non_linear_dsge_likelihood: It is not possible to use a non linear filter with the option loglinear!
I understand that the higher-order estimation or particle filter is relatively new. But is it possible to find any simple example or instruction in the dynare manual on the issue—how to use the estimation command with higher-order properly?
Note that the problem is not with using a log-linearized model, but rather you asking to log-linearize a model using Dynare functionality. It is usually better to work with auxiliary equations to get a log-linearization. See
Thank You for Your reply, I installed dynare 4.8 the latest unstable version. And now after running the same code I get such an error.
Error using initial_estimation_checks (line 99)
initial_estimation_checks:: particle filtering requires measurement error on the observables
Error in initial_estimation_checks (line 99)
error(‘initial_estimation_checks:: %s requires measurement error on the observables’,disp_string)
Error in dynare_estimation_1 (line 154)
Error in dynare_estimation (line 118)
Error in noisy_ar1.driver (line 179)
Error in dynare (line 281)