default_tune_control: Create Tuning Control Parameters

Description

This function creates a list of tuning parameters used by the pmmh function. The tuning choices are inspired by Pitt et al. [2012] and Dahlin and Schön [2019].

Usage

default_tune_control(
  pilot_proposal_sd = 0.5,
  pilot_n = 100,
  pilot_m = 2000,
  pilot_target_var = 1,
  pilot_burn_in = 500,
  pilot_reps = 100,
  pilot_resample_algorithm = c("SISAR", "SISR", "SIS"),
  pilot_resample_fn = c("stratified", "systematic", "multinomial")
)

Value

A list of tuning control parameters.

Arguments

pilot_proposal_sd: Standard deviation for pilot proposals. Default is 0.5.
pilot_n: Number of pilot particles for particle filter. Default is 100.
pilot_m: Number of iterations for MCMC. Default is 2000.
pilot_target_var: The target variance for the posterior log-likelihood evaluated at estimated posterior mean. Default is 1.
pilot_burn_in: Number of burn-in iterations for MCMC. Default is 500.
pilot_reps: Number of times a particle filter is run. Default is 100.
pilot_resample_algorithm: The resample_algorithm used for the pilot particle filter. Default is "SISAR".
pilot_resample_fn: The resampling function used for the pilot particle filter. Default is "stratified".

References

M. K. Pitt, R. d. S. Silva, P. Giordani, and R. Kohn. On some properties of Markov chain Monte Carlo simulation methods based on the particle filter. Journal of Econometrics, 171(2):134–151, 2012. doi: https://doi.org/10.1016/j.jeconom.2012.06.004

J. Dahlin and T. B. Schön. Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models. Journal of Statistical Software, 88(2):1–41, 2019. doi: 10.18637/jss.v088.c02