set_priors: Set the priors for estimation

In opendoor-labs/MarkovSwitchingDCF: Markov Switching Dynamic Common Factor Model using the Kim filter (Hamilton + Kalman filters)

Description

Set the priors for estimation

Usage

set_priors(yy_s, prior, panelID, timeID, n_states = 2, ms_var = F,
  detect.formula = F, formulas = c("y ~ c + e.l1 + e.l2"))

Arguments

yy_s	Multivariate time series of standardized data values from data_trans
prior	"estimate", "uninformative" or vector of named prior parameter guesses: DCF AR coefficients: phi1 and phi2 only; Error MA coefficients: psi_i1 to psi_i2 only for each series i; Error standard deviation: sigma_i only for each series i; Observation coefficient on DCF with first gamma (gamma_i, ..., gamma_n) only 1 index number, not i0 and any more gammas per equation: gamma_i1 to gamma_ik; Markov switching growth rate: mu_d and mu_u; Transition probabilities: p_dd, p_md (or p_mu), p_mm, p_md (or p_mu), p_uu, p_ud (or p_um)
panelID	Column name that identifies the cross section of the data
timeID	Column name that identifies the date
n_states	Number of states to include in the Markov switching model
ms_var,	Logical, T for Markow switching variance, default is F
detect.formula	Logical, detect lag length of the dynamic common factor to include in each observation equation using the cross correlation function up to a max of 3
formulas	R formula describing the relationship between each data series, the unobserved dynamic common factor, and the erorr structure

Value

vector of initial coefficient values

Author(s)

Alex Hubbard ([e-mail])

Examples

set_priors(yy_s = yy_s, prior = prior, panelID = "panel", timeID = "date")

opendoor-labs/MarkovSwitchingDCF documentation built on Jan. 8, 2020, 12:24 p.m.