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Declare priors for Bayesian estimation

Source: R/priors.R
priors.Rd

One named argument per estimated quantity: a structural parameter name or a shock name (meaning that shock's standard deviation). Everything without a prior stays calibrated.

Usage

priors(...)

Arguments

...

Named prior declarations, e.g. b1 = beta(0.7, 0.1), c2 = truncate(normal(1.5, 0.25), lower = 1).

Value

An object of class qpm_priors.

Details

Available distributions (usable only inside priors(), so base R's beta() and gamma() functions are never masked):

  • normal(mean, sd)

  • beta(mean, sd) — on (0, 1), mean/sd parametrization

  • gamma(mean, sd) — on (0, Inf)

  • invgamma(mean, sd) — on (0, Inf); the usual choice for shock sds

  • uniform(min, max)

  • truncate(d, lower, upper) — restrict any of the above; the normalizing constant is dropped (harmless for modes and MCMC)

Examples

p <- priors(
  b1 = beta(0.7, 0.1),
  b2 = gamma(0.25, 0.1),
  c2 = truncate(normal(1.5, 0.25), lower = 1),
  eps_pi = invgamma(1, 0.3)
)
p
#> <qpm_priors> 4 priors
#>   b1         beta(mean  0.7, sd  0.1) on (0,    1)
#>   b2         gamma(mean 0.25, sd  0.1) on (0,  Inf)
#>   c2         trunc-normal(mean  1.5, sd 0.25) on (   1,  Inf)
#>   eps_pi     invgamma(mean    1, sd  0.3) on (0,  Inf)