qpm_identify: Identification diagnostics (Iskrev-style Jacobian analysis)

In qpmR: Quarterly Projection Models for Monetary Policy Analysis

Identification diagnostics (Iskrev-style Jacobian analysis)

Description

Checks, before any estimation is run, whether the chosen parameters can be told apart by the data. Two Jacobians are analysed numerically at the current calibration, in the spirit of Iskrev (2010):

Usage

qpm_identify(model, params = NULL, observables = NULL, lags = 3, h = 1e-05)

Arguments

model	A qpm_model.
params	Parameters to check: a character vector of structural parameter and/or shock names, or a priors() object (its names are used). Default: all structural parameters.
observables	Observed variables the moment analysis conditions on. Default: all declared variables.
lags	Autocovariance lags in the moment vector.
h	Relative step for the central differences.

Details

• solution level: derivatives of the solved transition, shock loading, and observable steady state with respect to the parameters. Rank deficiency here means some parameter movements do not change the model's solution at all.

• moment level (stationary models only): derivatives of the observables' first and second moments (means, and autocovariances up to lags). Rank deficiency here means some parameter movements are observationally equivalent in population.

The report names parameters with (numerically) no effect, parameter combinations spanning any null space, and near-collinear pairs of Jacobian columns (correlation above 0.995) that are only jointly identified.

Value

An object of class qpm_identification with the ranks, singular values, and flagged parameters; printed as a verdict list.

References

Iskrev, N. (2010). Local identification in DSGE models. Journal of Monetary Economics, 57(2), 189-202.

Examples

qpm_identify(qpm_template("bkl"),
             params = c("b1", "b2", "b3", "c1", "c2"),
             observables = c("pi", "i", "q", "dy_obs"))

qpmR documentation built on Sept. 29, 2026, 5:10 p.m.