qpm_singular_F. Set options(qpmR.use_cpp = FALSE) to force
the R path; qpm_use_cpp() reports which is in use.N^2 x N^2 system — O(N^3) per iteration instead of
O(N^6). This turned out to matter more than the filter: it is 39x
faster on the Czech model and also speeds up model_properties(),
qpm_rule_eval() and qpm_identify(), which all solve the same
equation.A, B, C or D that each coefficient lands in — is now
computed once per model and cached on it. Estimation re-solves the
same equations thousands of times with different numbers, so only the
values are recomputed: build_first_order() went from 27.5 ms to
1.7 ms (16x) and qpm_solve() from 37 ms to 8.3 ms. The cache is
rebuilt automatically if it is missing (a model serialised by an older
version) or no longer matches its equations. Linearity is checked once
at construction rather than on every solve, since it is a property of
the equations.eigen_table() is assembled directly from
the QZ output rather than through data.frame(), whose constructor,
reorder and row-name reset were about a fifth of a posterior draw on
small models even though the estimation objective never reads the
table. The table itself is unchanged.qpm_risk() / risk_log(): express a balance of risks. Bands become
two-piece normal, so the mode stays on the model's projection while
the mean shifts by the stated skew and total variance is held fixed
— a skew redistributes risk rather than adding it. Fan charts centred
on the mode, as published fan charts are. Unlike add_judgment(),
which moves the projection and back-solves the supporting shocks, this
changes only the shape of the distribution around an unchanged path.qpm_rule_eval(): score alternative policy rules over a grid by the
unconditional loss, computed exactly from the stationary covariance
rather than by simulation, and trace the inflation-output variability
frontier. Rules that fail Blanchard-Kahn are reported as
indeterminate or explosive rather than silently dropped.qpm_counterfactual(): replay history with shocks switched off or
scaled — "what if the central bank had simply followed its rule?".
Replaying the unmodified shocks reproduces the smoothed history to
machine precision, which the function checks.qpm_compare_models(): compare model behaviour (impulse responses
and implied moments), complementing qpm_diff(), which compares
structure.qpm_disaggregate(): temporal disaggregation of annual data to
quarterly by Denton-Cholette or Chow-Lin, both satisfying the
aggregation constraint exactly — the first step for the many economies
that publish national accounts only annually.fevd(): forecast error variance decomposition — how much of each
variable's forecast uncertainty each structural shock accounts for, at
every horizon. Shares sum to one by construction (verified to 1e-10 in
the tests), exogenous processes come back as entirely own-driven, and
the decomposition is well defined for unit-root models even though the
variances themselves are not.model_properties(): the standard calibration check — model-implied
standard deviations and autocorrelations from the stationary
covariance, the shock dominating each variable's unconditional
variance, and the same statistics computed from data alongside, with a
warning when model and data volatility differ by more than a factor of
two.logLik() and nobs() for filtrations and estimates
(which makes AIC() and BIC() work), residuals() (one-step
innovations, standardised innovations, or smoothed structural shocks)
and fitted() for filtrations, vcov() and confint() for
estimates, and summary() methods returning data frames for both.write_dynare(): export any model as a Dynare .mod file. The
original equations are exported rather than qpmR's internal
first-order system, so Dynare builds its own auxiliary variables for
long leads and lags and the two implementations agree only if both
are right. The test suite now pins qpmR's impulse responses to golden
files generated by Dynare 6.0: across 4080 points (12 shocks x 17
variables x 20 quarters) the largest discrepancy is 1.4e-14, and
steady states agree to 8e-14. The food-block model agrees to 1.7e-14,
which independently validates add_block(). Regenerate the golden
files with data-raw/dynare_golden.R.-Inf as the largest stable root.qpm_condition(), add_judgment(), scenarios) are
computed from the conditioned rows and the diagonal of the stacked-path
covariance rather than from the full (H N) x (H N) matrix, which was the
package's one large matrix product and imposed a size cap above which
bands fell back to the unconditional ones. The cap is gone; band values
agree with the previous formula to 1e-9.write_dynare() no longer writes a file by default: file = NULL (the
new default) returns the Dynare source as a character vector, and a
file is written only when a path is given. qpm_report() and
save_round() likewise require an output path and a store directory
rather than defaulting to the working directory.qpm_estimate() reports progress with message() rather than cat(),
so suppressMessages() silences it.seed given to simulate() or qpm_estimate() no longer disturbs
the caller's random number stream: the previous RNG state is restored
on exit, as stats::simulate() does for linear models.\donttest{} examples were resized to run in a few
seconds each.First stable release. The full forecasting-and-policy-analysis workflow now runs end to end — data, filtering, gaps, model, baseline, judgment, policy, scenarios, rounds, revisions, verification, report — and is exercised on a real quarterly dataset for Czechia shipped with the package.
This release adds the reporting and audit layer and country-adaptation blocks on top of 0.4.
qpm_report(): turns a round into the document a policy meeting is
run from — executive summary with the numbers filled in, forecast
table and fan charts, filtered gaps, shock decomposition, the
judgment ledger with its implied shocks, an optional revision
decomposition against the previous round, and a reproducibility
appendix. The .Rmd source is always written (institutions replace
the template's text, not its plumbing) and rendered to HTML/PDF/Word
when pandoc or Quarto is available; where neither is — air-gapped
forecasting machines, bare CI runners — it says so and returns the
source rather than failing.chart_pack(): the standard round chart set (forecast fans,
filtered latent states, shock decomposition, monetary transmission)
as a multi-page PDF or numbered PNGs.verify_round(): re-runs an archived round from its own contents and
checks that the published numbers come back, reporting the largest
deviation, the worst variables, and any qpmR version drift. When the
round is loaded from a store it also checks the human-readable CSV
sidecars against the object, so a hand-edited audit trail is
detected.qpm_block() / add_block(): reusable bundles of variables, shocks,
parameters and equations that adapt a template to a country without
forking it. Equations whose left-hand side names an existing variable
replace that variable's equation; equations for newly declared
variables are appended. Blocks compose, and each one is recorded in
the model's meta$blocks.block_food_cpi(): headline CPI split into food and core. The
Phillips curve moves to core, food gets its own persistence, stronger
exchange-rate pass-through, and error correction on the relative food
price, and headline becomes the weighted identity. Food is 30-50
percent of the basket across most of sub-Saharan Africa and South
Asia, where a single-inflation model is unusable; a food supply shock
in this block raises headline while leaving core essentially
untouched, which is the relative-price story policy should look
through.block_fx_intervention(): a leaning-against-the-wind intervention
rule entering the UIP block, so one model spans a continuum of
exchange-rate regimes — intensity = 0 reproduces the free float
exactly, moderate values a managed float, large values approach a peg
(the peak exchange-rate response to a risk-premium shock falls from
0.97 to 0.53 to 0.08 across those settings).qpm_template() gains the "bkl_food" and "managed_fx" shortcuts.qpm_diff(): structural comparison of two models — variables,
shocks, parameters and equations added, removed or changed, plus
recalibrations — so a country team's customization is reviewable as a
diff rather than a fork.The estimation layer is complete.
qpm_identify(): Iskrev-style local identification diagnostics
before any sampling — numerical Jacobians of the solved model
(solution level) and of the observables' population moments (moment
level, stationary models) with respect to the chosen parameters.
Reports parameters with no effect, rank-deficient combinations, and
near-collinear pairs that are only jointly identified. Unit-root
models get the solution-level check with an explanatory note.marginal_likelihood(): log marginal likelihood by the modified
harmonic mean (Geweke 1999) across truncation probabilities with a
stability spread, plus a Laplace approximation at the mode in
transformed space as a cross-check. Differences across models on the
same data are log Bayes factors. truncate() priors are now
renormalized numerically at construction so they contribute proper
densities.qpmR-estimation: priors, the AR(1) estimation
laboratory, identification, Bayes factors, and the full Czech
estimation with its results discussed (including the honestly
weakly-identified policy-response coefficient).priors(): the prior mini-language. normal(), beta(), gamma(),
invgamma(), uniform(), and truncate() exist only inside
priors() (evaluated in a controlled environment), so base R's
beta() and gamma() functions are never masked. Beta/gamma/
inverse-gamma use the mean/sd parametrization economists write down.qpm_estimate(): Bayesian estimation of any subset of structural
parameters and shock standard deviations over the Kalman-filter
likelihood. Posterior mode in transformed (unconstrained) space,
BFGS Hessian as the proposal seed, adaptive random-walk Metropolis
(Haario-style covariance adaptation during burn-in, acceptance
targeted at 0.25), multiple sequential chains, split R-hat and
Geyer effective sample sizes. Draws violating Blanchard-Kahn get
zero weight (the usual determinacy truncation). method = "mle"
reuses the machinery with flat priors on the declared supports.plot() overlays prior and
posterior densities. coef() extracts point estimates;
apply_estimate() recalibrates the model at them.posterior_forecast(): fan charts integrating over the posterior --
each draw re-solves the model and re-filters the data, so the bands
combine future-shock and parameter uncertainty.kalman_loglik(), a storage-free filter pass for
estimation speed.The policy-analysis layer is complete.
qpm_round(): one replayable artifact per forecast -- model,
calibration, data vintage, filtration, and the conditioned forecast
together. [qpm_condition()], qpm_scenario(), and add_judgment()
apply to rounds directly.save_round() / load_round() / list_rounds(): a plain-directory
round store; each round is a self-contained round.rds plus
human-readable CSV sidecars (forecast, data, calibration, judgment)
for auditing without R.compare_rounds(): the revision decomposition. The forecast revision
between two rounds is split into parameters, data revisions, new data
(outturns), conditions, and judgment by re-running the full pipeline
swapping one ingredient at a time. Contributions telescope (they sum
to the total exactly); the endpoints are verified against the
archived rounds, so a version drift is reported rather than silently
absorbed. Judgment overtaken by data (a conditioned quarter that has
become an outturn) is dropped and reported. Waterfall printing and
stacked revision charts.next_quarters() exported for quarter-label arithmetic; formulas in
models are stored without environments, keeping serialized rounds
small.qpm_condition(): hard conditional forecasts. Impose paths on any
variables at any horizons; qpmR backs out the minimum-norm structural
shocks (in standard-deviation units, optionally restricted to
instruments) that deliver them. The anticipated switch is
explicit: TRUE means the conditioned path is announced at the start
of the forecast and expectations react ahead of it, FALSE means
period-by-period surprises. Anticipated propagation uses the exact
news recursion F_j = N^j Q, N = -(AP+B)^{-1} A, verified in the
tests against a brute-force perfect-foresight solve. Fan bands are
recomputed as the Gaussian conditional distribution given the
conditions (zero width at conditioned points). Announced rate holds
reproduce the Laseen-Svensson (2011) anticipated-path reversal, as
they should.qpm_scenario(): shock-based alternative scenarios (announced or
surprise), additive on any forecast.add_judgment() / judgment_log(): the judgment ledger. State the
adjustment in percentage points; qpmR back-solves the supporting
shocks, keeps the forecast model-consistent, records author,
timestamp and rationale, and flags judgment requiring shocks above
two standard deviations. Entries are stored as absolute targets and
the full condition/judgment set is re-solved jointly, so the ledger
is replayable.2026-Q3 style) inherited from the
filtration; conditions and judgment can be addressed by label or by
horizon (h3). Conditioned and judgment points are marked on fan
charts.The filtration layer is complete.
qpm_filter(): Kalman filter + RTS smoother over the solved model.
Jointly infers every latent state (output gap, neutral rate,
equilibrium exchange rate, trends) and the historical structural
shocks from any subset of observed variables, with missing data and
ragged edges handled naturally. Innovation diagnostics (Ljung-Box,
outlier flags) are computed and printed. The likelihood is tested
against the exact closed-form Gaussian likelihood.qpm_decompose(): exact historical shock decompositions of the
smoothed history (additivity verified internally), with stacked-bar
plots.state_space(): exports the exact T, R, Z, H, Qc, P1
matrices used internally, so other estimators can build on qpmR.qpm_forecast() accepts a qpm_filtration as from, forecasting
from the smoothed end-of-sample state with the smoothed history kept
for fan charts.unit_tol of the unit circle count as stable (the usual qz-criterium
convention) and are reported separately. Steady states with free
trend levels use a minimum-norm least-squares normalization; a
drifted random walk (no fixed point) is a typed
qpm_no_steady_state error explaining the balanced-growth
limitation.qpm_filter()/state_space() switch automatically to an approximate
diffuse initialization (damped-Lyapunov large-variance prior,
kappa = 1e6) when the model has unit roots. Exact Durbin-Koopman
diffuse recursions remain on the roadmap.qpm_template("bkl", trends = "rw"): equilibrium real exchange rate
and potential growth as driftless random walks; the neutral rate
stays anchored by real interest parity (a free random walk there
would make steady-state gaps indeterminate).dy_obs = dy_bar + 4 * (y_gap - y_gap[-1])), so the model filters
on actual national-accounts data without modelling the level of
potential output.czechia: quarterly Czech data 1996Q1 onward in model units (CPI
inflation QoQ and YoY, 3M PRIBOR, real CZK/EUR, GDP growth, EURIBOR,
euro-area HICP), compiled reproducibly by data-raw/czechia.R from
FRED/OECD/Eurostat/ECB public endpoints. Filtering it with the rw
template reproduces the known history: the pre-GFC boom, the 2009
and 2013 recessions, the COVID crater, the koruna's trend real
appreciation, and the post-GFC fall in potential growth (pinned in
the test suite).plot() on decompositions gains a periods window argument.qpm_model(), x[-1] / E(x[+1]),
automatic auxiliary states), Klein/QZ solver with Blanchard-Kahn
diagnostics, steady states, irf(), simulate(), qpm_forecast()
with fan bands, qpm_lint(), and the canonical Berg-Karam-Laxton
small-open-economy template qpm_template("bkl").