Package {qpmR}

qpmR: Quarterly Projection Models for Monetary Policy Analysis

Description

Build, solve, and simulate the semi-structural quarterly projection models (QPM) used in central-bank Forecasting and Policy Analysis Systems (FPAS). Start with qpm_template() for the canonical Berg-Karam-Laxton small open economy model, or declare your own model with qpm_model(). Solve with qpm_solve(), inspect dynamics with irf(), simulate with stats::simulate(), and produce forecasts with fan bands via qpm_forecast().

Author(s)

Maintainer: Mustapha Mohammed muswaseja@gmail.com

See Also

Useful links:

• https://mustapha-wasseja.github.io/qpmR/

• https://github.com/Mustapha-Wasseja/qpmR

• Report bugs at https://github.com/Mustapha-Wasseja/qpmR/issues

Expectations operator (equation syntax only)

Description

E() marks model-consistent expectations inside eqs() declarations, e.g. E(pi[+1]). It is parsed by qpm_model() and never evaluated as an ordinary function.

Usage

E(x)

Arguments

Value

No return value; calling E() outside of model equations is an error by design.

Apply an extension block to a model

Description

Apply an extension block to a model

Usage

add_block(model, block)

Arguments

Value

The extended qpm_model, with the block recorded in meta$blocks.

Examples

m <- add_block(qpm_template("bkl"), block_food_cpi(weight = 0.4))
m

Add logged judgment to a forecast

Description

Central-bank forecasts are never raw model output: the desk knows about the announced electricity tariff, the tax change, the one-off the model cannot see. add_judgment() makes that adjustment a first-class, logged operation: you state the change you want (in percentage points, relative to the current forecast), qpmR back-solves the structural shocks needed to support it while keeping the whole forecast model-consistent, records who imposed it and why, and flags judgment that requires implausibly large shocks.

Usage

add_judgment(
  fc,
  ...,
  author = "desk",
  rationale = "",
  anticipated = NULL,
  instruments = NULL
)

Arguments

Details

Judgment entries are stored as absolute targets, so the ledger is replayable; the full set of conditions and judgment is re-solved jointly each time. Inspect the ledger with judgment_log().

Value

The adjusted qpm_forecast with the entry appended to its judgment ledger.

Examples

sol <- qpm_solve(qpm_template("bkl"))
fc <- qpm_forecast(sol, horizon = 8)
fc2 <- add_judgment(fc, pi = c(h2 = 0.5),
                    author = "desk",
                    rationale = "announced electricity tariff increase")
judgment_log(fc2)

Recalibrate a model at an estimate's point values

Description

Recalibrate a model at an estimate's point values

Usage

apply_estimate(est, type = "mean")

Arguments

Value

The estimation model recalibrated at the chosen point estimate (structural parameters and shock sds).

Examples

# see ?qpm_estimate

Disaggregated CPI: food and core inflation

Description

Splits headline inflation into food and core. Food is 30-50 percent of the consumption basket across most of sub-Saharan Africa and South Asia, and a single-inflation model is unusable there: supply shocks to food dominate headline, but monetary policy should look through the relative-price component. Practically every technical-assistance engagement rebuilds this split by hand.

Usage

block_food_cpi(
  weight = 0.35,
  persistence = 0.5,
  demand = 0.2,
  passthrough = 0.25,
  correction = 0.1,
  sd = 3
)

Arguments

Details

The block replaces headline inflation with an identity and adds:

• pi_core — the Phillips curve, now for core inflation (it keeps the template's b1, b2, b3 and the eps_pi shock);

• pi_food — food inflation: its own persistence, expectations of headline, demand, a stronger exchange-rate pass-through than core, and error correction on the relative food price;

• rp_food — the relative food price gap, which accumulates the food-core inflation differential and mean-reverts through f4.

Headline is pi = w_food * pi_food + (1 - w_food) * pi_core, so everything downstream (the 4-quarter average, the Fisher equation, the policy rule) continues to use headline. To target core instead, replace the policy rule with another block.

Value

A qpm_block().

Examples

m <- add_block(qpm_template("bkl"), block_food_cpi(weight = 0.45))
sol <- qpm_solve(m)
plot(irf(sol, shock = "eps_pifood"), vars = c("pi_food", "pi", "pi_core", "i"))

Foreign-exchange intervention (managed float)

Description

Turns the template's floating exchange rate into a managed one. A leaning-against-the-wind rule responds to the real exchange rate gap, and intervention enters the UIP block directly, so the same model spans a continuum of regimes: intensity = 0 is a free float, moderate values a managed float, and large values approach a peg. Program countries — where reserves, not just the policy rate, are the operative instrument — live in the middle of that range.

Usage

block_fx_intervention(intensity = 1, persistence = 0.6, sd = 1)

Arguments

Details

fx_int is intervention intensity, positive meaning sales of foreign exchange in support of the domestic currency (which appreciates the real exchange rate, lowering q).

Value

A qpm_block().

Examples

float <- qpm_solve(qpm_template("bkl"))
managed <- qpm_solve(add_block(qpm_template("bkl"),
                               block_fx_intervention(intensity = 1)))
# the same risk-premium shock moves the exchange rate less under management

The standard forecast-round chart pack

Description

Produces the chart set a forecast round is discussed from: the forecast fans, the filtered latent states, the historical shock decomposition of inflation, and the model's monetary transmission. Output is a multi-page PDF by default, or numbered PNGs.

Usage

chart_pack(
  round,
  file = NULL,
  charts = NULL,
  vars = NULL,
  width = 9,
  height = 6.5
)

Arguments

Value

The output path (invisibly), or NULL when drawing to the current device.

Examples

m <- qpm_template("bkl")
obs <- simulate(qpm_solve(m), nsim = 40, seed = 1, burn = 20)
obs$period <- next_quarters("2016-Q1", 40)
r <- qpm_round("demo", m, obs[, c("period", "pi", "i", "q")], horizon = 8)
pdf_path <- file.path(tempdir(), "pack.pdf")
chart_pack(r, pdf_path)

Compare two forecast rounds: the revision decomposition

Description

Decomposes the forecast revision between two rounds – "inflation for 2027-Q1 is 0.4pp higher than we said in June: why?" – into the contributions of new data (outturns), data revisions, calibration changes, changed conditions, and judgment, by re-running the full pipeline swapping one ingredient at a time. The contributions telescope, so they sum to the total revision exactly; the final step is verified against the new round's archived forecast.

Usage

compare_rounds(old, new, variables = NULL, store = "rounds")

## S3 method for class 'qpm_revision'
plot(x, variable = NULL, ...)

Arguments

Details

Requirements: both rounds must use the same model structure and the same observables, and their data must carry "YYYY-Qq" period labels so the calendars align. Comparison covers the calendar quarters both rounds forecast.

Value

An object of class qpm_revision: a data frame with one row per variable and overlap period, columns old, new, total, and the five contributions. Print shows waterfall tables; plot() draws stacked contribution bars across the overlap.

Examples

m <- qpm_template("bkl")
obs <- simulate(qpm_solve(m), nsim = 44, seed = 1, burn = 20)
obs$period <- next_quarters("2015-Q4", 44)
rA <- qpm_round("June", m, obs[1:40, c("period", "pi", "i", "q")], horizon = 8)
rB <- qpm_round("September", m, obs[, c("period", "pi", "i", "q")], horizon = 8)
rB <- add_judgment(rB, pi = stats::setNames(0.3, rB$forecast$periods[2]),
                   author = "desk", rationale = "tariff")
rev <- compare_rounds(rA, rB)
rev

Czech quarterly macroeconomic dataset

Description

Quarterly data for Czechia, 1996Q1 onward, in the units and sign conventions of the qpm_template() model, ready for qpm_filter(). Czechia is the canonical FPAS economy: a small open inflation targeter (since 1998) with three decades of clean data, a large disinflation, a currency-crisis start, the GFC, a floor episode, COVID, and the 2022 inflation wave.

Usage

czechia

Format

A data frame with one row per quarter and columns:

period

Quarter label, "1996-Q1" style.

pi

CPI inflation, QoQ annualised percent, seasonally adjusted with stats::stl() on the quarterly log index.

pi4

CPI inflation, year on year percent (no adjustment needed).

i

3-month PRIBOR, percent p.a., quarterly average – a proxy for the CNB policy rate.

q

Real CZK/EUR exchange rate, 100 times log, CPI-based, normalized so the 2015 average is zero; an increase is a real depreciation of the koruna.

dy_obs

Real GDP growth, QoQ annualised percent, from seasonally and calendar adjusted chain-linked volumes.

istar

3-month EURIBOR, percent p.a., quarterly average.

pistar

Euro-area HICP inflation, year on year percent.

Details

Missing values are genuine ragged edges (e.g. the euro exists only from 1999, so q, istar start later); qpm_filter() handles them. Observe pi4 or pi, not both – they are linked by an identity and the filter will report the collinearity.

Source

Compiled by data-raw/czechia.R from FRED series CLVMNACSCAB1GQCZ (Eurostat national accounts), CZECPIALLMINMEI, IR3TIB01CZM156N, IR3TIB01EZM156N (OECD Main Economic Indicators), CP0000EZ19M086NEST (Eurostat HICP), and the ECB reference exchange rate EXR/Q.CZK.EUR.SP00.A. Retrieved 2026-08-22.

Examples

head(czechia)
m <- qpm_calibrate(qpm_template("bkl", trends = "rw"),
                   pi_tar = 2, istar_ss = 2, pistar_ss = 2, prem_ss = 1)
cz <- czechia[czechia$period >= "1999",
              c("period", "pi4", "i", "q", "dy_obs", "istar", "pistar")]
fit <- qpm_filter(m, cz)
fit

Generalized eigenvalues of a solved model

Description

Generalized eigenvalues of a solved model

Usage

eigen_table(x)

Arguments

Value

A data frame with the moduli of the generalized eigenvalues of the model companion pencil, sorted ascending, with stability flags.

Examples

head(eigen_table(qpm_solve(qpm_template("bkl"))))

Declare model equations

Description

Each equation is a two-sided formula. Lags and leads use index notation: x[-1] is the first lag, x[+1] the first lead. Wrap expectations in E(): E(pi[+1]) is the model-consistent expectation of next quarter's inflation. Longer lags and leads (e.g. E(pi4[+4])) are handled automatically via auxiliary state variables.

Usage

eqs(...)

Arguments

Value

A list of formulas.

Examples

eqs(
  pi ~ b1 * pi[-1] + (1 - b1) * E(pi[+1]) + b2 * y_gap + eps_pi
)

Forecast error variance decomposition

Description

Splits the forecast error variance of each variable at each horizon into the contributions of the structural shocks — "how much of inflation uncertainty two years out is the cost-push shock?". For

x_t = P x_{t-1} + Q e_t, e_t ~ N(0, S)

the h-step forecast error variance is

V_h = sum_{j<h} P^j Q S Q' (P^j)'

and the share of shock k is the same sum with only column k of Q active. Shares sum to one across shocks for every variable and horizon.

Usage

fevd(x, ...)

## S3 method for class 'qpm_solution'
fevd(x, horizon = 24, vars = NULL, shocks = NULL, ...)

## S3 method for class 'qpm_fevd'
plot(x, var = NULL, drop_zero = TRUE, ...)

Arguments

Details

Shares remain well defined when the model has unit roots, even though the variances themselves grow without bound.

Value

A data frame of class qpm_fevd in long format with columns variable, shock, horizon, share and variance. print() shows the dominant shocks; plot() draws stacked shares.

Examples

sol <- qpm_solve(qpm_template("bkl"))
fv <- fevd(sol, horizon = 20)
fv
plot(fv, var = "pi")

Impulse response functions

Description

Impulse response functions

Usage

irf(x, ...)

## S3 method for class 'qpm_solution'
irf(x, shock = NULL, horizon = 24, size = NULL, vars = NULL, ...)

Arguments

Value

A data frame of class qpm_irf in long format with columns shock, variable, horizon, value (deviations from steady state). Printing shows peak effects; plot() draws panel charts.

Examples

sol <- qpm_solve(qpm_template("bkl"))
ir <- irf(sol, shock = "eps_i", horizon = 16)
ir
plot(ir, vars = c("pi", "y_gap", "i", "q"))

Print a forecast's judgment ledger

Description

Print a forecast's judgment ledger

Usage

judgment_log(fc)

Arguments

Value

The ledger data frame, invisibly.

Examples

sol <- qpm_solve(qpm_template("bkl"))
fc <- add_judgment(qpm_forecast(sol, horizon = 8), pi = c(h2 = 0.5),
                   author = "desk", rationale = "tariff")
judgment_log(fc)

Log-likelihood of a filtration or an estimate

Description

For a qpm_filtration this is the Kalman-filter log-likelihood of the data under the calibrated model, with zero degrees of freedom (nothing was estimated). For a qpm_estimate it is the log-likelihood at the posterior mode (or at the maximum for method = "mle"), with degrees of freedom equal to the number of estimated parameters — so stats::AIC() and stats::BIC() work.

Usage

## S3 method for class 'qpm_filtration'
logLik(object, ...)

## S3 method for class 'qpm_estimate'
logLik(object, ...)

Arguments

Value

An object of class logLik.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 40, seed = 1, burn = 20)
fit <- qpm_filter(sol, obs[, c("period", "pi", "i", "q")])
logLik(fit)

Marginal likelihood of an estimated model

Description

Computes the log marginal likelihood \log p(y) of a Bayesian estimate – the quantity whose differences across models on the same data are log Bayes factors. Two estimators are reported:

Usage

marginal_likelihood(est, taus = seq(0.1, 0.9, by = 0.2))

Arguments

Details

• Modified harmonic mean (Geweke 1999) from the posterior draws, computed for a range of truncation probabilities; a small spread across truncations indicates a reliable estimate.

• Laplace approximation at the posterior mode in transformed space (parametrization-invariant because the Jacobian is included), with a numerically differenced Hessian.

Truncated priors created with truncate() are renormalized numerically at construction, so they contribute proper densities here.

Value

An object of class qpm_logml: ⁠$logml⁠ (harmonic-mean estimate, averaged over taus), ⁠$by_tau⁠, ⁠$laplace⁠, and the spread across truncations.

References

Geweke, J. (1999). Using simulation methods for Bayesian econometric models. Econometric Reviews, 18(1), 1-73.

Examples

m <- qpm_model(variables = vars(x = "x"), shocks = shocks(e),
               equations = eqs(x ~ rho * x[-1] + e),
               params = list(rho = 0.5))
obs <- simulate(qpm_solve(qpm_calibrate(m, rho = 0.8)), nsim = 120, seed = 1)
est <- qpm_estimate(m, obs, priors(rho = beta(0.5, 0.2)),
                    iter = 300, chains = 2, seed = 2, verbose = FALSE)
marginal_likelihood(est)

Model-implied moments, and how they compare with the data

Description

The standard calibration check: does the model reproduce the volatilities and persistence actually observed? Reports the population standard deviation and autocorrelations implied by the solved model — from the stationary covariance V = P V P' + Q S Q' and corr_k = diag(P^k V) / diag(V) — next to the same statistics computed from data, plus the shock that accounts for most of each variable's unconditional variance.

Usage

model_properties(x, data = NULL, vars = NULL, lags = c(1, 4))

Arguments

Details

Population moments exist only for stationary models. When the model has unit roots (random-walk trends) they are undefined, and the function reports that rather than returning nonsense; use fevd() and qpm_filter() diagnostics instead.

Value

An object of class qpm_properties: a data frame with the model and (optionally) data moments.

Examples

sol <- qpm_solve(qpm_template("bkl"))
model_properties(sol, vars = c("y_gap", "pi", "i", "q"))

# against simulated data
obs <- simulate(sol, nsim = 200, seed = 5, burn = 50)
model_properties(sol, data = obs, vars = c("y_gap", "pi", "i"))

Generate consecutive quarter labels

Description

Generate consecutive quarter labels

Usage

next_quarters(last, H)

Arguments

Value

Character vector of H quarter labels.

Examples

next_quarters("2026-Q3", 4)

Number of observations

Description

Number of observations

Usage

## S3 method for class 'qpm_filtration'
nobs(object, ...)

## S3 method for class 'qpm_estimate'
nobs(object, ...)

Arguments

Value

Number of time periods used.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 40, seed = 1, burn = 20)
nobs(qpm_filter(sol, obs[, c("period", "pi", "i")]))

Forecast with parameter uncertainty (posterior fan)

Description

Produces a forecast whose fan bands integrate over the posterior: for each sampled parameter draw the model is re-solved, the data re-filtered (so the initial state reflects that draw), and a path sampled from the resulting Gaussian predictive; pointwise band quantiles are then taken across draws. The bands therefore combine future-shock uncertainty with parameter uncertainty — they are wider than the fixed-parameter fans of qpm_forecast().

Usage

posterior_forecast(est, horizon = 12, ndraws = 200, bands = c(0.5, 0.7, 0.9))

Arguments

Value

A qpm_forecast-compatible object (posterior-mean path, quantile bands, smoothed history from the posterior-mean model); ⁠$posterior⁠ records the number of draws used.

Examples

m <- qpm_model(variables = vars(x = "x"), shocks = shocks(e),
               equations = eqs(x ~ rho * x[-1] + e),
               params = list(rho = 0.5))
obs <- simulate(qpm_solve(qpm_calibrate(m, rho = 0.8)), nsim = 100, seed = 1)
est <- qpm_estimate(m, obs, priors(rho = beta(0.5, 0.2)),
                    iter = 300, chains = 1, seed = 2, verbose = FALSE)
fc <- posterior_forecast(est, horizon = 8, ndraws = 15)
plot(fc, vars = "x")

Declare priors for Bayesian estimation

Description

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

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)

Value

An object of class qpm_priors.

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

Model extension blocks

Description

A block is a documented, reusable bundle of variables, shocks, parameters and equations that adapts a template to a country. Blocks are how a technical-assistance engagement customizes the canonical model without forking it: add_block() applies one, qpm_diff() shows exactly what changed, and the result is an ordinary qpm_model.

Usage

qpm_block(
  name,
  variables = NULL,
  shocks = NULL,
  params = list(),
  sigma = NULL,
  equations = NULL,
  description = ""
)

Arguments

Details

Equations whose left-hand side names a variable that already exists replace that variable's equation; equations for newly declared variables are appended. Left-hand sides must be bare variable names.

Value

An object of class qpm_block.

See Also

block_food_cpi(), block_fx_intervention()

Examples

b <- qpm_block(
  name = "risk premium shifter",
  params = list(prem_extra = 0),
  equations = eqs(prem ~ rho_prem * prem[-1] +
                    (1 - rho_prem) * (prem_ss + prem_extra) + eps_prem)
)
m <- add_block(qpm_template("bkl"), b)

Update a model's calibration

Description

Update a model's calibration

Usage

qpm_calibrate(model, ..., sigma = NULL)

Arguments

Value

The updated qpm_model.

Examples

m <- qpm_template("bkl")
m <- qpm_calibrate(m, b2 = 0.3, c2 = 2)

Compare the behaviour of two or more models

Description

qpm_diff() compares model structure — what a country team changed in the code. This compares model behaviour: the transmission of a given shock and the moments each specification implies. When a calibration is revised, both questions matter, and the second is the one a policy audience asks.

Usage

qpm_compare_models(models, shock = NULL, vars = NULL, horizon = 20)

## S3 method for class 'qpm_model_comparison'
plot(x, vars = NULL, ...)

Arguments

Value

An object of class qpm_model_comparison holding the impulse responses and, for stationary models, the implied moments.

Examples

base <- qpm_template("bkl")
flat <- qpm_calibrate(base, b2 = 0.05)   # a much flatter Phillips curve
cmp <- qpm_compare_models(list(baseline = base, flat = flat),
                          shock = "eps_y")
cmp
plot(cmp, vars = c("pi", "i"))

Conditional forecasts: impose paths, back out the shocks

Description

Imposes hard conditions on future values of model variables and finds the minimum-norm structural shocks (in standard-deviation units, optionally restricted to instruments) that deliver them. This is how a policy question becomes a forecast: "what if the policy rate is held at 3.5 for four quarters?" or "what paths are consistent with inflation back at target by 2027?".

Usage

qpm_condition(fc, ..., anticipated = FALSE, instruments = NULL)

Arguments

Details

The anticipated switch is the economics: TRUE means the whole conditioned path is announced at the start of the forecast, so expectations react immediately (forward-looking variables move before the conditioning bites); FALSE means the implied shocks arrive as period-by-period surprises. The two produce materially different paths in any forward-looking model, and practitioners frequently do not know which one their tools assume.

Fan bands are recomputed as the Gaussian conditional distribution of the path given the conditions (using all shocks), so conditioned points have (near) zero width. The mean path uses only the chosen instruments; when instruments is restricted, mean and bands answer slightly different questions – see Antolin-Diaz, Petrella and Rubio-Ramirez (2021) for the full treatment, which is on the qpmR roadmap.

Value

The conditioned qpm_forecast, with ⁠$shocks_implied⁠ (raw units), ⁠$shocks_implied_std⁠ (standard deviations), and the conditions recorded. Printing summarizes the implied shocks and flags any larger than two standard deviations.

Examples

sol <- qpm_solve(qpm_template("bkl"))
fc <- qpm_forecast(sol, horizon = 8)
hold <- qpm_condition(fc, i = c(h1 = 9.5, h2 = 9.5, h3 = 9.5, h4 = 9.5),
                      anticipated = TRUE, instruments = "eps_i")
hold

Historical counterfactuals

Description

Rewrites history with some shocks switched off or scaled: "what if the central bank had simply followed its rule through 2022?" is the path implied by setting the policy shocks to zero over that window and re-running the model from the same starting point with all other shocks unchanged.

Usage

qpm_counterfactual(fit, shocks, periods = NULL, factor = 0, label = NULL)

## S3 method for class 'qpm_counterfactual'
plot(x, vars = NULL, ...)

Arguments

Details

This differs from qpm_decompose(), which attributes the history that happened; here the history is replayed under a different assumption. The counterfactual is only as good as the model's invariance to the intervention — a Lucas-critique caveat that applies to every exercise of this kind and is worth stating in any write-up.

Value

An object of class qpm_counterfactual holding the actual and counterfactual paths and their difference.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 60, seed = 4, burn = 20)
obs$period <- next_quarters("2010-Q4", 60)
fit <- qpm_filter(sol, obs[, c("period", "pi", "i", "q")])
cf <- qpm_counterfactual(fit, shocks = "eps_i",
                         label = "no policy surprises")
cf
plot(cf, vars = c("pi", "i", "y_gap"))

Historical shock decomposition

Description

Splits the smoothed history of every variable into the additive contributions of each structural shock plus the carry-over of the pre-sample initial state: with smoothed shocks e_t,

a_t = sum_j c_j(t) + c_0(t), c_j(t) = P c_j(t-1) + Q_j e_{j,t}

The contributions sum exactly to the smoothed state (deviations from steady state); this identity is verified internally.

Usage

qpm_decompose(fit, vars = NULL)

## S3 method for class 'qpm_decomposition'
plot(x, var = NULL, drop_zero = TRUE, periods = NULL, ...)

Arguments

Value

A long data frame of class qpm_decomposition with columns period, variable, component (shock names plus "initial"), and value (contribution, in deviations from steady state). plot() draws a stacked-bar decomposition with the smoothed total overlaid.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 60, seed = 3, burn = 20)
fit <- qpm_filter(sol, obs[, c("period", "pi", "i", "q")])
dec <- qpm_decompose(fit)
plot(dec, var = "pi")

Compare two models structurally

Description

Shows what a customization actually changed: variables, shocks and parameters added or removed, equations changed, and recalibrations. This is how a model review works when country teams adapt a template.

Usage

qpm_diff(old, new, tol = 1e-10)

Arguments

Value

An object of class qpm_model_diff, printed as a report.

Examples

qpm_diff(qpm_template("bkl"),
         add_block(qpm_template("bkl"), block_food_cpi()))

Temporal disaggregation of low-frequency data

Description

Turns annual series into quarterly ones consistent with the annual totals. Many of the economies these models are built for publish national accounts annually, so a quarterly projection model has to start by constructing quarterly GDP — usually from an indicator such as industrial production, imports or credit.

Usage

qpm_disaggregate(
  annual,
  indicator = NULL,
  frequency = 4,
  method = c("denton", "chow-lin"),
  conversion = c("sum", "average"),
  rho = NULL
)

## S3 method for class 'qpm_disaggregation'
plot(x, ...)

Arguments

Details

Two standard methods:

• "denton" — Denton-Cholette proportional first differences. Minimises the squared change in the ratio of the quarterly series to the indicator (or, without an indicator, in the series itself), subject to matching the annual figures. Purely a smoothing method: no regression, no parameters.

• "chow-lin" — generalised least squares on the indicator with AR(1) quarterly residuals, distributing the annual residual across quarters. Uses the indicator's regression relationship, so it is the better choice when the indicator genuinely tracks the target.

Both enforce the aggregation constraint exactly: "sum" for flows (annual GDP is the sum of quarters), "average" for stocks and index levels.

Value

An object of class qpm_disaggregation: the high-frequency series, the method used and the fitted parameters.

References

Denton, F. T. (1971); Chow, G. C. and Lin, A. (1971).

Examples

# annual GDP with a quarterly indicator
set.seed(1)
q_true <- cumsum(rnorm(40, 0.5)) + 100
annual <- colSums(matrix(q_true, nrow = 4))
ind <- q_true + rnorm(40, 0, 1)
d <- qpm_disaggregate(annual, ind, method = "chow-lin")
d
plot(d)

Estimate model parameters (Bayesian or maximum likelihood)

Description

Estimates any subset of structural parameters and shock standard deviations from data, using the Kalman-filter likelihood of the solved model. method = "bayes" finds the posterior mode (in transformed, unconstrained space), seeds an adaptive random-walk Metropolis sampler with the inverse Hessian, and returns posterior draws with split R-hat and effective-sample-size diagnostics. method = "mle" uses the same machinery with flat priors on the declared supports.

Usage

qpm_estimate(
  model,
  data,
  priors,
  observables = NULL,
  measurement_error = 0,
  kappa = 1e+06,
  method = c("bayes", "mle"),
  iter = 4000,
  burn = floor(iter/2),
  chains = 2,
  thin = 1,
  seed = NULL,
  verbose = TRUE
)

## S3 method for class 'qpm_estimate'
coef(object, type = c("mean", "mode", "median"), ...)

Arguments

Details

Draws that violate Blanchard-Kahn (indeterminacy or explosiveness) receive zero posterior weight — the usual truncation of the prior to the determinacy region. Everything without a prior stays calibrated at its current value.

Value

An object of class qpm_estimate: posterior draws (natural units), the mode, acceptance rate, split R-hat and effective sample sizes, and the originating model/data. Use coef() to extract point estimates and apply_estimate() to recalibrate the model.

Examples

m <- qpm_model(variables = vars(x = "x"), shocks = shocks(e),
               equations = eqs(x ~ rho * x[-1] + e),
               params = list(rho = 0.5))
obs <- simulate(qpm_solve(qpm_calibrate(m, rho = 0.8)), nsim = 100, seed = 1)
est <- qpm_estimate(m, obs, priors(rho = beta(0.5, 0.2), e = invgamma(1, 0.3)),
                    iter = 300, chains = 2, seed = 2, verbose = FALSE)
est

Estimate latent states from data (Kalman filter/smoother)

Description

Runs the Kalman filter and RTS smoother over the solved model, jointly inferring every latent variable — output gap, neutral rate, equilibrium exchange rate, trend processes — and the historical structural shocks from whatever subset of variables you actually observe. Missing values (ragged edges, gappy series) are handled naturally.

Usage

qpm_filter(x, data, observables = NULL, measurement_error = 0, kappa = 1e+06)

Arguments

Details

The filter is initialized at the model's stationary distribution (Lyapunov covariance), which is exact for the stationary models qpmR currently supports.

Value

An object of class qpm_filtration: smoothed states in levels (⁠$states⁠), their standard errors (⁠$se⁠), smoothed structural shocks (⁠$shocks⁠), the log-likelihood (⁠$loglik⁠), innovation diagnostics (⁠$diag⁠), and the full expanded-state matrix (⁠$states_dev⁠). Feed it to qpm_decompose() for historical shock decompositions or to qpm_forecast() to forecast from the smoothed current state.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 60, seed = 3, burn = 20)
fit <- qpm_filter(sol, obs[, c("period", "pi", "i", "q")])
fit
plot(fit, vars = c("y_gap", "r_bar"))

Model forecast with uncertainty bands

Description

Iterates the solved model forward from an initial state and computes analytic forecast uncertainty from the shock variances,

V_h = P V_{h-1} P' + Q S Q'

giving Gaussian fan bands around the mean path. The result can then be conditioned on assumed paths with qpm_condition(), shifted by shock scenarios with qpm_scenario(), or adjusted with logged judgment via add_judgment().

Usage

qpm_forecast(
  object,
  from = NULL,
  horizon = 12,
  bands = c(0.5, 0.7, 0.9),
  sigma = NULL
)

Arguments

Value

An object of class qpm_forecast: a list with paths (long data frame: variable, h, mean, and ⁠lo_*⁠/⁠hi_*⁠ per band, in levels), forecast-period labels in ⁠$periods⁠, plus the machinery needed for conditioning.

Examples

sol <- qpm_solve(qpm_template("bkl"))
histq <- simulate(sol, nsim = 40, seed = 7, burn = 20)
fc <- qpm_forecast(sol, from = histq, horizon = 12)
fc
plot(fc, vars = c("pi", "i", "y_gap", "q"))

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

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"))

Check a model for common specification problems

Description

Runs static checks (unused shocks/parameters, bare leads outside E(), calibrations outside plausible ranges when the template documents them) and then attempts to solve the model, reporting the Blanchard-Kahn outcome. Unit algebra on declared units is on the roadmap.

Usage

qpm_lint(model)

Arguments

Value

An object of class qpm_lint (a data frame of checks with status ok, note, warn, or fail), printed with markers.

Examples

qpm_lint(qpm_template("bkl"))

Define a quarterly projection model

Description

Declares a linear semi-structural model with model-consistent expectations. Equations are parsed immediately, so unknown symbols and malformed lag/lead references are caught at construction time. Coefficients are extracted from the current calibration when the model is solved with qpm_solve().

Usage

qpm_model(
  name = "QPM model",
  variables,
  shocks,
  equations,
  params = list(),
  sigma = NULL,
  meta = list()
)

Arguments

Value

An object of class qpm_model.

Examples

m <- qpm_model(
  name      = "AR(1) toy",
  variables = vars(x = "A persistent process"),
  shocks    = shocks(eps_x),
  equations = eqs(x ~ rho * x[-1] + eps_x),
  params    = list(rho = 0.8)
)
m

Write (and optionally render) a monetary policy report

Description

Turns a forecast round into the document a policy meeting is run from: an executive summary with the numbers filled in, the forecast table and fan charts, the filtered gaps, the shock decomposition, the judgment ledger, an optional revision decomposition against the previous round, and a reproducibility appendix.

Usage

qpm_report(
  round,
  file,
  compare_to = NULL,
  store = "rounds",
  render = TRUE,
  engine = c("auto", "quarto", "rmarkdown"),
  quiet = TRUE
)

Arguments

Details

The report is always written as a self-contained .Rmd source, on the principle that institutions replace the template's text, not its plumbing: edit the file, re-render, keep the analysis. Rendering additionally needs pandoc (via Quarto or RStudio/Positron); where none is available — air-gapped forecasting machines, bare CI runners — qpm_report() reports that and returns the .Rmd unrendered rather than failing.

Value

The path actually produced (the rendered document, or the .Rmd when rendering was not possible), invisibly.

Examples

m <- qpm_template("bkl")
obs <- simulate(qpm_solve(m), nsim = 40, seed = 1, burn = 20)
obs$period <- next_quarters("2016-Q1", 40)
r <- qpm_round("demo", m, obs[, c("period", "pi", "i", "q")], horizon = 8)
src <- qpm_report(r, file.path(tempdir(), "mpr.Rmd"), render = FALSE)

Express a balance of risks (skewed fan charts)

Description

Central banks rarely believe their fan charts are symmetric: the published judgement is usually "risks to inflation are tilted to the upside". qpm_risk() applies that judgement, replacing the Gaussian bands of a forecast with two-piece normal bands whose mode stays on the model's projection while the mean shifts by the stated skew. Total uncertainty is preserved: the variance implied by the model at each horizon is held fixed, so a skew redistributes risk rather than adding it.

Usage

qpm_risk(fc, ..., author = "MPC", rationale = "")

Arguments

Details

The skew is stated in the variable's own units as mean minus mode — a value of 0.3 on inflation means the risks are worth 0.3 percentage points to the upside. Skews may be given for any subset of variables and horizons; anything unstated keeps symmetric bands.

Unlike add_judgment(), which moves the projection itself and back-solves the shocks that support it, this changes only the shape of the distribution around an unchanged central path.

Value

The forecast with skewed bands. ⁠$risk⁠ records the skews and ⁠$paths⁠ gains a mode column alongside mean.

Examples

sol <- qpm_solve(qpm_template("bkl"))
fc <- qpm_forecast(sol, horizon = 8)
risky <- qpm_risk(fc, pi = 0.4, author = "MPC",
                  rationale = "energy prices tilted to the upside")
risky
plot(risky, vars = c("pi", "i"))

Forecast rounds: one replayable artifact per forecast

Description

A forecast round binds everything that produced a forecast – the model and its calibration, the data vintage, the filtration, the forecast with its conditions and judgment – into one object that can be saved, reloaded, re-run, and compared with later rounds via compare_rounds(). This is the object an institution archives: next quarter, "why did the forecast move?" is answered from the rounds, not from memory.

Usage

qpm_round(
  name,
  model,
  data,
  observables = NULL,
  horizon = 12,
  bands = c(0.5, 0.7, 0.9),
  measurement_error = 0,
  kappa = 1e+06
)

Arguments

Details

qpm_round() runs the standard pipeline (solve, filter, baseline forecast). Conditions, scenarios, and judgment are then applied to the round directly: qpm_condition(), qpm_scenario(), and add_judgment() all accept a round and update its forecast.

Value

An object of class qpm_round with elements model, data, solution, fit, and forecast.

Examples

m <- qpm_template("bkl")
sol <- qpm_solve(m)
obs <- simulate(sol, nsim = 40, seed = 1, burn = 20)
obs$period <- next_quarters("2016-Q1", 40)
r <- qpm_round("test round", m, obs[, c("period", "pi", "i", "q")],
               horizon = 8)
r

Evaluate alternative policy rules

Description

Answers the question a policy committee actually asks — what if we responded differently? — by re-solving the model over a grid of rule parameters and scoring each one by the unconditional loss

L = sum_v w_v var(v) + sum_v w^d_v var(v - v_{-1})

computed from the model's stationary covariance rather than by simulation, so it is exact. Tracing the resulting variance pairs gives the inflation-output variability frontier (the Taylor curve).

Usage

qpm_rule_eval(model, grid, loss = c(pi = 1, y_gap = 0.5), diff_loss = NULL)

## S3 method for class 'qpm_rule_eval'
plot(x, xvar = NULL, yvar = NULL, ...)

Arguments

Details

Rules that violate Blanchard-Kahn are reported as such rather than dropped: a policy response too weak to deliver determinacy is a finding, not a missing row.

Value

A data frame of class qpm_rule_eval: the grid, the variance of each targeted variable, the loss, and the Blanchard-Kahn outcome.

Examples

m <- qpm_template("bkl")
grid <- expand.grid(c2 = c(1.2, 1.5, 2, 3), c3 = c(0, 0.5, 1))
ev <- qpm_rule_eval(m, grid, loss = c(pi = 1, y_gap = 0.5),
                    diff_loss = c(i = 0.5))
ev
plot(ev)

Shock-based alternative scenarios

Description

Adds a specified path of structural shocks to a forecast – "what if oil pushes the exchange rate 10 percent weaker next quarter?" – without any inversion. Under anticipated = TRUE the shock path is announced at the start of the forecast and expectations react ahead of it.

Usage

qpm_scenario(fc, shocks, anticipated = FALSE, label = NULL)

Arguments

Value

The shifted qpm_forecast (bands unchanged: a deterministic scenario shifts the mean, not the uncertainty).

Examples

sol <- qpm_solve(qpm_template("bkl"))
fc <- qpm_forecast(sol, horizon = 8)
dep <- qpm_scenario(fc, shocks = list(eps_q = c(h1 = 3)),
                    label = "10pct depreciation")

Solve a model under model-consistent expectations

Description

Reduces the model to first-order form (adding auxiliary states for lags/leads beyond one quarter), computes the steady state, and solves for the unique stable rational-expectations solution

x_t = P x_{t-1} + Q e_t

via the generalized Schur (QZ) decomposition (Klein 2000), with full Blanchard-Kahn diagnostics.

Usage

qpm_solve(model, tol = 1e-07)

Arguments

Value

An object of class qpm_solution with elements P, Q (transition and impact matrices over the expanded state vector), ss (steady state), and an eigenvalue table (see eigen_table()).

References

Klein, P. (2000). Using the generalized Schur form to solve a multivariate linear rational expectations model. Journal of Economic Dynamics and Control, 24(10), 1405-1423.

Examples

sol <- qpm_solve(qpm_template("bkl"))
sol

Shipped model templates

Description

"bkl" is the canonical semi-structural small-open-economy quarterly projection model in the tradition of Berg, Karam and Laxton (2006, IMF WP/06/80-81): an IS curve, a hybrid Phillips curve, a forward-looking inflation-targeting policy rule, and a dampened (hybrid) UIP block, plus equilibrium-trend and foreign processes, and an observation block for real GDP growth (dy_obs = dy_bar + 4 * (y_gap - y_gap[-1])), so the model can be filtered on actual national-accounts data without modelling the level of potential output. The default calibration is illustrative, for a higher-inflation emerging economy ("Meridia"); it is not any actual country. See czechia for a real dataset and a matching recalibration example.

Usage

qpm_template(
  name = c("bkl", "bkl_food", "managed_fx"),
  trends = c("stationary", "rw")
)

Arguments

Details

trends selects the equilibrium processes:

• "stationary" (default): AR(1) trends anchored at steady-state parameters; the model is fully stationary.

• "rw": driftless random walks for the equilibrium real exchange rate and potential growth (whose levels are pure normalizations); the neutral rate stays anchored by real interest parity (rstar + prem), since a free random walk there would leave steady-state gaps indeterminate. The model then has unit roots: free trend levels are normalized to minimum norm in the steady state, and qpm_filter() switches to diffuse initialization automatically. This is the configuration for real data, where trends drift.

Conventions: gaps in percentage points; inflation QoQ annualised; interest rates in percent per annum; q is 100 times the log real exchange rate, an increase is a real depreciation; dy_obs is QoQ annualised real GDP growth.

Country-shaped variants are shipped as shortcuts for the canonical model plus an extension block (see add_block()):

• "bkl_food" — headline CPI split into food and core (block_food_cpi()), the configuration for economies where food is a large share of the basket.

• "managed_fx" — a leaning-against-the-wind intervention rule entering the UIP block (block_fx_intervention()).

Value

A calibrated qpm_model.

References

Berg, A., Karam, P., and Laxton, D. (2006). A Practical Model-Based Approach to Monetary Policy Analysis - Overview. IMF Working Paper 06/80; and the companion How-To guide, IMF WP 06/81.

Examples

m <- qpm_template("bkl")
summary(m)
m_rw <- qpm_template("bkl", trends = "rw")

Use the compiled Kalman filter

Description

qpmR ships a compiled (C++) Kalman filter and an equivalent reference implementation in R. The compiled one is used by default because estimation runs it once per posterior draw; the R one is kept because the two agreeing to machine precision is what makes the compiled path trustworthy, and it is useful when debugging.

Usage

qpm_use_cpp()

Details

Set options(qpmR.use_cpp = FALSE) to force the R implementation.

Value

TRUE if the compiled filter will be used.

Examples

qpm_use_cpp()

One-step-ahead prediction errors and fitted values

Description

residuals() returns the filter's one-step-ahead prediction errors for the observed series (type = "innovation"), the same divided by their standard deviations ("standardized", which is what the outlier flags use), or the smoothed structural shocks ("shock"). fitted() returns the one-step-ahead predictions of the observables, so that observed = fitted + innovation.

Usage

## S3 method for class 'qpm_filtration'
residuals(object, type = c("innovation", "standardized", "shock"), ...)

## S3 method for class 'qpm_filtration'
fitted(object, ...)

Arguments

Value

A data frame with a period column and one column per series.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 40, seed = 1, burn = 20)
fit <- qpm_filter(sol, obs[, c("period", "pi", "i", "q")])
head(residuals(fit, "standardized"))
head(fitted(fit))

Print a forecast's balance-of-risks assessment

Description

Print a forecast's balance-of-risks assessment

Usage

risk_log(fc)

Arguments

Value

The risk table, invisibly.

Examples

sol <- qpm_solve(qpm_template("bkl"))
fc <- qpm_risk(qpm_forecast(sol, horizon = 8), pi = 0.4,
               rationale = "upside energy risk")
risk_log(fc)

Save, load, and list forecast rounds

Description

A round store is a plain directory: each round lives in ⁠store/<slug>/⁠ as a self-contained round.rds plus human-readable sidecars (forecast.csv, data.csv, calibration.csv, and judgment.csv when judgment was applied) so that a round can be audited without R.

Usage

save_round(round, store, overwrite = FALSE)

load_round(name, store = "rounds")

list_rounds(store = "rounds")

Arguments

Value

save_round() returns the round directory invisibly; load_round() returns the qpm_round; list_rounds() returns a data frame of the store's contents.

Examples

m <- qpm_template("bkl")
obs <- simulate(qpm_solve(m), nsim = 40, seed = 1, burn = 20)
obs$period <- next_quarters("2016-Q1", 40)
r <- qpm_round("demo", m, obs[, c("period", "pi", "i", "q")], horizon = 8)
store <- file.path(tempdir(), "rounds")
save_round(r, store)
list_rounds(store)
r2 <- load_round("demo", store)

Declare the structural shocks of a model

Description

Accepts bare names or character strings.

Usage

shocks(...)

Arguments

Value

A character vector of shock names.

Examples

shocks(eps_y, eps_pi, eps_i)

Simulate a solved model

Description

Draws Gaussian shocks with the model's standard deviations and iterates the solved transition. Returns variables in levels (steady state plus simulated deviations). The final full state is stored as an attribute so a simulation can be handed straight to qpm_forecast().

Usage

## S3 method for class 'qpm_solution'
simulate(object, nsim = 40, seed = NULL, sigma = NULL, burn = 0, ...)

Arguments

Value

A data frame of class qpm_sim (period plus one column per declared variable), with attributes state (final expanded state, deviations) and shocks (the drawn shocks).

Examples

sol <- qpm_solve(qpm_template("bkl"))
hist <- simulate(sol, nsim = 60, seed = 42, burn = 20)
head(hist)

State-space representation of a solved model

Description

Exposes the exact matrices qpmR itself uses for filtering, so other estimators and filters can be built on top of a solved model. The representation (in deviations from steady state) is

a_t = T a_{t-1} + R e_t, e_t ~ N(0, Qc)

y_t = Z a_t + d + u_t, u_t ~ N(0, H)

with d the steady state of the observables and P1 the stationary (Lyapunov) covariance used to initialize the filter.

Usage

state_space(solution, observables = NULL, measurement_error = 0, kappa = 1e+06)

Arguments

Details

For stationary models P1 is the exact stationary covariance. When the model has unit roots (random-walk trends), an approximate diffuse initialization is used: P1 solves the Lyapunov equation for the slightly damped transition sqrt(1 - 1/kappa) * T, which reproduces the stationary covariance in stable directions and a variance of order kappa in unit-root directions, with the exact cross-coupling. Exact Durbin-Koopman diffuse recursions are on the roadmap.

Value

A list with elements T, R, Z, d, Qc, H, P1, vars_all, observables, diffuse, n_unit.

Examples

sol <- qpm_solve(qpm_template("bkl"))
ss <- state_space(sol, observables = c("pi", "i", "q"))
dim(ss$T); ss$d

Steady state of a model or solution

Description

Steady state of a model or solution

Usage

steady_state(x, ...)

Arguments

Value

Named vector of steady-state values for the declared variables.

Examples

steady_state(qpm_template("bkl"))

Summarise an estimate

Description

Returns the posterior summary as a data frame — prior, mode, mean, standard deviation, credible interval, R-hat and effective sample size — so it can be used programmatically rather than only read.

Usage

## S3 method for class 'qpm_estimate'
summary(object, level = 0.9, ...)

Arguments

Value

A data frame, one row per estimated parameter.

Examples

m <- qpm_model(variables = vars(x = "x"), shocks = shocks(e),
               equations = eqs(x ~ rho * x[-1] + e),
               params = list(rho = 0.5))
obs <- simulate(qpm_solve(qpm_calibrate(m, rho = 0.8)), nsim = 120, seed = 1)
est <- qpm_estimate(m, obs, priors(rho = beta(0.5, 0.2)),
                    iter = 300, chains = 1, seed = 2, verbose = FALSE)
summary(est)

Summarise a filtration

Description

Summarise a filtration

Usage

## S3 method for class 'qpm_filtration'
summary(object, ...)

Arguments

Value

A data frame with, for each model variable, whether it was observed and the mean, standard deviation and range of its smoothed path, plus the mean estimation standard error for latent states.

Examples

sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 40, seed = 1, burn = 20)
summary(qpm_filter(sol, obs[, c("period", "pi", "i", "q")]))

Declare a model variable with a label and unit

Description

Used inside vars() to attach documentation to a variable. Units are stored and displayed; unit algebra (automatic consistency checking inside equations) is on the roadmap.

Usage

var(label = "", unit = "", ...)

Arguments

Details

When given numeric data instead of a character label, var() delegates to stats::var(), so attaching qpmR does not break variance computations.

Value

An object of class qpm_var, or the result of stats::var().

Examples

vars(y_gap = var("Output gap", unit = "pp"))
var(rnorm(10))  # still the sample variance

Declare the endogenous variables of a model

Description

Declare the endogenous variables of a model

Usage

vars(...)

Arguments

Value

A data frame with columns name, label, unit.

Examples

vars(
  y_gap = var("Output gap", unit = "pp"),
  pi    = "CPI inflation, QoQ annualised"
)

Posterior covariance and credible intervals

Description

Posterior covariance and credible intervals

Usage

## S3 method for class 'qpm_estimate'
vcov(object, ...)

## S3 method for class 'qpm_estimate'
confint(object, parm = NULL, level = 0.9, ...)

Arguments

Value

vcov() returns the posterior covariance matrix of the estimated parameters; confint() returns equal-tailed posterior credible intervals (posterior quantiles, not asymptotic intervals).

Examples

m <- qpm_model(variables = vars(x = "x"), shocks = shocks(e),
               equations = eqs(x ~ rho * x[-1] + e),
               params = list(rho = 0.5))
obs <- simulate(qpm_solve(qpm_calibrate(m, rho = 0.8)), nsim = 120, seed = 1)
est <- qpm_estimate(m, obs, priors(rho = beta(0.5, 0.2)),
                    iter = 300, chains = 1, seed = 2, verbose = FALSE)
vcov(est)
confint(est)

Verify that an archived round still reproduces

Description

Re-runs a saved forecast round from its own contents — model, calibration, data vintage, conditions and judgment — and checks that the published numbers come back. This is the audit an institution needs and that a folder of scripts cannot provide: a round that no longer reproduces is a finding, not a mystery.

Usage

verify_round(round, store = "rounds", tol = 1e-06)

Arguments

Details

When the round is loaded from a store, the human-readable CSV sidecars are checked against the object too, so a hand-edited audit trail is detected.

Value

An object of class qpm_verification: ⁠$ok⁠, the largest deviation, the worst variables, sidecar checks, and the qpmR versions involved.

Examples

m <- qpm_template("bkl")
obs <- simulate(qpm_solve(m), nsim = 40, seed = 1, burn = 20)
obs$period <- next_quarters("2016-Q1", 40)
r <- qpm_round("demo", m, obs[, c("period", "pi", "i", "q")], horizon = 8)
verify_round(r)

Export a model to a Dynare .mod file

Description

Writes the model as Dynare source so that anyone can re-solve it in Dynare and compare. This is deliberate: qpmR's solver is pinned in its own tests to analytic solutions, but "does it agree with Dynare?" is the first question a modeller asks, and the answer should be cheap to check rather than something to take on trust.

Usage

write_dynare(model, file = NULL, irf = 40, order = 1, extra = NULL)

Arguments

Details

The original equations are exported, not qpmR's internal first-order system: Dynare creates its own auxiliary variables for long leads and lags, so the two implementations agree only if both handle them correctly. Expectation wrappers are dropped (E(pi[+1]) becomes pi(+1)), since leads in Dynare are already model-consistent expectations.

Value

The file path invisibly, or the Dynare source as a character vector when file is NULL.

Examples

src <- write_dynare(qpm_template("bkl"), file = NULL)
cat(head(src, 15), sep = "\n")