PolicyEngine Macro

model 01 — OBR macroeconometric model · obr-macro · UK · hosted

UK fiscal reform, quarter by quarter.

Run selected UK tax and spending scenarios and trace GDP, consumption, and investment over 3–5 years against the March 2026 EFO baseline. Borrowing is not yet returned by the PolicyEngine Macro adapter.

methodology

Transpile, solve, swap the closure.

The pipeline in five steps: the OBR's published EViews equation listing is rewritten in Python, initialised from official data, solved quarter by quarter until the equations agree, anchored so the baseline reproduces the published EFO, and shocked against an identical baseline so the difference is the policy effect.

1

Transpile the published listing

From EViews source to open Python EViews listing dlog(X) X(-1) @elem error-correction terms needs an EViews licence transpiler line by line Python expressions log(X) - log(X[-1]) indexed array reads compiled once per solve no licence needed same equations, identities, and technical relationships — in open code
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From EViews to Python

The OBR publishes its full equation listing, but as EViews source that few can run. The transpiler rewrites every EViews line — dlog(), @elem, lags like X(-1), error-correction terms — into an equivalent Python expression, compiled once per solve.

What comes out

The result is the model itself in open code: the same behavioural equations, identities, and technical relationships the official forecaster relies on, runnable without an EViews licence.

2

Initialise from official data

Where the starting values come from OBR detailed forecast tables March 2026 EFO vendored ONS snapshot 348 series, ships in the package model history: 199 of 261 unprinted endogenous variables matched by ONS code fiscal and financial sub-blocks get real balancing data
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Loading history

History loads from the OBR's detailed forecast tables. Of the 261 endogenous variables the listing referenced but the outlook did not print, 199 had ONS series codes; a vendored 348-series ONS snapshot supplies them.

Why it matters

That snapshot gives the circular fiscal and financial sub-blocks real balancing data rather than guessed starting values.

3

Solve by Gauss–Seidel iteration

Simultaneous within a quarter, sequential across quarters within a quarter cycle through the equations update each in turn repeat to convergence converged Q1 Q2 Q3 … quarters solve sequentially forward 12-quarter anchored solve: ~30–40 s single-threaded; a reform run is two solves
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Simultaneous within a quarter

The equations depend on each other, so each quarter is solved simultaneously: cycle through the equations, update, repeat to convergence. Quarters then solve sequentially forward.

Runtime

A twelve-quarter anchored solve takes roughly 30–40 seconds single-threaded; a reform run is two such solves.

4

Anchor to the published EFO

Add-factors pull the baseline onto the published EFO fit window 2024Q1–2025Q4 add-factors EFO raw fit add-factors hold constant, project to 2027Q4 to 2031Q1
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Add-factors, the OBR's own device

Add-factors — the residuals reconciling each behavioural equation to data — are set so the baseline reproduces the published EFO. This is the OBR's own device, used the OBR's own way.

Forecasting with held add-factors

For forecasting rather than one-off shocks, the emulator fits add-factors over a base window of 2024Q1–2025Q4, holds them constant, and projects to 2027Q4, with exogenous inputs extrapolated (seasonal cash series at their trailing annual mean). Scenario solves run twelve quarters, 2025Q1–2027Q4.

Horizon limits

The anchored horizon extends to 2031Q1, where GDP is reproduced to 0.29%. Anchored unemployment (LFSUR) is unreliable beyond 2027Q4, drifting to 0.9% against the EFO's 4.1% by 2031Q1.

5

Swap the closure, shock, compare

Two identical solves; the difference is the policy effect closure swap GDPM endogenised DINV = residual shocked baseline policy effect shock applied reported result = shocked path − baseline path identical structure isolates the policy from the forecast
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Closure swap

The GDP identity is switched off in the OBR file, so for a shock GDPM is swapped in and inventories (DINV) become the residual — output responds to the policy instead of being fixed.

Shock against an identical baseline

The emulator solves shocked and baseline paths with identical structure and reports the difference, isolating the policy effect from the underlying forecast.