Dear Madam, Dear Sir

I have a rather complicated nonlinear macroeconomic model for which some blocks are estimated as system objects with FIML (supply block, monetary block, housing block) and several remaining equations are estimated as single equations. All equations are then combined as text into a model object. Stochastic model simulations and forecasts are technically computed without any problems - with the expected message that the equations are treated as non-stochastic (see below). Obviously, as EViews receives no information about the coefficient estimates in case of text equations, the simulations cannot account for coefficient uncertainty. Nevertheless, confidence bounds for all endogenous variables are computed. These must be based somehow on the residual distributions of the individual equations (the estimated standard errors of the equations). Now my question: Where does EViews find this information in case of text equations? Are the residuals and the corresponding standard errors of the equations computed within the model object? Probably yes. More generally speaking, a more transparent technical outline of what happens exactly in stochastic simulations would be welcome.

Many thanks for your answer,

Peter (pestal@bluewin.ch)

Model: SIMMOD

Date: 05/01/19 Time: 11:55

Sample: 2019Q1 2025Q4

Solve Options:

Dynamic-Stochastic Simulation

Solver: Newton

Max iterations = 5000, Convergence = 1e-06

Requested repetitions = 10000, Allow up to 25 percent failures

Solution does not account for coefficient uncertainty in linked equations

Track endogenous: mean, standard deviation, 75% confidence interval

Calculating Innovation Covariance Matrix

Sample: 1990Q1 2018Q4 (this is the estimation period)

Insufficient IME innovations - Equation treated as non-stochastic

Insufficient YC innovations - Equation treated as non-stochastic

Insufficient LCAP innovations - Equation treated as non-stochastic

Insufficient LSUP innovations - Equation treated as non-stochastic

Insufficient LFPOT innovations - Equation treated as non-stochastic

Insufficient POP innovations - Equation treated as non-stochastic

Insufficient UROFF innovations - Equation treated as non-stochastic

Insufficient WAGE innovations - Equation treated as non-stochastic

Insufficient PGDP innovations - Equation treated as non-stochastic

Insufficient WINCI innovations - Equation treated as non-stochastic

Insufficient PCONSP innovations - Equation treated as non-stochastic

Insufficient PCI innovations - Equation treated as non-stochastic

Insufficient PCONSG innovations - Equation treated as non-stochastic

Insufficient PIME innovations - Equation treated as non-stochastic

Insufficient PIMTOT innovations - Equation treated as non-stochastic

Insufficient POIL innovations - Equation treated as non-stochastic

Insufficient PHR innovations - Equation treated as non-stochastic

Insufficient PICNSTR innovations - Equation treated as non-stochastic

Insufficient PEXTOT innovations - Equation treated as non-stochastic

Insufficient YPRIMB innovations - Equation treated as non-stochastic

Insufficient CONSP innovations - Equation treated as non-stochastic

Insufficient CONSG innovations - Equation treated as non-stochastic

Insufficient ICBUS innovations - Equation treated as non-stochastic

Insufficient PHOUSE innovations - Equation treated as non-stochastic

Insufficient IHOUSE innovations - Equation treated as non-stochastic

Insufficient EXTOT innovations - Equation treated as non-stochastic

Insufficient IMTOT innovations - Equation treated as non-stochastic

Insufficient LRATE innovations - Equation treated as non-stochastic

Insufficient EVEUROFR innovations - Equation treated as non-stochastic

Insufficient MRATE innovations - Equation treated as non-stochastic

Insufficient PIMCC innovations - Equation treated as non-stochastic

Insufficient PEXCC innovations - Equation treated as non-stochastic

Insufficient EXS innovations - Equation treated as non-stochastic

Insufficient EXCC innovations - Equation treated as non-stochastic

Insufficient IMS innovations - Equation treated as non-stochastic

Insufficient IMCC innovations - Equation treated as non-stochastic

Matrix scaled to equation specified variances

Scenario: Fcst_BASE

Solve begin 11:55:36

Repetitions 1-3200: successful 11:55:45

Repetitions 3201-6400: successful 11:55:53

Repetitions 6401-9600: successful 11:56:02

Repetitions 9601-10000: successful 11:56:03

Solve complete 11:56:03

10000 successful repetitions, 0 failure(s)

## Models with text equations: How EViews computes confidence bounds in stochastic simulations

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