Innovations in stochastic scenarios

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dholland
Posts: 6
Joined: Thu May 07, 2015 7:33 am

Innovations in stochastic scenarios

Postby dholland » Thu Mar 07, 2019 6:17 pm

Hi, I have a question about calibrating the correct innov values for stochastic equations in a model. Is it the estimated standard error of the equation, or of the variable? For example, if I have an equation in a model that is specified as:

dlog(gnq_pcr) = -0.033432218067729 + -0.0719655003020915* (log(gnq_pcr(-1)) - log(gnq_rpdi(-1)) ) + 0.363199702910733 * dlog(gnq_rpdi)

should I calculate the innov value as the standard deviation of

dlog(gnq_pcr)-dlog(gnq_pcr)_hat

or as

gnq_pcr - gnq_pcr_hat ?

Does it depend on the type of add factor that has been declared, or is it always one or the other?
Thanks very much!
Dawn

EViews Gareth
Fe ddaethom, fe welon, fe amcangyfrifon
Posts: 12155
Joined: Tue Sep 16, 2008 5:38 pm

Re: Innovations in stochastic scenarios

Postby EViews Gareth » Fri Mar 08, 2019 8:27 am

It should be on the equation, not the variable.
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pstalder
Posts: 2
Joined: Tue Apr 30, 2019 3:54 am

Stochastic simulation: How EVIews computes confidence bounds in case of text equations

Postby pstalder » Wed May 01, 2019 6:08 am

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)


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