If you want to continue this discussion, I think you need to take it offlist, as it seems to be primarily about methodology, not R programming.
Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Jul 13, 2017 at 4:14 AM, Berend Hasselman <b...@xs4all.nl> wrote: > >> On 13 Jul 2017, at 12:55, Pfaff, Bernhard Dr. >> <bernhard_pf...@fra.invesco.com> wrote: >> >> Who was speaking about non-linear models in the first place??? >> The Klein-Model(s) and pretty much all simultaneous equation models >> encountered in macro-econometrics are linear > > That's really not true. Klein model is linear but Oseibonsu did not say that > explicitly. > "Klein like" can just mean same size or same variables. > >> and/or can contain linear approximations to non-linear relationships, e.g., >> production functions of the Cobb-Douglas type. >> > > One can indeed sometimes approximate without too much harm. But not always. > > Berend > >> Best, >> Bernhard >> >> -----Ursprüngliche Nachricht----- >> Von: Berend Hasselman [mailto:b...@xs4all.nl] >> Gesendet: Donnerstag, 13. Juli 2017 10:53 >> An: OseiBonsu, Frances >> Cc: Pfaff, Bernhard Dr.; r-help@r-project.org >> Betreff: [EXT] Re: [R] Question on Simultaneous Equations & Forecasting >> >> Frances, >> >> I would not advise Gauss-Seidel for non linear models. Can be quite tricky, >> slow and diverge. >> >> You can write your model as a non linear system of equations and use one of >> the nonlinear solvers. >> See the section "Root Finding" in the task view NumericalMathematics >> suggesting three packages (BB, nleqslv and ktsolve). These package are >> certainly able to handle medium sized models. >> (https://cran.r-project.org/web/views/NumericalMathematics.html) >> >> Write a function with the system of equations with each equation written as >> >> y[..] <- lefthandside - (righthandside) >> >> You can then include identities naturally. >> >> You would have to make the model dynamic but that shouldn't be too difficult >> using vector indexing. >> >> Berend Hasselman >> >>> On 13 Jul 2017, at 10:06, Pfaff, Bernhard Dr. >>> <bernhard_pf...@fra.invesco.com> wrote: >>> >>> Hi Frances, >>> >>> I have not touched the system.fit package for quite some time, but to solve >>> your problem the following two pointers might be helpful: >>> >>> 1) Recast your model in the revised form, i.e., include your identity >>> directly into your reaction functions, if possible. >>> 2) For solving your model, you can employ the Gauß-Seidel method (see >>> https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method). >>> This has not only the advantage of generating forecasts, in terms of your >>> exogenous variables, but you can also compute 'dynamic ex post' forecasts. >>> This is probably the most powerful testing for dynamic simultaneous >>> equation systems, given that you provide only your predetermined variables >>> as starting values and then apply the Gauss-Seidel method (recursively) >>> in-sample. The progressions of your endogenous variables should then not >>> depart too much from your observed in-sample endogenous variables, i.e., >>> you are assessing the stability of your model. Because forecast-errors >>> cumulate over time in a dynamic ex-post forecast, this is a rather good and >>> stringent model-test. >>> >>> Incidentally, when you use simultaneous equation models on a larger scale >>> (say, between 200-300 equations, like medium-sized macroeconomic models), >>> the only route to go for, is by estimating your reaction equations >>> separately and then putting all your pieces - including identities and/or >>> technical equations - together in a format suitable for applying the >>> Gauss-Seidel method. Hence, forget about 2SLS or 3SLS and Haavelmo-bias. >>> >>> Best wishes, >>> Bernhard >>> >>> -----Ursprüngliche Nachricht----- >>> Von: R-help [mailto:r-help-boun...@r-project.org] Im Auftrag von >>> OseiBonsu, Frances >>> Gesendet: Mittwoch, 12. Juli 2017 22:36 >>> An: r-help@r-project.org >>> Betreff: [EXT] [R] Question on Simultaneous Equations & Forecasting >>> >>> Hello, >>> >>> I have estimated a simultaneous equation model (similar to Klein's model) >>> in R using the system.fit package. >>> >>> I have an identity equation, along with three other equations. Do you know >>> how to explicitly identify the identity equation in R? >>> >>> I am also trying to forecast the dependent variables in the simultaneous >>> equation model, while incorporating the identity equation in the forecasts. >>> Is there a way to do this in R? >>> >>> The only way that I have been able to forecast the dependent variables has >>> been by getting the predictions of each variable, converting them to time >>> series uni-variables, and forecasting each variable individually. >>> >>> Any help would be appreciated. >>> >>> Best Regards, >>> >>> Frances Osei-Bonsu >>> Summer Analyst, Research and Strategy >>> LaSalle Investment Management >>> 333 West Wacker Drive, Suite 2300, Chicago IL 60606 Email >>> frances.oseibo...@lasalle.com<mailto:frances.oseibo...@lasalle.com> >>> Tel +1 312 897 4024 >>> lasalle.com<http://www.lasalle.com/> >>> >>> >>> >>> This email is for the use of the intended recipient(s) only. 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