> 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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