Svein,
I know of no such automated procedure. I think that you would have to
write the
program yourself.
Regards,
Robert
Robert A. Yaffee, Ph.D.
Research Professor
Shirley M. Ehrenkranz
School of Social Work
New York University
home address:
Apt 19-W
2100 Linwood Ave.
Fort Lee, NJ
07024-3171
Phone: 201-242-3824
Fax: 201-242-3825
[email protected]
----- Original Message -----
From: "Svein.Oskar Lauvsnes" <[email protected]>
Date: Friday, December 8, 2006 11:35 am
Subject: Re: SV: Re: st: Time series: VECM with recursive window forecasts
> Robert,
> thank you for a rapid answer. I am fully aware of the optimization
> issues regarding model congruency, of which I will have to make
> some simplifying assumptions for this "exercise". Serial
> correlation should be handled, though.
>
> Seasonality can be controlled by centered seasonal dummies.
>
> A vecm (reduced rank VAR) is expressed in first differences
> (stationary if levels are I(1)) plus a stationary linear
> combination of lagged levels. Then the vecm is stationary. Of
> course, if the variables are not cointegrated, then a VAR in first
> differences is appropriate. The variables in question here are
> cointegrated (rank tests on entire sample and subsamples). Also,
> eigenvalue stability tests shows that the largest eigenvalue is
> fairly stable, justifying the use of only one cointegrating vector
> in the vecm (see also below).
>
> Structural breaks: Chow tests indicate that they are absent for
> this system (and the variables react fairly similarly to stochastic
> shocks, they share the same deterministic trend).
>
> Granger causality: not recommended in its usual form for vars and
> vecm's (see Phillips, 1991)
>
> Exogeneity: long run weak exogeneity is captured by the alpha
> (adjustment) coefficient, meaning that the variable is decided
> outside the system, but still belongs to the cointegrating
> equation. A very small (significant) value of alpha yields a
> "practically" weakly exogenous variable in that the adjustment
> process is very slow. One could also implement a procedure that
> sets alpha to zero if its absolute t-value is below a certain
> level, say 1,5. Some variables are obviously exogenous, such as a
> US interest rate in combination with Norwegian data.
>
> IRF's are not an issue here, only forecasting from a reduced form
> model, and structural models will not be considered. The forecasts
> are compared to single equation models with different restrictions
> to see if the system approach adds quality to the forecasts.
>
> As a start, I intend to estimate vecms with a fixed number of lags
> and cointegrating vectors, disregarding rank tests for each
> subsample. One argument for doing so can be found with
> Johansen/Juselius: the vector with the largest eigenvalue is the
> most useful. Also, information criteria are very often ambigous
> regarding "optimal" lag length, such that in practice one would
> apply general to specific modeling, choosing the most parsimonious
> model (smallest number of lags that yields non-serially correlated
> errors). As for trends, quadratic trends are highly unlikely in
> economic data in general, but there is often a deterministic trend
> in the levels (and in the cointegrating equation). Though, if the
> variables in question share the same deterministic trend, it will
> cancel (see L�tkepohl). Hence, trending behaviour is captured by an
> unrestricted constant in the vecm.
>
> Therefore, I was looking for a procedure that could perform the
> estimations and predictions with the assumptions mentioned above. A
> comparison of models with different lag lengths could then be
> undertaken, and see which information criterium (e.g. AIC, SC, FPE,
> HQ) is closest to the model with the lowest mean squared forecast
> error. Suggestions?
>
>
> >>> [email protected] 08.12.2006 15:58 >>>
> Svein,
> Automation is a tall order here. You first have to transform your
> variables to attain stationarity. Logging may work if you have
> varianceinstability, but then the errors become multiplicative.
> You will have
> to look for seasonality and possibly deasonalize your variables to
> attain stationarity, lest you use deterministic seasonal dummies
> later.
> Then there is the matter of graphing the series and looking for
> structural breaks. Then there is the matter of running the
> stationaritytests. One has to transform the series to stationarity.
> Regraphing
> follows. Then there is the issue of lag determination. Granger
> Causality tests should be run to asertain whether any variables are
> exogenous. You might want to run a trace test to determine the number
> of cointegrating vectors. Modeling the error correction mechanism
> mightbe necessaryat thisjuncture. You might have to identify the
> VECM form,
> determining
> whether deterministic terms--such as drift, linear or even quadratic
> terms--might be in order. There may be parameter restrctions
> required.
> The process would have to be iterated till the model is optimized.
> Thenther is the structural VAR to run with the impulse response
> functions.
> Once all this is done, you might want to forecast, plotting the
> forecasterror variance decomposition. Automation of this process
> more than what
> Stata has done in the VAR and VECM procedures is daunting.
> Good luck,
> Robert
>
>
>
> Robert A. Yaffee, Ph.D.
> Research Professor
> Shirley M. Ehrenkranz
> School of Social Work
> New York University
>
> home address:
> Apt 19-W
> 2100 Linwood Ave.
> Fort Lee, NJ
> 07024-3171
> Phone: 201-242-3824
> Fax: 201-242-3825
> [email protected]
>
> ----- Original Message -----
> From: "Svein.Oskar Lauvsnes" <[email protected]>
> Date: Friday, December 8, 2006 9:36 am
> Subject: SV: Re: st: Time series: VECM with recursive window forecasts
>
> > Robert,
> > thanks for a rapid answer. I agree that in general arima models
> > (perhaps applying genreal to specific modeling) would be more
> > suitable than ols for time series in order to get a "congruent
> > model", i.e. no serial correlation/heterosced, normality in
> > residuals. However, in this case I first intend to compare a
> simple
> > AR(1) model (close to a pure random walk) with an extended model
> > (also single equation) including some macrovariables regarding
> > predictive abilities. For this purpose I might as well use ols,
> > regressing the change in the log of the dependent variable on its
> > 1st lag instead of formulating an AR(1), which would be the same.
> > Also, I also intend to compare my results with those of Rapach et
> > al (2005)
> >
> > The second step in this exercise is to estimate a vecm system,
> and
> > again compare predictive abilities (see e.g. McRae et al, 2002).
> It
> > is in this step that I need some help to automatize the
> estimation
> > and forecasting process. Here too, congruency is not considered,
> I
> > intend to estimate a vecm with a fixed number of lags and
> > cointegrating vectors for each estimation. Of course, I will
> check
> > subsamples to see if they differ greatly regarding these
> > assumptions. When estimating the vecm on the entire sample, an
> > eigenvalue test show that they are fairly stable throughout.
> Also,
> > there are arguments for using only the cointegrating vector with
> > the largest eigenvalue (See Johansen/Juselius).
> >
> > So, comparing predictive ability by increasing the informational
> > content in a parsimonious model is the main topic. What do you
> > think about this? Any programming suggestions would be great.
> >
> > Regards,
> >
> > Svein.
> >
> > >>> [email protected] 08.12.2006 14:57 >>>
> > Sven,
> > Should you not should consider using tssmooth exponential,
> arima, or
> > prais rather than ols reg, unless you have a theoretical reason for
> > showing the defects of
> > not controlling for autocorrelation in the series?
> > Regards,
> > Robert
> >
> >
> > Robert A. Yaffee, Ph.D.
> > Research Professor
> > Shirley M. Ehrenkranz
> > School of Social Work
> > New York University
> >
> > home address:
> > Apt 19-W
> > 2100 Linwood Ave.
> > Fort Lee, NJ
> > 07024-3171
> > Phone: 201-242-3824
> > Fax: 201-242-3825
> > [email protected]
> >
> > ----- Original Message -----
> > From: "Svein.Oskar Lauvsnes" <[email protected]>
> > Date: Friday, December 8, 2006 3:03 am
> > Subject: st: Time series: VECM with recursive window forecasts
> >
> > > Hi,
> > > I am investigating the predictive abilities of macrovariables
> on
> > > stock market returns. So far I have made 1-step ahead
> predictions
> > > from single equation models, keeping the starting point fixed
> and
> > > for each new regression extending the dataset by one
> observation.
> > I
> > > would like to compare the single equation forecasts with
> > forecasts
> > > from a system of equations such as a vector error correction
> > model
> > > and a VAR. I have used the following program for my forecasts:
> > >
> > > gen time = _n
> > > tsset time
> > >
> > >
> > >
> > > capture program drop rforecast
> > > program rforecast, rclass
> > > syntax [if]
> > > regress dose l.dose dnib `if'
> > > summ time if e(sample)
> > > local last = r(max)
> > > local fcast = _b[_cons] + _b[L.dose]*dose[`last']///
> > > + _b[dnib]*nib[`last'+1]
> > >
> > > return scalar forecast = `fcast'
> > > return scalar actual = dose[`last' +1]
> > > end
> > >
> > > rolling actual=r(actual) forecast=r(forecast), recursive ///
> > > window(149) saving(myrolling, replace): rforecast
> > >
> > > use myrolling, clear
> > > list in 1/100
> > >
> > > Hopefully, the program will work on a VECM by substituting the
> > > sentences in bold. How should I modify my program to do rolling
> > > window estimation/forecasting using a VECM? I suppose the
> number
> > of
> > > cointegrating vectors and lags would have to be fixed.
> > >
> > > Sincerely
> > >
> > > Svein Lauvsnes
> > > Bodoe Graduate School of Business, Norway
> > >
> > >
> > >
> > >
> > > *
> > > * For searches and help try:
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> > > * http://www.stata.com/support/statalist/faq
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> > >
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> >
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> >
> *
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>
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>
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