Dear Renault

If you provide us with your data, you could then also give the code of the
analysis, would help us to help you :-)

answers below
2009/12/23 <markleeds@verizon.net>

> Hi: I don't know the answers to your second and third questions but Schwert
> has a heuristic for choosing the correct number of
> lags in the ECM.  It's in Eric Zivot's S+ Finmetrics book if you have that.
> There is no "correct way" really and there are probably other ways but I've
> seen that one used in the literature. If you don't have the book and can't
> find it on the internet, let
> me know and I'll look it up in Eric's book assuming it's in my apt and not
> in other places that it might be.
>
> if you do "Schwert lags vars" in google, i bet something will come up.
>
>
> On Dec 23, 2009, *Gautier RENAULT* <renault.gautier@gmail.com> wrote:
>
> Hi R-users,
>
> I try to deal with cointegration in R and estimate an Error Correction
> Model (ECM) in a bivariate case in which I consider two variables:
>
> ·         Pt: index house prices in France from 1996:Q1 to 2009:Q3 (log
> dependent variable)
>
> ·         Xt: amount that households can borrow (log explanatory
> variable). Xt captures the role of credit, income and interest rate as
> drivers of the french housing demand.
>
> Data are in “fr-demand-house.csv”.
>
> The final aim is to estimate the long run relationship between houses
> prices (Pt) and the credit (Xt) in France :
>
> Pt = α + ϕ Xt
>
> I wish to estimate a general specification of the ECM as follows :
>
>
>
> ΔPt=λ(Pt-1-α - ϕ Xt-1)+Σ(ΔXt-i)+Σ(ΔPt-i)
>
>
>
> Note that you are including an intercept in the ECM. It is not possible
with VAR(), but a (experimental) possibility is in package tsDyn:
library(tsDyn)
lineVar(data, lag=2, LRinclude="const",model="VECM")

But this works only with 2 step ols estimator (Engel Garnger) and Not
Johansen one.

Note actually that this is pretty tricky, the link between constant in the
LR and restricted VECM, Enders (applied time seriies eco) discusses it.

> First, following the methodology presented in the book of B. Pfaff I
> bought, I already concluded that Pt and Xt are cointegrated  I(1) with
> ur.df (ADF test ) and ca.po (Phillips-Ouliaris Method) functions.
>
> Second, how can I do :
>
> 1.       to choose optimal number of lag in this general specification for
> the two cointegrated variables Pt (ΔPt-i) and Xt (ΔXt-i)?
>
> Why don't you select the number of lags following  the AIC/BIC based on the
VAR? See then VARselect() from package vars

> 2.       to add a dummy variable for the first quarter of 2009
> (dumQ1-2009) to test the collapse of houses prices in France at the
> beginning of 2009 ?
>
>
where? In the ECM or as normal regressor in the VECM? If in the ECM, again
you could set it in tsDyn as:
dummy<- c(rep(0, 20), 1, rep(0, 30))
lineVar(data, lag=2, LRinclude=dummy, model="VECM")

If in the VECM, use arg "exogen" in the VAR maybe...

Note that if you are testing for structural break within the LR, I had
implemented the Gregory Hansen test in R, but not ported further, bt I can
send you eventually rwa code if you want.

Hope this helps

> Can anyone help?
>
> thanking you in advance,
>
>
>
> Gautier RENAULT
>
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