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Forecasting using VECM

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Forecasting using VECM

Preetam Pal
Hi,

I have attached the historical dataset (titled data) containing numerical
variables GDP, HPA, FX and Y - I am interested to predict Y given some
future values of GDP, HPA and FX.

   - Some variables are non-statioanry as per adf.test()
   - I wanted to implement a VECM framework for modeling cointegration, so
   I have used *result = VECM(data, lag = 3, r = 1)* , and I get the output
   below showing that cointegration relationship does exist between these 4
   variables:
   - My question is: How do I get predictions of Y given
   externally-generated future values of the other variables (for say,
   upcoming 10 time points), using this result programmatically?

Regards,
Preetam
#############
Model VECM
#############
Full sample size: 25 End sample size: 22
Number of variables: 4 Number of estimated slope parameters 40
AIC 23.84198 BIC 70.75681 SSR 156.5155
Cointegrating vector (estimated by ML):
   GDP      HPA        FX           Y
r1   1 2.171994 -6.823215 -0.07767563


             ECT                 Intercept           GDP -1
Equation GDP 0.0612(0.0436)      0.0141(0.0687)      -0.4268(0.2494)
Equation HPA -0.6368(0.2381)*    0.1858(0.3749)      3.1656(1.3609)*
Equation FX  0.1307(0.0874)      -0.0039(0.1377)     0.1739(0.4997)
Equation Y   -0.0852(0.4261)     0.3219(0.6711)      -5.0248(2.4359).
             HPA -1              FX -1               Y -1
Equation GDP -0.0910(0.0790)     0.1988(0.2261)      0.0413(0.0299)
Equation HPA 0.4891(0.4311)      -2.2140(1.2337).    -0.3206(0.1631).
Equation FX  -0.2108(0.1583)     -0.2536(0.4530)     -0.0303(0.0599)
Equation Y   -0.3686(0.7716)     0.5234(2.2083)      -0.9638(0.2920)**
             GDP -2              HPA -2              FX -2
Equation GDP -0.2892(0.2452)     -0.0622(0.0563)     0.0598(0.1352)
Equation HPA -0.7084(1.3379)     0.1877(0.3069)      -0.2231(0.7377)
Equation FX  -0.1773(0.4913)     -0.0170(0.1127)     -0.2486(0.2709)
Equation Y   -3.8521(2.3948)     -0.4559(0.5494)     1.1239(1.3205)
             Y -2
Equation GDP 0.0411(0.0279)
Equation HPA -0.2447(0.1521)
Equation FX  -0.0102(0.0559)
Equation Y   -0.1696(0.2723)

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Re: Forecasting using VECM

Bert Gunter-2
Searching on "VECM" on rseek.org brought up:

"VECM" on the Rdocumentation site, which clearly states:

"The predict method contains a newdata argument allowing to compute
rolling forecasts."

If that is not what you want, you'll need to explain why not, I think.
If it is, please do such searching on your own in future.

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 Tue, Feb 14, 2017 at 4:18 AM, Preetam Pal <[hidden email]> wrote:

> Hi,
>
> I have attached the historical dataset (titled data) containing numerical
> variables GDP, HPA, FX and Y - I am interested to predict Y given some
> future values of GDP, HPA and FX.
>
>    - Some variables are non-statioanry as per adf.test()
>    - I wanted to implement a VECM framework for modeling cointegration, so
>    I have used *result = VECM(data, lag = 3, r = 1)* , and I get the output
>    below showing that cointegration relationship does exist between these 4
>    variables:
>    - My question is: How do I get predictions of Y given
>    externally-generated future values of the other variables (for say,
>    upcoming 10 time points), using this result programmatically?
>
> Regards,
> Preetam
> #############
> Model VECM
> #############
> Full sample size: 25 End sample size: 22
> Number of variables: 4 Number of estimated slope parameters 40
> AIC 23.84198 BIC 70.75681 SSR 156.5155
> Cointegrating vector (estimated by ML):
>    GDP      HPA        FX           Y
> r1   1 2.171994 -6.823215 -0.07767563
>
>
>              ECT                 Intercept           GDP -1
> Equation GDP 0.0612(0.0436)      0.0141(0.0687)      -0.4268(0.2494)
> Equation HPA -0.6368(0.2381)*    0.1858(0.3749)      3.1656(1.3609)*
> Equation FX  0.1307(0.0874)      -0.0039(0.1377)     0.1739(0.4997)
> Equation Y   -0.0852(0.4261)     0.3219(0.6711)      -5.0248(2.4359).
>              HPA -1              FX -1               Y -1
> Equation GDP -0.0910(0.0790)     0.1988(0.2261)      0.0413(0.0299)
> Equation HPA 0.4891(0.4311)      -2.2140(1.2337).    -0.3206(0.1631).
> Equation FX  -0.2108(0.1583)     -0.2536(0.4530)     -0.0303(0.0599)
> Equation Y   -0.3686(0.7716)     0.5234(2.2083)      -0.9638(0.2920)**
>              GDP -2              HPA -2              FX -2
> Equation GDP -0.2892(0.2452)     -0.0622(0.0563)     0.0598(0.1352)
> Equation HPA -0.7084(1.3379)     0.1877(0.3069)      -0.2231(0.7377)
> Equation FX  -0.1773(0.4913)     -0.0170(0.1127)     -0.2486(0.2709)
> Equation Y   -3.8521(2.3948)     -0.4559(0.5494)     1.1239(1.3205)
>              Y -2
> Equation GDP 0.0411(0.0279)
> Equation HPA -0.2447(0.1521)
> Equation FX  -0.0102(0.0559)
> Equation Y   -0.1696(0.2723)
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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