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
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Model VECM
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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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GDP HPA FX Y
0.514662421 0.635997077 1.37802145 1.773342598
0.936722 3.127683176 1.391916535 3.709809052
0.101482324 1.270555421 0.831157511 0.226267793
0.017548634 2.456061547 1.003945759 9.510258161
0.236462416 0.988324147 0.223682679 5.026671536
0.372005149 2.177631629 0.904226065 4.219235789
0.153915709 4.620341653 0.033410743 3.17396006
0.524887329 1.050861084 0.518201484 7.950098612
0.776616937 0.503349512 0.666089868 3.320938471
0.760074361 3.635853456 0.470220952 6.380945175
0.802986662 1.260738545 0.452674872 1.036040804
0.375145127 0.20035625 1.837306306 6.486871565
0.002568896 3.532359526 0.556752154 8.536594244
0.754309276 3.952381767 0.247402168 8.559081716
0.585966577 4.01463047 1.184382133 0.148121669
0.39767356 1.553753452 0.983129422 5.378373676
0.859898623 4.73191381 0.828795696 3.367809329
0.741376169 4.993350692 1.758051281 5.516460988
0.329240391 3.465836416 1.701655508 1.249497907
0.078661064 3.298298811 0.04575857 5.132921426
0.270971873 0.46627043 1.739487411 4.94697541
0.731072625 0.940642982 0.728747166 7.583041122
0.385038046 3.51048946 0.021866584 7.361148458
0.530760376 1.204422978 0.415530715 1.163503483
0.555323667 4.777712592 1.844184811 8.596644394