dfev: Decompositions of Forecast Error Variance (DFEV) for...
In MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
Computes the m dimensional decomposition of forecast error variance
(DFEV) for a VAR, BVAR, and BSVAR models. User can specify the
decomposition of the contemporaneous innovations.
Usage
1
Arguments
varobj
VAR/BVAR/BSVAR object created from fitting a
VAR/BVAR/BSVAR model using szbvar, szbsvar, or
reduced.form.var.
A0
Decomposition of the contemporaneous error covariance
matrix. Default is to use the (lower) Cholesky decomposition of the
residual error covariance matrix for VAR and BVAR models, or the
inverse of A(0) in B-SVAR models.
k
Number of periods over which to compute the deccomposition.
Details
The decomposition of the forecast error variance (DFEV) provides a
measure of the relationship among forecast errors or impact of shocks
to a VAR/BVAR/BSVAR system. It is computed by finding the moving average
representation (MAR) of the VAR/BVAR/BSVAR model and then tracing out the path of
innovations through the system. For each of the M innovations in a
VAR/BVAR/BSVAR, the amount of the variance in these forecast errors or
innovations is computed and returned in a table. The table can be
accessed via the print.dfev and summary.dfev
functions.
Value
Returns a list with
errors
M x M x K of the percentage of the innovations in
variable i explained by the other M variables.
std.err
M x k dimension matrix of the forecast standard errors.
names
Variable names
Note
The interpretation of the DFEV depends on the decomposition of
the contemporaneous residuals. In the default case of a Cholesky
decomposition, this means that the ordering of the variables in the
decomposition determines the effect of each innovation on the
subsequent DFEVs. For high correlated series, this will mean that the
DFEV is not very robust to the ordering.
Author(s)
Patrick T. Brandt
References
Brandt, Patrick T. and John T. Williams. Multiple Time
Series Models. Thousand Oaks, CA; Sage Press.
See Also
See also print.dfev and
summary.dfev for a nicely formatted tables
and an output example
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
data(IsraelPalestineConflict)
varnames <- colnames(IsraelPalestineConflict)
fitted.BVAR <- szbvar(IsraelPalestineConflict, p=6, z=NULL,
lambda0=0.6, lambda1=0.1,
lambda3=2, lambda4=0.25, lambda5=0, mu5=0,
mu6=0, nu=3, qm=4, prior=0,
posterior.fit=FALSE)
A0 <- t(chol(fitted.BVAR$mean.S))
dat.dfev <- dfev(fitted.BVAR, A0, 24)
print(dat.dfev)
summary(dat.dfev)
Example output
##
## MSBVAR Package v.0.9-2
## Build date: Sun Nov 26 09:42:00 2017
## Copyright (C) 2005-2017, Patrick T. Brandt
## Written by Patrick T. Brandt
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0351179, SES-0351205, SES-0540816, and SES-0921051)
##
Decomposition of Forecast Errors for a Shock to i2p
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 69.91837 92.45011 7.549894
[2,] 85.19947 91.45585 8.544145
[3,] 93.29141 90.50129 9.498709
[4,] 98.48954 89.75914 10.240857
[5,] 102.12060 89.23478 10.765221
[6,] 104.74257 88.85975 11.140247
[7,] 106.67557 88.59042 11.409584
[8,] 108.10783 88.39666 11.603343
[9,] 109.17458 88.25605 11.743948
[10,] 109.97209 88.15316 11.846837
[11,] 110.56986 88.07735 11.922649
[12,] 111.01871 88.02117 11.978826
[13,] 111.35618 87.97937 12.020634
[14,] 111.61013 87.94815 12.051853
[15,] 111.80137 87.92478 12.075225
[16,] 111.94546 87.90724 12.092755
[17,] 112.05405 87.89408 12.105924
[18,] 112.13593 87.88417 12.115826
[19,] 112.19766 87.87672 12.123279
[20,] 112.24423 87.87111 12.128892
[21,] 112.27935 87.86688 12.133121
[22,] 112.30584 87.86369 12.136309
[23,] 112.32583 87.86129 12.138712
[24,] 112.34091 87.85948 12.140524
-------------------------------------------------------------
Decomposition of Forecast Errors for a Shock to p2i
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 28.01374 0.000000 100.00000
[2,] 32.36424 4.479604 95.52040
[3,] 35.52102 12.797824 87.20218
[4,] 38.09995 20.267312 79.73269
[5,] 40.11753 25.766405 74.23360
[6,] 41.64429 29.597448 70.40255
[7,] 42.79027 32.267735 67.73226
[8,] 43.64364 34.141402 65.85860
[9,] 44.27964 35.474853 64.52515
[10,] 44.75463 36.436237 63.56376
[11,] 45.11011 37.136797 62.86320
[12,] 45.37663 37.651580 62.34842
[13,] 45.57676 38.032300 61.96770
[14,] 45.72720 38.315258 61.68474
[15,] 45.84040 38.526341 61.47366
[16,] 45.92563 38.684251 61.31575
[17,] 45.98984 38.802634 61.19737
[18,] 46.03823 38.891525 61.10848
[19,] 46.07471 38.958352 61.04165
[20,] 46.10222 39.008637 60.99136
[21,] 46.12297 39.046500 60.95350
[22,] 46.13861 39.075025 60.92497
[23,] 46.15042 39.096524 60.90348
[24,] 46.15932 39.112731 60.88727
-------------------------------------------------------------
Decomposition of Forecast Errors for a Shock to i2p
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 69.91837 92.45011 7.549894
[2,] 85.19947 91.45585 8.544145
[3,] 93.29141 90.50129 9.498709
[4,] 98.48954 89.75914 10.240857
[5,] 102.12060 89.23478 10.765221
[6,] 104.74257 88.85975 11.140247
[7,] 106.67557 88.59042 11.409584
[8,] 108.10783 88.39666 11.603343
[9,] 109.17458 88.25605 11.743948
[10,] 109.97209 88.15316 11.846837
[11,] 110.56986 88.07735 11.922649
[12,] 111.01871 88.02117 11.978826
[13,] 111.35618 87.97937 12.020634
[14,] 111.61013 87.94815 12.051853
[15,] 111.80137 87.92478 12.075225
[16,] 111.94546 87.90724 12.092755
[17,] 112.05405 87.89408 12.105924
[18,] 112.13593 87.88417 12.115826
[19,] 112.19766 87.87672 12.123279
[20,] 112.24423 87.87111 12.128892
[21,] 112.27935 87.86688 12.133121
[22,] 112.30584 87.86369 12.136309
[23,] 112.32583 87.86129 12.138712
[24,] 112.34091 87.85948 12.140524
-------------------------------------------------------------
Decomposition of Forecast Errors for a Shock to p2i
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 28.01374 0.000000 100.00000
[2,] 32.36424 4.479604 95.52040
[3,] 35.52102 12.797824 87.20218
[4,] 38.09995 20.267312 79.73269
[5,] 40.11753 25.766405 74.23360
[6,] 41.64429 29.597448 70.40255
[7,] 42.79027 32.267735 67.73226
[8,] 43.64364 34.141402 65.85860
[9,] 44.27964 35.474853 64.52515
[10,] 44.75463 36.436237 63.56376
[11,] 45.11011 37.136797 62.86320
[12,] 45.37663 37.651580 62.34842
[13,] 45.57676 38.032300 61.96770
[14,] 45.72720 38.315258 61.68474
[15,] 45.84040 38.526341 61.47366
[16,] 45.92563 38.684251 61.31575
[17,] 45.98984 38.802634 61.19737
[18,] 46.03823 38.891525 61.10848
[19,] 46.07471 38.958352 61.04165
[20,] 46.10222 39.008637 60.99136
[21,] 46.12297 39.046500 60.95350
[22,] 46.13861 39.075025 60.92497
[23,] 46.15042 39.096524 60.90348
[24,] 46.15932 39.112731 60.88727
-------------------------------------------------------------
MSBVAR documentation built on May 30, 2017, 1:23 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
Computes the m dimensional decomposition of forecast error variance (DFEV) for a VAR, BVAR, and BSVAR models. User can specify the decomposition of the contemporaneous innovations.
Usage
1 |
Arguments
varobj |
VAR/BVAR/BSVAR object created from fitting a
VAR/BVAR/BSVAR model using |
A0 |
Decomposition of the contemporaneous error covariance matrix. Default is to use the (lower) Cholesky decomposition of the residual error covariance matrix for VAR and BVAR models, or the inverse of A(0) in B-SVAR models. |
k |
Number of periods over which to compute the deccomposition. |
Details
The decomposition of the forecast error variance (DFEV) provides a
measure of the relationship among forecast errors or impact of shocks
to a VAR/BVAR/BSVAR system. It is computed by finding the moving average
representation (MAR) of the VAR/BVAR/BSVAR model and then tracing out the path of
innovations through the system. For each of the M innovations in a
VAR/BVAR/BSVAR, the amount of the variance in these forecast errors or
innovations is computed and returned in a table. The table can be
accessed via the print.dfev and summary.dfev
functions.
Value
Returns a list with
errors |
M x M x K of the percentage of the innovations in variable i explained by the other M variables. |
std.err |
M x k dimension matrix of the forecast standard errors. |
names |
Variable names |
Note
The interpretation of the DFEV depends on the decomposition of the contemporaneous residuals. In the default case of a Cholesky decomposition, this means that the ordering of the variables in the decomposition determines the effect of each innovation on the subsequent DFEVs. For high correlated series, this will mean that the DFEV is not very robust to the ordering.
Author(s)
Patrick T. Brandt
References
Brandt, Patrick T. and John T. Williams. Multiple Time Series Models. Thousand Oaks, CA; Sage Press.
See Also
See also print.dfev and
summary.dfev for a nicely formatted tables
and an output example
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(IsraelPalestineConflict)
varnames <- colnames(IsraelPalestineConflict)
fitted.BVAR <- szbvar(IsraelPalestineConflict, p=6, z=NULL,
lambda0=0.6, lambda1=0.1,
lambda3=2, lambda4=0.25, lambda5=0, mu5=0,
mu6=0, nu=3, qm=4, prior=0,
posterior.fit=FALSE)
A0 <- t(chol(fitted.BVAR$mean.S))
dat.dfev <- dfev(fitted.BVAR, A0, 24)
print(dat.dfev)
summary(dat.dfev)
|
Example output
##
## MSBVAR Package v.0.9-2
## Build date: Sun Nov 26 09:42:00 2017
## Copyright (C) 2005-2017, Patrick T. Brandt
## Written by Patrick T. Brandt
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0351179, SES-0351205, SES-0540816, and SES-0921051)
##
Decomposition of Forecast Errors for a Shock to i2p
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 69.91837 92.45011 7.549894
[2,] 85.19947 91.45585 8.544145
[3,] 93.29141 90.50129 9.498709
[4,] 98.48954 89.75914 10.240857
[5,] 102.12060 89.23478 10.765221
[6,] 104.74257 88.85975 11.140247
[7,] 106.67557 88.59042 11.409584
[8,] 108.10783 88.39666 11.603343
[9,] 109.17458 88.25605 11.743948
[10,] 109.97209 88.15316 11.846837
[11,] 110.56986 88.07735 11.922649
[12,] 111.01871 88.02117 11.978826
[13,] 111.35618 87.97937 12.020634
[14,] 111.61013 87.94815 12.051853
[15,] 111.80137 87.92478 12.075225
[16,] 111.94546 87.90724 12.092755
[17,] 112.05405 87.89408 12.105924
[18,] 112.13593 87.88417 12.115826
[19,] 112.19766 87.87672 12.123279
[20,] 112.24423 87.87111 12.128892
[21,] 112.27935 87.86688 12.133121
[22,] 112.30584 87.86369 12.136309
[23,] 112.32583 87.86129 12.138712
[24,] 112.34091 87.85948 12.140524
-------------------------------------------------------------
Decomposition of Forecast Errors for a Shock to p2i
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 28.01374 0.000000 100.00000
[2,] 32.36424 4.479604 95.52040
[3,] 35.52102 12.797824 87.20218
[4,] 38.09995 20.267312 79.73269
[5,] 40.11753 25.766405 74.23360
[6,] 41.64429 29.597448 70.40255
[7,] 42.79027 32.267735 67.73226
[8,] 43.64364 34.141402 65.85860
[9,] 44.27964 35.474853 64.52515
[10,] 44.75463 36.436237 63.56376
[11,] 45.11011 37.136797 62.86320
[12,] 45.37663 37.651580 62.34842
[13,] 45.57676 38.032300 61.96770
[14,] 45.72720 38.315258 61.68474
[15,] 45.84040 38.526341 61.47366
[16,] 45.92563 38.684251 61.31575
[17,] 45.98984 38.802634 61.19737
[18,] 46.03823 38.891525 61.10848
[19,] 46.07471 38.958352 61.04165
[20,] 46.10222 39.008637 60.99136
[21,] 46.12297 39.046500 60.95350
[22,] 46.13861 39.075025 60.92497
[23,] 46.15042 39.096524 60.90348
[24,] 46.15932 39.112731 60.88727
-------------------------------------------------------------
Decomposition of Forecast Errors for a Shock to i2p
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 69.91837 92.45011 7.549894
[2,] 85.19947 91.45585 8.544145
[3,] 93.29141 90.50129 9.498709
[4,] 98.48954 89.75914 10.240857
[5,] 102.12060 89.23478 10.765221
[6,] 104.74257 88.85975 11.140247
[7,] 106.67557 88.59042 11.409584
[8,] 108.10783 88.39666 11.603343
[9,] 109.17458 88.25605 11.743948
[10,] 109.97209 88.15316 11.846837
[11,] 110.56986 88.07735 11.922649
[12,] 111.01871 88.02117 11.978826
[13,] 111.35618 87.97937 12.020634
[14,] 111.61013 87.94815 12.051853
[15,] 111.80137 87.92478 12.075225
[16,] 111.94546 87.90724 12.092755
[17,] 112.05405 87.89408 12.105924
[18,] 112.13593 87.88417 12.115826
[19,] 112.19766 87.87672 12.123279
[20,] 112.24423 87.87111 12.128892
[21,] 112.27935 87.86688 12.133121
[22,] 112.30584 87.86369 12.136309
[23,] 112.32583 87.86129 12.138712
[24,] 112.34091 87.85948 12.140524
-------------------------------------------------------------
Decomposition of Forecast Errors for a Shock to p2i
-------------------------------------------------------------
Std. Error i2p p2i
[1,] 28.01374 0.000000 100.00000
[2,] 32.36424 4.479604 95.52040
[3,] 35.52102 12.797824 87.20218
[4,] 38.09995 20.267312 79.73269
[5,] 40.11753 25.766405 74.23360
[6,] 41.64429 29.597448 70.40255
[7,] 42.79027 32.267735 67.73226
[8,] 43.64364 34.141402 65.85860
[9,] 44.27964 35.474853 64.52515
[10,] 44.75463 36.436237 63.56376
[11,] 45.11011 37.136797 62.86320
[12,] 45.37663 37.651580 62.34842
[13,] 45.57676 38.032300 61.96770
[14,] 45.72720 38.315258 61.68474
[15,] 45.84040 38.526341 61.47366
[16,] 45.92563 38.684251 61.31575
[17,] 45.98984 38.802634 61.19737
[18,] 46.03823 38.891525 61.10848
[19,] 46.07471 38.958352 61.04165
[20,] 46.10222 39.008637 60.99136
[21,] 46.12297 39.046500 60.95350
[22,] 46.13861 39.075025 60.92497
[23,] 46.15042 39.096524 60.90348
[24,] 46.15932 39.112731 60.88727
-------------------------------------------------------------
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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