Emerging Needs and Tailored Products 3 pdf
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_5 pdf
... MRS
Model (Structure: 4 Lags, 3 Neurons)
Diagnostic Linear Model (Network Model)
Estimate
R
2
.35 ( .38 )
HQIF 32 91 (32 68)
L-B
∗
.91
M-L
∗
.0009
E-N
∗
.0176
J-B
∗
.36
L-W-G 13
B-D-S∗ .0002
∗
marginal ... model, they do
132 5. Estimating and Forecasting with Artificial Data
0 50 100 150 200 250 30 0 35 0 400 450 500
−6
−4
−2
0
2
4
6
8
0 50 100 150 200 250 30 0 35 0 400 450 500
0...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_2 ppt
... of falling in category 1, 2, or 3 come from the
cumulative probabilities:
p
1,i
=
P
1,i
3
j=1
P
j,i
(2.89)
p
2,i
=
P
2,i
3
j=1
P
j,i
(2.90)
p
3, i
=
P
3
3
j=1
P
j,i
(2.91)
Neural network ... between long- and short-term
rates for government bonds, the expected and unexpected inflation rates,
industrial production growth, and the yield between corporate high and
low-gra...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_3 doc
... properties,
82 3. Estimation of a Network with Evolutionary Computation
0 100 200 30 0 400 500 600 700 800 900 1000
−4
3
−2
−1
0
1
2
3
0 100 200 30 0 400 500 600 700 800 900 1000
0
5
10
15
20
25
x1=randn(1000,1)
y1=sin(x1)
2
... p. 31 5].
As Table 3. 2 shows, we again start with a candidate solution vector,
Ω
0
, and the associated error criterion, Ψ
0
. A shock to the solution ve...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_4 pptx
... ols1.m, wnnest1.m, and
bds.m, available on the webpage of the author.
% Create random regressors, constant term,
% and dependent variable
for i = 1:1000,
randn(’state’,i);
xxx = randn(1000,1);
x1 ... boostrapping
method originally due to Efron (1979, 19 83) and Efron and Tibshirani
(19 93) . This bootstrapping method is different from the . 632 bootstrap
method for in-sample bias. In t...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_5 doc
... MRS
Model (Structure: 4 Lags, 3 Neurons)
Diagnostic Linear Model (Network Model)
Estimate
R
2
.35 ( .38 )
HQIF 32 91 (32 68)
L-B
∗
.91
M-L
∗
.0009
E-N
∗
.0176
J-B
∗
.36
L-W-G 13
B-D-S∗ .0002
∗
marginal ... Model 133
TABLE 5.9. In-Sample Diagnostics: VRS
Model (Structure: 4 Lags, 3 Neurons)
Diagnostic Linear Model (Network Model)
Estimate
R
2
.422 (. 438 )
HQIF 34 84 (34 88)
L-B
∗
....
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_7 doc
... Costs
Mean 0 .30 0 −0.060 0.027 0.086 0. 234 0.016 0.082
1985 0.294 −0.056 0.024 0.050 0.226 −0.015 0.072
1996 0 .30 0 −0.060 0.027 0.091 0. 235 0.020 0.084
2002 0 .30 9 −0.067 0. 032 0. 130 0.244 0.0 53 0.0 93
Statistical ... Index Costs
Mean 0 .31 2 −0. 037 0.0 93 0.168 0 .30 6 0.055 0.141
1985 0.295 −0.018 0.071 0.182 0.292 0.051 0.1 23
1996 0 .32 0 −0.046 0.1 03 0.161 0 .31 2 0.056 0...
New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets_8 pptx
... 23. 500 22 .30 0 21.000 20.000
Std. Dev. 12.906 10.1 83 8.1 23 6.719 5. 238 4 .30 3
Coeff. Var 0.4516 0 .38 88 0 .33 448 0.2927 0.246 0.216
Skewness 0.487 0.590 0.582 0. 536 0.404 0.584
Kurtosis 2.064 2. 235 ... 0.591 0.500 0.428 0 .37 2 0 .30 0 0. 239
Skewness 1.122 1.214 1.2 23 1.191 1.092 0.952
Kurtosis 2.867 3. 114 3. 186 3. 156 3. 0 23 2. 831
Max 66.000 59.000 50.000 44 .30 0 3...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_1 docx
... regression
techniques.
[Dayhoff and DeLeo (2001), p. 1624].
32 2. What Are Neural Networks?
{y} can be decomposed into linear and nonlinear components, and then
we would expect all three sets of coefficients, {β}, {ω}, and ... from the input vector and the center of that unit,
whereas the MLP or feedforward network computes the inner products
of the inputs and the weights for that...
Emerging Needs and Tailored Products for Untapped Markets by Luisa Anderloni, Maria Debora Braga and Emanuele Maria Carluccio_3 pot
... p. 31 5].
As Table 3. 2 shows, we again start with a candidate solution vector,
Ω
0
, and the associated error criterion, Ψ
0
. A shock to the solution vector is
then randomly generated, Ω
1
, and ... the
82 3. Estimation of a Network with Evolutionary Computation
0 100 200 30 0 400 500 600 700 800 900 1000
−4
3
−2
−1
0
1
2
3
0 100 200 30 0 400 500 600 700 800 900 1000
0
5
10
15...
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