Emerging Needs and Tailored Products 2 doc

... abs(x(1)) ˆ1.5 + .5 *abs(x(1)) 2. 5 + .5 * abs(x (2) ) ˆ1.5 + .5 * abs(x (2) ) 2. 5 - 4 * x(1) - 4 * x (2) - 1’); % Initial guess of solution based on random numbers x0 = randn(1 ,2) ; % Initialization for ... properties, 82 3. Estimation of a Network with Evolutionary Computation 0 100 20 0 300 400 500 600 700 800 900 1000 −4 −3 2 −1 0 1 2 3 0 100 20 0 300 400 500 600 700 800 900 10...

Ngày tải lên: 21/06/2014, 07:20

... State 1 State 2 c i .909 −. 420 φ i,1 .26 5 .21 6 φ i ,2 . 029 . 628 φ i,3 −. 126 −.073 φ i,4 −.110 −.097 σ i .816 1.01 w i .118 .28 6 5.8 Conclusion 143 0 10 20 30 40 50 60 70 80 90 100 −0 .2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0 .2 0 .25 Linear Network ... states in the economy, S 1 and S 2 : x t = c c + p  i−1 φ 1,i x t−i + ε 1,i ,ε 1 ˜φ(0,σ 2 1 ), if S = S 1 = c 2 + p  i−...

Ngày tải lên: 21/06/2014, 07:20

... Costs Mean 0.300 −0.060 0. 027 0.086 0 .23 4 0.016 0.0 82 1985 0 .29 4 −0.056 0. 024 0.050 0 .22 6 −0.015 0.0 72 1996 0.300 −0.060 0. 027 0.091 0 .23 5 0. 020 0.084 20 02 0.309 −0.067 0.0 32 0.130 0 .24 4 0.053 0.093 Statistical ... Costs Mean 0.3 12 −0.037 0.093 0.168 0.306 0.055 0.141 1985 0 .29 5 −0.018 0.071 0.1 82 0 .29 2 0.051 0. 123 1996 0. 320 −0.046 0.103 0.161 0.3 12 0.056...

Ngày tải lên: 21/06/2014, 07:20

... form: L t = T  t=1  1 2 σ 2 t exp  − (y t − y t ) 2 2σ 2 t  (2. 10) y t = α +  βσ t (2. 11)  t = y t − y t (2. 12) σ 2 t =  δ 0 +  δ 1 σ 2 t−1 +  δ 2  2 t−1 (2. 13) where the symbols ... σ 2 0 and  2 t−1 : 4 Max {α,  β,  δ 0 ,  δ 1 ,  δ 2 } T  t=1 ln(L t )= T  t=1  −.5 ln (2 ) − .5 ln(σ t ) − .5  (y t − y t ) 2 σ 2 t  (2....

Ngày tải lên: 21/06/2014, 09:20

... regime switching models, 1 32 134 out-of-sample predictions, 3 output gap, 169–170, 184–185 output neurons, 21 22 P parallel processing, 21 22 parallel processing advantage, 22 parametric models, 20 Pesaran-Timmerman ... in-sample bias, 101–1 02 for out-of-sample performance, 20 2, 20 4 0.6 32 bootstrap test, 101–1 02, 20 2, 20 4 bounded rationality assumption, 7 Brock-Deckert-S...

Ngày tải lên: 21/06/2014, 09:20

... normal random variable with mean L(µδt − 1 2 σ 2 δt) =(µ − 1 2 σ 2 )t and variance Lσ 2 δt = σ 2 t, that is, approximately, log  S(t) S 0  ∼ N  (µ − 1 2 σ 2 )t,σ 2 t  . (6.7) 5.5 Notes and references ... the power δt 3 /2 or higher. Exercise 6.4 asks you to show that E  µδt + σ √ δtY i − 1 2 σ 2 δtY 2 i  = µδt − 1 2 σ 2 δt (6.5) and var  µδt + σ √ δtY...

Ngày tải lên: 21/06/2014, 09:20

... d 1 and d 2 in (8 .20 ) and (8 .21 ) simplify to d 1 = m τ + τ 2 and d 2 = m τ − τ 2 , (11.1) and, from (8.19) and (8 .24 ), the re-scaled call and put values become c(m,τ)= N(d 1 ) − e −m N(d 2 ) ... as W(S, t) = e −r(T−t)  ∞ 0 (x) xσ √ 2 √ T − t exp    −  log x − log S − (µ − 1 2 σ 2 )(T − t)  2 2σ 2 (T − t)    dx. ( 12. 4) The values ( 12. 2)...

Ngày tải lên: 21/06/2014, 09:20

... 2. What Are Neural Networks? 2. Compute the pooled variance,  Σ=  n 1 −1 n 1 +n 2 2   Σ 1 +  n 2 −1 n 1 +n 2 2   Σ 2 , where n 1 ,n 2 represent the population sizes in groups 1 and 2. 3. ... The transition function Ψ, 44 2. What Are Neural Networks? x1 x2 x3 x4 Inputs x2 x4 x1 x3 Inputs c11 c 22 c21 c 12 H-Units FIGURE 2. 12. Neural principal components 2. 6 .2...

Ngày tải lên: 21/06/2014, 07:20