New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets 2 ppt

... parameters in state 1, and α 2 is the parameter vector in state 2. 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. ... p 2 , p 3 for each observation i: P 1,i = 1 1+e −[x i β 1 +β 10 ] (2. 86) P 2, i = 1 1+e −[x i β 2 +β 20 ] (2. 87) P 3,i = 1 1+e −[x i β 3 +β 30 ] (2. 88) 2. 6 N...

Ngày tải lên: 21/06/2014, 07:20

... bootstrapping the initial training set. As we discuss in Section 4 .2. 8, bootstrapping involves resampling the original training set with replace- ment, and then taking repeated forecasts. Bagging is ... annealing solution, and the local quasi- Newton based on the BFGS algorithm appear, is given by the following sets of commands: % Define simple function z = inline(’.5 * x(1) 2 + .5 *...

Ngày tải lên: 21/06/2014, 07:20

... estimation an expanding window. The sample size, of course, becomes larger as we move forward in time. An alternative to the expanding window is the moving window. In this case, for the first forecast ... Estimation 4 .2. 5 Out-of-Sample Comparison with Nested Models Clark and McCracken (20 01), Corradi and Swanson (20 02) , and Clark and West (20 04) have proposed tests for co...

Ngày tải lên: 21/06/2014, 07:20

... are forecast onevar scmodel new1 .m (for the stochastic 1 32 5. Estimating and Forecasting with Artificial Data 0 50 100 150 20 0 25 0 300 350 400 450 500 −6 −4 2 0 2 4 6 8 0 50 100 150 20 0 25 0 300 ... .27 DM-1 ∗ — .25 DM -2 ∗ — .15 DM-3 ∗ — .22 DM-4 ∗ — .24 SR .77 . 72 B-Ratio — .9 82 ∗ marginal significance levels net is slightly higher and the Hannan-Quinn information...

Ngày tải lên: 21/06/2014, 07:20

... model, outlined in McKibbin and Wilcoxen (1998). 1 92 7. In ation and Deflation: Hong Kong and Japan 1988 1990 19 92 1994 1996 1998 20 00 20 02 −0.04 −0.03 −0. 02 −0.01 0 0.01 0. 02 0.03 0.04 0.05 Linear NNRS ... forthcoming. Chen, Racine, and Swanson (20 01) have used a ridgelet neural network for forecasting in ation in the United States. McNelis and McAdam (20 04) u...

Ngày tải lên: 21/06/2014, 07:20

... Hong Kong 21 5 20 02. 2 20 02. 4 20 02. 6 20 02. 8 20 03 20 03 .2 2003.4 20 03.6 20 03.8 20 02. 2 20 02. 4 20 02. 6 20 02. 8 20 03 20 03 .2 2003.4 20 03.6 20 03.8 −15 −10 −5 0 5 10 15 −10 −5 0 5 10 15 Linear Principal Component Nonlinear ... 1997– July 20 03 Statistic Maturity in Years 23 45710 Mean 28 .581 26 .1 92 24 .28 6 22 .951 21 .29 5 19.936 Median 27 .500...

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 ... or forecast well sharp turning points in data. For this reason, we turn to nonlinear forecasting techniques. 2. 2 GARCH Nonlinear Models Obviously, there are many...

Ngày tải lên: 21/06/2014, 09:20

... bootstrapping the initial training set. As we discuss in Section 4 .2. 8, bootstrapping involves resampling the original training set with replace- ment, and then taking repeated forecasts. Bagging is ... faith in particular point forecasts. Granger and Jeon (20 02) have suggested “thick modeling” as a strategy for neural net- works, particularly for forecasting. The idea is simpl...

Ngày tải lên: 21/06/2014, 09:20