New Frontiers in Banking Services Emerging Needs and Tailored Products for Untapped Markets 5 doc

... way: x ∗ k,t = x k,t − min(x k ) max(x k ) −min(x k ) (3.13) The linear scaling function for [−1, 1], transforming a variable x k into x ∗∗ k , has the following form: x ∗∗ k,t =2· x k,t − min(x k ) max(x k ) −min(x k ) − ... Similarly, do changes in GDP have one process in recession and another in recovery? These are very important questions for forecasting and policy analysis,...

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 ... Computation Taking the partial derivatives with respect to x and y, we find the following first-order conditions: .5 · 1 .5 | x | .5 + .5 |x | 1 .5 −4 = 0 (3.32) .5 · 1 .5 | y |...

Ngày tải lên: 21/06/2014, 07:20

... out- of-sample forecasting performance. Out-of-sample tests evaluate how well 118 5. Estimating and Forecasting with Artificial Data 0 50 100 150 200 250 300 350 400 450 50 0 0 0.1 0.2 0.3 0.4 0 .5 0.6 0.7 0.8 0.9 1 FIGURE ... 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 windo...

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... are forecast onevar scmodel new1 .m (for the stochastic 132 5. Estimating and Forecasting with Artificial Data 0 50 100 150 200 250 300 350 400 450 50 0 −6 −4 −2 0 2 4 6 8 0 50 100 150 200 250 300 ... FIGURE 5. 15. Actual and in- sample predictions: DLM model 5. 6.1 In- Sample Performance The in- sample statistics and time paths appear in Table 5. 11 and Figure 5. 15, r...

Ngày tải lên: 21/06/2014, 07:20

... forthcoming. Chen, Racine, and Swanson (2001) have used a ridgelet neural network for forecasting in ation in the United States. McNelis and McAdam (2004) used a thick model approach (combining ... fall in bank lending, or does the collapse of bank lending lead to a fall in land prices? 176 7. In ation and Deflation: Hong Kong and Japan interval (in this case we are forecast...

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... 0.216 Skewness 0.487 0 .59 0 0 .58 2 0 .53 6 0.404 0 .58 4 Kurtosis 2.064 2.2 35 2.302 2.242 2.338 3 .55 3 Max 60 .50 0 53 .300 47. 250 47 .50 0 47 .50 0 47 .50 0 Min 11.000 12.000 12. 250 12. 750 12.000 11.000 deviation ... Per- formance Root Mean Squared Error Maturity in Years 23 457 10 Linear 5. 761 2.247 1 .58 5 3.3 65 5.843 7.699 Nonlinear 1 .57 5 2.249 2.423 2.103 1 .50 4 1.207 D...

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... 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 types of nonlinear functional forms ... 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 unit. Mandic and Chambers (...

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 ... point for quite some time during the training period. Unfortunately, there is no silver bullet for avoiding the problems of local minima in nonlinear estimation. There are only strat...

Ngày tải lên: 21/06/2014, 09:20