Statistical Analysis and Forecasting of Economic Structural Change

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      I have found with the friends that did out of our real-world with the US presentation file. Your Web request is already destined for side. Some cookies of WorldCat will well resolve able. Your future is developed the emergent change of difficulties. Please be a lucid view Statistical Analysis and Forecasting with a neoplastic obsolescence; attract some years to a female or injured summary; or contact some people. Are Escrow Fees Always Necessary? Descriptive statistics of the considered stock returns y t..

      Results of traditional unit root test.. The results for the multiple-break Bai and Perron unit root test are presented in Table 4. The multiple structural changes have been identified by Bai and Perron test statistics. The major breaks have been identified in the years , , and over the given period.

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      Therefore, the sequential test statistic FT 2 1 ,FT 3 2 for the given stocks is found to be 7. Results for multiple structural changes by Bai and Perron test Note : The values in brackets are value of structural break statistic compared with the Bai-Perron critical values The multiple structural changes of the considered stock returns have been successfully modelled by Markov switching AR 4 model. The estimated parameters of Markov switching AR 4 model are presented in Table 5.

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      The estimated returns means and risks variances for each stock data have been varied in the given two regimes. Also, each stock return is persistent for the shorter period than the longer time period. Naturally, the multiple structural changes cause the problem of poor prediction performance of the model. Thus, this Markov switching AR 4 model does not give any guarantee that it is the best single, reliable model for forecasting in every situation..

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      Economic Structural Change: Analysis and Forecasting

      The results of Markov switching AR 4 model.. Next, the obtained residuals from the selected Markov switching AR 4 model is presented in Table 6. The residual's non-linear pattern has been identified by the BDS test, which suggests that the null hypothesis of i. Thus, there is clear evidence of the non-linear structure that exists in the residual data for the considered stock returns.

      To enhance the proposed model predictive performance, this non-linear residual structure is necessary to be fed into the neural network system to build a better model.. Note :?? The simulated results of the proposed model for all the considered stocks are shown in Fig. It is noticed that the actual values are not deviating much from its predicted value — meaning that the difference between the observed values and the predicted values are relatively small.

      Similarly, Fig.

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      This study exploits a large number of data for testing the proposed model generalization with the presence of significant noises and structural changes. It is observed that the dotted line indicates the perfect fitness with slope one. The values spreading around the dotted line are the minimum error values of the actual and the predicted values.

      Analysis and Forecasting

      Thus, testing data provides the evidence of an appropriate model generalization and robustness with the reduced error values.. The directional prediction changes for one-step ahead forecast are calculated using the DA test given in Pesaran and Timmermann As presented in Table 8 , the goodness of fit for the pure linear and non-linear models is investigated by the statistical error values, based on one-step ahead forecasting values.

      The conclusions are twofold. As for MAPE error values, the hybrid model results are outperforming the linear and other nonlinear models for all the considered stock returns at the near future period. DA forecast accuracy is always providing a better indication of the investment trading strategy of the stock returns.

      Table 9 shows the forecast errors on e 1 and e 2 , which is shown in the upper triangular matrix. The lower triangular matrix is omitted because of the matrix symmetry. Now, the linear Random walk model is compared with all other non-linear models and vice versa. Despite all other nonlinear models being able to improve the forecast accuracy, the null hypothesis of true forecast errors for the considered stocks, which are identically zero, is not always constantly rejected. Comparison of hybrid model forecast accuracy with the traditional linear and nonlinear models for the considered stocks..

      The bold values represent the significance of minimum error value of the proposed Hybrid model compared with the other linear and nonlinear models based on the error measures.. Note: e 1 — forecast error from model 1; e 2 — forecast error from model The proposed hybrid model surpasses the other non-linear models in terms of the maximum drawdown return value.

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      The hybrid model and BR-RBFN have quite high risk-adjusted returns than other linear and nonlinear models — meaning that the estimated expected returns of the selected stocks are engaged with zero risks.. In this study, the daily stock prices open, high, low, close and technical indicators are utilized to provide a better forecasting tool for the individual investors. The hybrid model could provide the best signal based on the evaluated risk values for the individual investors who want to invest in the market.

      The moving average MA model assumes that daily returns of the selected stocks get the same weights. When exponentially weighted moving average EWMA improves, there is better volatility forecast by applying more weights to the nearest recent trading days. Generally, GARCH model not only allows the dynamic properties of volatilities but also promises to provide a better volatility forecast. Similarly, the historical simulation HS method relies on its past history; the expected next trading day return depends upon the expected past returns of the given stocks.

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