Estimating Markov-Switching ARMA Models with Extended Algorithms of Hamilton

This paper proposes two innovative algorithms to estimate a general class of N-state Markov-switching autoregressive moving-average (MS-ARMA) models with a sample of size T. To resolve the problem of NT possible routes induced by the presence of MA parameters, the first algorithm is built on Hamilton’s (1989) method and Gray’s (1996) idea of replacing the lagged error terms with their corresponding conditional expectations. We thus name it as the Hamilton-Gray (HG) algorithm. The second method refines the HG algorithm by recursively updating the conditional expectations of these errors and is named as the extended Hamilton-Gray (EHG) algorithm. The computational cost of both algorithms is very mild, because the implementation of these algorithms is very much similar to that of Hamilton (1989). The simulations show that the finite sample performance of the EHG algorithm is very satisfactory and is much better than that of the HG counterpart. We also apply the EHG algorithm to the issues of dating U.S. business cycles with the same real GNP data employed in Hamilton (1989). The turning points identified with the EHG algorithm resemble closely to those of the NBER’s Business Cycle Dating Committee and confirm the robustness of the findings in Hamilton (1989) about the effectiveness of Markov-switching models in dating U.S. business cycles.Markov-switching, ARMA process

Using affinity set on mining the necessity of computed tomography scanning

Computed tomography (CT) is a medical imaging method of tomography. Digital geometry processing is used to generate a three-dimensional image of the inside of a patient from a large series of two-dimensional X-ray images taken around a single axis of rotation. The scanning ofCT has become an important tool in medical imaging to supplement X-rays and medical ultrasonography. Although it is expensive, it is the best tool to diagnose a large number of different disease entities; especially, for the trauma patients in emergency room. In this study, the trauma patients, who were treated by the CT scanning are collected in order to discover the critical knowledge; that is, what characteristics of trauma patients would lead to the necessity of CT scanning? The data mining model of affinity set and neural network (NN) are both used for resolution and comparison. Finally, studying results show that he affinity model performs better than the NN model, but the collected data lacks the explanatory power in practices. Thus, a further research is necessary

Analyzing the influential factors of industry 4.0 in precision machinery industry

Abstract. Nowadays the science and technology progresses not only create the change to have a big impact on various industries, but also stimulate Industry 4.0 being applied in the manufacturing industry to achieve manufacturing efficiency and to reduce its cost to increase additional values. This study uses the Analytical Hierarchical Process (AHP) evaluation method, which considers four criteria layers: Internet of things factors, Automationfactors, Intelligent factors, Big data factors, and twelve influence factors in sub-layer are: perceived layer, network layer, application layer, field layer, management layer, control layer, process control visualization, system supervisory and control omni bearing, green energy manufacturing production, variety, volume, and velocity. Then, the relative risk indicator (RRI) is obtained by the Analytical Hierarchical Process method, and the overall risk indicator (ORI) can be obtained after introducing the evaluation value of each impact factor through the case. The research results confirm that the risk assessment values obtained the hierarchical analysis method are consistent. This research through the Analytic Hierarchy Process, then discusses Industry 4.0 pair of Taiwan's precision machinery industry management pattern institute emphatically face with target, expected will provide the existing machine manufacture industry as well as the future wants to invest the precision machine industry the management policy-maker reference value, also might take the government policy consideration factors and the machine manufacture industry scholars study the academic for reference.Keywords. Industry 4.0, Precision machine industry, Analytic Hierarchy Process.JEL. L22, M11, O14