Band 4, Heft 5

Regional tax burden difference is related to regional sustainable development and social interests balance. Based on the calculation of the tax burden of 31 provinces and cities in 1995–2013, this paper puts forward that the over-statement of GDP is an important reason that causes the deviation of tax share and economic share. By using spatial econometric method, the results show that the economy as a whole is overvalued; investment in fixed assets, the proportion of secondary industry and state-owned economy have positive effects on regional GDP overestimated; per capita income and R&D have significantly negative effects on regional tax growth. Besides, the regional tax burden has spatial autocorrelation characteristic. This paper explains the abnormal difference of regional tax burden from a new perspective.

This paper proposes a financing system consisting of a bank under Mean-Variance criterion and a capital-constrained retailer, where the bank offers an unlimited credit to the retailer. The demand is assumed to be stochastic. The newsvendor is allowed to make an emergency order with a minimum reorder quantity threshold (RQT). It shows that under RQT, the newsvendor has different reorder strategies. The optimal primary order quantity and interest rate are derived, sequentially. Extension under perfectly competitive capital market is given. The mathematic model reveals that RQT and reorder price have significant effect on the optimal strategies.

This paper constructs the relationship between consumption and economic growth by a structure equation model and uses the provincial panel data of 29 provinces (municipalities, autonomous regions) from 1992 to 2010 in China, using maximum likelihood estimation method to analyze empirically the relationship between the consumption and economic growth in China. The result shows that the path coefficients between consumptions and economic growth are all positive, that suggests the consumption has significant positive effects on the economic growth. Also in this paper, it gives a new try to use a structural equation model to research the relationship between consumption and economic growth.

This paper considers a multi-period fuzzy portfolio selection problem maximizing the terminal wealth imposed by risk control, in which the returns of assets are characterized by fuzzy numbers. A fuzzy absolute deviation is originally defined as the risk control of portfolio. Entropy constraints and borrowing constraints are added in the portfolio selection model. Based on the theories of possibility measures, a new multi-period portfolio optimization model with transaction costs is proposed. And then, the proposed model is transformed into a crisp nonlinear programming problem by using fuzzy programming approach. Because of the transaction costs, the multi-period portfolio selection is the dynamic optimization problem with path dependence. Through changing the cost function into a variable, the multi-period portfolio selection is approximately turned into the dynamic programming. Furthermore, the discrete approximate iteration method is designed to obtain the optimal portfolio strategy. Finally, an example is given to illustrate the behavior of the proposed model and the designed algorithm using real data from the Shanghai Stock Exchange.

Owing to the limited number of inspections during a short run process, it is impossible to get the correct estimate of the population mean and standard deviation during Phase I implementation of control chart. The t control chart proposed recently can overcome this problem. The EWMA t control chart has been proposed to monitor the process mean, but a single EWMA t control chart cannot perform well for small and large shifts simultaneously, which is known as the “inertia problem”. The adaptive varying smoothing parameter EWMA (AEWMA) control chart can overcome the inertia problem. In this paper, the AEWMA t control chart for short run process is proposed. The truncated average run length and the probability of trigger a signal are adopted to test the performance of short run AEWMA t chart. Based on the investigation of the joint effect of control chart parameters on the performance of AEWMA t chart, a new optimization algorithm is proposed for statistical design of the AEWMA control chart. Simulations are performed for perfect and imperfect setup conditions, the results show that the AEWMA t control chart performs better than the EWMA t control chart.

For a class of non-affine nonlinear systems with state constraints, input constraint, uncertain parameters and unknown external disturbance, a back-stepping control scheme is proposed based on mean value theorem, nonlinear mapping and prescribed performance bounds (PPB). The non-affine system is first transformed into a time-varying system with a linear structure by using the mean value theorem, and the intervals of the time-varying uncertain parameters are calculated. The bounded time-varying parameters and external disturbance are estimated by adaptive algorithms with projection; the estimation error is compensated by employing nonlinear damping technology. To handle the state and input constraints, the nonlinear mapping technique (NMT), hyperbolic tangent function and Nussbaum function are employed. The prescribed performance control method improves the performance of the system. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Cox’s proportional hazard models with time-varying coefficients have much flexibility for modeling the dynamic of covariate effects. Although many variable selection procedures have been developed for Coxs proportional hazard model, the study of such models with time-varying coefficients appears to be limited. The variable selection methods involving nonconvex penalty function, such as the minimax concave penalty (MCP), introduces numerical challenge, but they still have attractive theoretical properties and were indicated that they are worth to be alternatives of other competitive methods. We propose a group MCP method that uses B-spline basis to expand coefficients and maximizes the log partial likelihood with nonconvex penalties on regression coefficients in groups. A fast, iterative group shooting algorithm is carried out for model selection and estimation. Under some appropriate conditions, the simulated example shows that our method performs competitively with the group lasso method. By comparison, the group MCP method and group lasso select the same amount of important covariates, but group MCP method tends to outperform the group lasso method in selection of unimportant covariates.