This paper simulated the influence of technological progress based FDI on China's agriculture through theoretical analysis and CGE model. The results show: With the East Asian regional economic cooperation, there were increasing FDI based on free trade area to China gradually; The technological progress of agriculture and their factors sectors would promote their production and exports. Vegetables, meat and milk exports increased significantly, while real wages and aggregate consumer value dropped; The elasticity of economic indicators to technological progress based on agricultural factors sector were relatively large; The lager technological progress of agricultural sector, the greater regional differences in output growth, while the technological progress of agricultural factors sectors led to even more regional development unbalanced compared to agricultural sectors.

Taking into account an incomplete market and making use of utility indifference pricing method, this paper studies subjective values of corporate securities, optimal capital structure and optimal default threshold. We find that: 1) In contrast to a complete market, for a given debt level, the subjective equity value decreases but the leverage increases with idiosyncratic risk. 2) By comparison with the capital structure under a complete market, the optimal leverage is less and the yield spread on debt is greater under an incomplete market. 3) The systematic and idiosyncratic risk premium for equity decrease with the current level of cash flow of the firm. 4) Risk aversion parameter and idiosyncratic risk have a significant impact upon the optimal default threshold, the subjective equity value and debt value.

This paper proved that, based on the data generated by the linear dynamic causal model, the present alogrithms of causal infernce just as IC, SGS or PC alogrithm can be used to explore the true causal structure between contemporaneous variables correctly, and this result is not dependent on the assumption that the disturbance terms in the dynamic causal structure model are normally distributed. Furthermore, the paper demonstrates that the SVAR model and the linear dynamic causal model are the same data generating process under the assumption of recursive structure, and there are corresponding relations between the coefficient matrix of contemporaneous variables in the SVAR model and the causal structure of contemporaneous variables in the linear dynamic causal model which is a DAG. Therefore, We have established a theoretical basis for the method of identifying the SVAR model based on DAG. The paper has also given a necessary and sufficient condition for identifying SVAR model with the method. Futherly, the results of Monte Carlo simulation have supported all of those conclusions.

Based on efficient importance sampling (EIS) technique, we propose the maximum likelihood (ML) method to estimate the stochastic volatility (SV) models based on four different return distributions. Taking Shanghai Stock Exchange composite and Shenzhen Stock Exchange component indices as an example, we empirically test the performance of the SV models based on different return distributions, and aim to find the appropriate return distribution for China's stock market. Empirical results demonstrate that the SV model based on the skew student's t-distribution (SVSKt model) which can account for skewed and peaked and heavy-tailed returns provides significant improvement in model fit over the SV models based on the normal, the student's t and the generalized error distributions (GED).

This paper numerically analyzed the effectiveness of monetary and fiscal policies, and their associated risks on growth rate, inflation rate under the different monetary and fiscal policy risk mix respectively by using equilibrium in a stochastic dynamic optimal model. The research results show that the effect of different monetary fiscal policy risk mix are different in different economic conditions. The policy effect of fiscal policy on output is more significant than the effect of monetary policy, and the policy effect of monetary policy on inflation expectation is more significant than the effect of fiscal policy. Monetary and fiscal policies are interactive and complementary. Monetary policy and fiscal policy mix can remain the economy stable and sustainable growth.

Theoretically, this study investigated the effects of the density of firms' relationship network on executive compensation and pay-performance sensitivity by developing a theoretical model. Using a dataset of Chinese listed firms over the period 2005-2010, this study quantized the density of relationship network, and tested conclusions drawn by our theoretical model. The result shows that about 83% of Chinese listed firms have at least one director/CEO who also works as a director/CEO in other listed firms. Consistent with the theoretical model, this study also shows that the greater density of firms' relationship network is associated with significantly higher level of executive compensation, but significantly lower pay-performance sensitivity.

This paper finds that value added induced by per unit of final demand has decreased in China from 2002 to 2007, by employing non-competitive input-output model capturing processing trade. To explore the reason why it decreased, the paper proposes a modified structural decomposition analysis (SDA) method, considering the characteristic of multi-regional input-output table. By using this method, one not only can decompose the technology coefficients into substitution effect and fabrication effect, but also can evaluate the contribution of each region to the change of dependent variables. The results show that decreasing value-added ratio and change of final demand structure of domestic enterprises' products are the main causes of the decrease. Meanwhile, substitution of foreign invested enterprises' products to imports has inhibited this decline effectively.

In a mixed model assembly line, the demands for products are usually uncertain. When the actual demands are larger than expectation, capacity adjustments should be made. The difficulty and cost of these adjustments rely heavily on the initial balancing. To the best of knowledge, the convenience of future adjustment and corresponding cost have not been considered in literature. This paper investigates the line balancing problem in a mixed model assembly system which faces uncertain demand and makes use of overtime work to meet unexpected demands. The objective is to minimize the expected labor cost, including cost for normal operation and overtime work. A mathematical model is built and a method is proposed to estimate the cost lower bound, based on which a single pass heuristic is designed. The numerical experiment shows that the algorithm is effective and efficient.

A principal-agent problem with asymmetric information is considered in this paper. Based on theories of ill-posed bilevel programming, the definition of ill-posed principal-agent problem is proposed. Then, a fuzzy interactive coordinating algorithm is presented for its optimistic formulation. Furthermore, a satisfactory contract is eventually derived for both principal and agent so that both are winners. Finally, an example is given to illustrate the rationality and the operability of the proposed algorithm.

Labor division model of ant colony is one of important manifestations of collective intelligence, which has been widely applied to task allocations, logistics distribution and so on. In this paper, ability-evaluation and interest-driven are introduced so as to extend basic labor division model. In addition, three different simulation examples are given in order to analyze its performances. The results show extended model has better robustness and flexibility, which is more suitable to be applied to task allocations in a dynamic environment.

Traditional method of constructing production frontier in data envelopment analysis seems to be much more complex, in order to solve this problem, we propose a new DEA production frontier algorithm, which is called the rotation algorithm. We explain the meaning of rotation algorithm. Respectively, from the two-dimensional and high-dimensional perspective, we build the production frontier of traditional four DEA models, and prove the theoretical basis of the algorithm. Through practical examples, we prove that, compared to traditional methods, such as vertex and extreme direction method, Graham scanning method, rotation algorithm is much simpler and has a very wide practical value. Finally, we apply the rotation algorithm to practice——Construct a new DEA model dealing with negative data in decision making units, which the traditional DEA model cannot handle.

In order to improve the benefit and competitiveness, dynamic pricing decision-making under uncertain demand is one of the most important problems for a multimodal transport operator. Based on revenue management combined with freight organization features of container sea-rail intermodal transport, from the point of differentiated pricing of contract sale and free sale, a two-stage optimal model was established in this paper, which integrated dynamic pricing and slot allocation together on a multi-node container sea-rail intermodal transport line. The model in the first stage was proposed by considering long-term slot allocation in contract market and the empty container transportation, and the model in the second stage was proposed by considering the dynamic pricing in different booking periods of free market. According to the demand randomness in the models, the methods of chance constrained programming and robust optimization model were employed to solve the models respectively. The example shows that the models and algorithms are feasible and effective.

The integrated problem with truck scheduling and storage allocation was researched in this paper. Here, the container's arrival was dynamic and truck was used circularly, so the containers stacking position and truck scheduling had to be decided simultaneously. It was formulated as an integer programming model to minimize makespan of the whole discharging course. In the model, the truck scheduling constraints and stack crane operation constraints were also considered. Two stages tabu search algorithm was designed for solving above problem. The new method was compared with software CPLEX 11.0. The experiment demonstrates that the two stages tabu search obtains optimal solution for seven cases of total cases and the average gap of left cases is less than 5%. The experimental results show that this algorithm can solve this integrated problem effectively.

A novel model was built to optimize the container ship routing and empty/full containers transport under the mixed route structure. With the objective to maximize the profit, the model determined the calling ports of a trunk line, calling sequence, feeder ports and transport plan of the empty/full containers. A new algorithm which combined genetic algorithm and linear programing algorithm was developed to solve the model. The optimization results of a company's Asia-Europe route show that if the route is operated by 4000-TEU ships, 9 ships are needed and the profit of the carrier is 195.6 thousands USD per week, while if 10000-TEU ships are selected, 7 ships will be deployed and the profit is 313.5 thousands USD. In addition, by comparing the above two shipping schemes with different ship types, it demonstrates that with the use of larger ships, the hub-spoke and multi-port route structures will be the main routing pattern, which is consistent with the actual situation of the shipping market.

Based on the theories of Markov decision processes (MDP) and robust optimization, a robust capacity control model based overbooking and group booking of single-leg flight (abbreviated as R-MDP) is set up. At the same time, the nominal MDP model and the modified expected marginal seat revenue (EMSR) model which ignore the estimation error are also provided. Numerical results illustrate that the robust model can significantly improve the expected revenue when demand estimation errors exist, and the robust strategy performs better than the nominal strategy when the estimation error is bigger. Numerical examples also reveal that the smaller the number of remaining seats or the longer the remaining time, the better performance the robust strategy will be.

The traditional model of airports productivity evaluation ignored the impact of undesirable outputs such as delayed flights, noise, harmful emissions and so on. A non-parametric directional output distance function, rather than the traditional data envelopment analysis, was applied to construct an airport productivity evaluation model based undesirable outputs. The empirical research regarding 10 airports in Yangtze River Delta multi-airport region was performed to verify the model. The results show that undesirable output is one of the indispensable factors on airport productivity. Secondly, undesirable outputs affect the airport productivity significantly. There is non-positive correlation between airport productivity and airport size after considering the undesirable output. Finally, some suggestions of a balance between quantity and quality of outputs in the achievement of efficient outcomes for authorities are provided.

From the analysis of traffic's characteristic of China's urban intersections and considering the constraints of queue length and pedestrians cross the street, a multi-objective programming function is constructed to minimize not only the vehicle average delay but also the mean square value of saturations of car flows of all phases. The constraints are that the queue length is not more than the driveway default length and the pedestrians cross the street phase is long enough. The efficacy coefficient method is used to formulate an evaluation function that covers all objective functions of the original problem. An example of Tianhebei road and Tianshou road intersection validated the model and the evaluation function was optimized by genetic algorithm. The results show that the model can prevent urban isolated intersection which contains short sections overflow effectively.

To solve collaborative slot allocation with airport probability capacity, a stochastic collaborative slot allocation strategy was proposed. According to the form of airport capacity being updated, both static model and dynamic model were proposed. With the aim to minimize the expected total flight delay cost and expected average delay time of passengers, the principles of efficiency and equity were respectively established. On the basis of the constraints of effectiveness, slots were allocated to arrival flights. Then, multi-objective genetic algorithm NSGA-II was applied to solving the models. To test how well the models work in real world, a simulation test was performed based on the operation data of an airport. Test results show that, compared with typical models for stochastic ground delay policy, the models proposed could efficiently, equitably and effectively allocate slot with airport probability capacity.

This paper is the first attempt to use the non-cooperation game theory to study the strategies of airline carbon tax from EU. Based on inducing four scenarios, non-resistance, refusal of payment, retaliatory duties and creditable threat, Stackelberg game models are developed to find the optimal strategies of countries. The results show that the strategy of refusal of payment, which China is taking step, is superior to the retaliatory duties. The main contribution of this paper is to study a new important international issue, airline carbon tax, summarize four coping strategies and analyze this issue by establishing Stackelberg game models for the first time.

For the problem of interval numbers multiple attribute decision, this paper proposes a ranking method for interval numbers decision vectors. This method processes the information of interval numbers decision vectors using Mahalanobis-Taguchi system and ranks interval numbers decision vectors using TOPSIS method. In this method, an m-dimensional interval numbers decision vector is regarded as super cuboids in m-dimensional attributes space; some points are uniformly and dispersedly selected by two-level orthogonal arrays. Those points compose of distribution points set to stand for decision making project. The distance of points in distribution points set of decision making project and distribution points set of positive ideal project is calculated with Mahalanobis distance; the closeness degree from decision making project to positive ideal project is defined by signal to noise ratio. The detailed decision making steps are given. Finally, an application example is analyzed. At the same time, the method proposed and others are compared, which shows the effectiveness and feasibility of the method.

A new 2-tuple linguistic decision-making model based on pure linguistic labels is presented by applying the classical 2-tuple fuzzy linguistic representation model and the continuous linguistic model synthetically. Furthermore, two transforming functions between the extended linguistic set and the interval of [0, 1] are defined, and the concept of linguistic utility vector of linguistic label is introduced. Later, linguistic variable weight vector and linguistic state variable weight vector are constructed by using utility vector of linguistic label and classical variable weight vector as well as classical state variable weight vector. Simultaneously, the formula for calculating variable weights based on linguistic labels and the variable weights synthesis decision-making models are proposed. Finally, an example of group decision-making is presented.

Group decision-making problems based on time-series fuzzy soft set environment have been investigated. For the situation that fuzzy soft sets information's dynamic change as time, the concept of time-series fuzzy soft set is defined, and relative operations and properties have been discussed, too. Considering fuzzy soft sets information of different time has different effect on final decision, the weight determination formulas based on the exponential decay model are given in continuous and discrete time, respectively. Then operations of fuzzy soft sets and the computational formula of the arithmetic weighted average operator have been given with aggregating thought, and the time-series fuzzy soft sets have been aggregated into collective fuzzy soft set. The optimal decision can be obtained by calculating choice value of objects with tools such as the level soft set. Finally, the group decision making approach based on time-series fuzzy soft sets has been proposed and a practical example has been analyzed to verify the reasonability and feasibility of the approach.

We propose an improved fuzzy maximum operator and fuzzy subtraction operator to determine the fuzzy time parameters of fuzzy network. By enumerating different values of α-cut, the improved interval numbers distance measure-based fuzzy maximum operator computes the earliest starting fuzzy time for each activity and determines the possible changed critical path of the project, thereby fills the gap of the existing work which did not consider the fact that the critical path may change in case of fuzzy activity times. The latest starting fuzzy time for each activity is calculated by the improved fuzzy subtraction, which avoids generating negative and infeasible solution while ordinary backward recursive calculation conducted. An example is illustrated to validate the effectiveness and superiority of the proposed method on solving the fuzzy time parameters of fuzzy network. The proposed method not only can be used to the calculation of fuzzy network time parameters, but also can be used to the fuzzy resource-constrained project scheduling problem.

Fuzzy analytic hierarchy process has been widely used in many fields, but Thomas L. Saaty, the architect of AHP, questioned the validity of fuzzy AHP and had published three articles which show the invalidity of using fuzziness with the AHP. In this paper, we review Saaty's comments on fuzzy AHP from three aspects: fuzzy AHP is lack of mathematical validity; the judgments in AHP are themselves already fuzzy and fuzzy them again has no meaning; improving inconsistency need not improve the validity of the outcome. Through this paper, we do further analysis on both AHP and fuzzy. We hope it will make some help for decision-makers when using AHP.

Population characteristics deserve closer study when two kinds of conflict information spread over social network at the same time. According to complexity theory and sociology achievement, we developed conflict information spreading model by using SIR model on network. Here we introduced three manners for individual to resolve conflict information involving "first impressions are strongest", "herd effect" and "leader opinion" into the spreading process, and then conducted contrastive experiments of the model corresponding different behaviors on the large-scale social network. The experiment results show that social network structure and individual manners significantly affect the information spreading. From local interactions to collective behavior, global feature has more diverse trends than local feature, and this study will contribute to the explanation of large-scale population characteristics such as go to extremes and diversification.

Adaptive governance is a new choice to solve the problem of water resource governance which caused by the increasing complexity and uncertainty. It offers a great feasible approach with the learning capacity to settle issues caused by climate change, etc. This paper proposed the experience-weighted attraction (EWA) transformational learning based on the EWA learning theory, social learning theory and behavioral game theory. The EWA transformational learning has characteristics of multi-level and triple loop characters to promote the knowledge sharing and communication between individuals. Meanwhile, the paper analyzed the factors which may affect the human behavior pattern by system dynamics. Then, the paper modeled the dynamic system of water transfer in Hami region to analyze the law of the human behavior pattern, identify the key factors which affected human decision-making, and offer the plan for policy adjustment. The outcome of simulation proved that the learning rule of adaptive governance could improve the effective and the continuity of water transfer.

In line with the fact that major inspections and minor inspections are usually adopted together for system inspection in engineering practice, this paper proposes a multiple inspection policy model based on a three-stage failure process. The concept of the three-stage failure process divides the system lifetime into three stages, that is, normal stage, minor defective stage and severe defective stage. With the consideration of all the possible scenarios for failure renewal, minor inspection renewal and major inspection renewal, a multiple inspection policy model is presented for single-unit systems using the renewal reward theory. The objective of the model is to minimize the long-run expected cost per unit time, with the minor and major inspection intervals as the decision variables. In order to illustrate the applicability of the multiple inspection policy, a single inspection policy counterpart which includes only major inspections is also developed for the purpose of comparison. The threshold value for the minor inspection cost which makes the multiple inspection more favorable than the single inspection policy is studied. Numerical examples are presented and the results show that the multiple inspection policy is better than the single inspection policy when the cost of minor inspection is smaller than the threshold level and other parameters are fixed.

This article analyzes the M/M/R machine repair problem and assumes that a breakdown machine may be served by multiple servers at a time. The steady-state probabilities are obtained by the matrix analytical method with numerical examples. In addition, we develop a cost model to discuss the impact of the service rate on the performance indexes of the system, and get the optimal service rate.

In double-layer capacitated arc routing problems (DCARP), both the high-level configuration problem and the low-level service problem are considered. In the proposed knowledge-based genetic algorithm (KGA), two extended heuristic approaches are applied to produce initial individuals, the performance knowledge of operators is applied to determine an appropriate operator for each selection, crossover and mutation, the arc priority knowledge is employed to decide a suitable broken position for operations of crossover and mutation, and the partial replacement procedure is implemented to maintain population diversity. Experimental results suggest that KGA outperforms other methods.

In most existed researches on architecture-based software reliability, parameters are mostly assumed as constants. But such an assumption is inconsistent with the practical situation for ignoring the impact on system reliability caused by statistical deviations of parameter estimation. For solving this problem, a judgment method for key parameters in software reliability analysis is proposed in this paper, which is based on the moment estimation theory. In this method, the characteristics of architecture-based software reliability models are analyzed, and the relationships between the system reliability prediction result and the moment estimation values of the parameters are given explicitly, then the impacts of the different parameters on system reliability can be analyzed quantitatively and the key elements can be judged simultaneously. The method can give some new theoretical supports for system reliability controlling, system architecture optimization and optimal allocation of software test resource. In the end of this paper, two actual examples of software system reliability analysis are given to illustrate the effectiveness of the method.

Dempster-Shafer evidence theory based on interval-valued belief structures has wide application prospect in the fields of information
fusion and decision analysis. To suppress the counterintuitive results generated from the combination of conflicting interval-valued belief
structures, a modified evidence combination approach is proposed. An optimization model of pignistic probability distance is built from the
global perspective to provide the relative importance weights for weighting evidence such that the weighted evidence can be reasonably combined with the
Dempster rule of combination. Numerical examples show the efficiency and rationality of the proposed approach.

In order to improve the accuracy of prediction of aircraft arrival time and the stability of queue in the terminal airspace, a probabilistic model for the same type aircraft crossing the same entry point was established through analysis of the main factors influenced the estimated time of arrival, and a probabilistic prediction method was proposed using a large amount of actual flight data. The uncertainty of estimated time of arrival was calculated using Bayesian network. The results show that the probabilistic method for estimated time of arrival could quantize the uncertainty exactly and be help to improve the stability of arrival queue.

In order to calculate the damage probability of antiaircraft gun weapon system more accurately, the shooting errors were separated according to error sources. The time community (correlation) of the shooting error components caused by the same error source was analyzed. For antiaircraft gun weapon systems with common fire control subsystem and with independent fire control subsystem, the space community (repetitiveness) of the shooting error components was analyzed. The generation method of weak correlated error series was given. Based on the above, the damage probability calculation method by Monte Carlo method was given. The contrastive study was made between the Monte Carlo method based on time and space community analysis of errors and those existing methods.

In this paper, we consider a three-dimension state space model for establishing a discrete-time dynamic Lévy process, including time-varying drift, conditional volatility and stochastic jump activity. Then we obtain the equivalent non-arbitrage pricing model through local risk-neutral valuation relationship (RNVR). Taking non-Gaussian ARMA-NGARCH model as our benchmark, we construct a discrete time dynamic Lévy process with GARCH effect for modeling S&P500 index. Furthermore we jointly estimate the parameters of the model and study the option pricing performance based on Bayesian learning approach. Research results show that our dynamic Lévy process can depict the time-varying drift rate, conditional volatility and infinite activity styles. Meanwhile, Bayesian approach improves the option valuation ability of our model. Infinite jump models are significant superior and increase the pricing accuracy of implied volatility. We also find that unscented particle filtering (UPF) has the best estimation performance, non-Gaussian models in the yield prediction are of no significant difference, but the rapidly decreasing tempered stable processes (RDTS) have minimum errors for option pricing.

Based on contagion identification, this paper studies contagion channels and mechanisms of the U.S. subprime mortgage crisis. The securities markets of 25 economies are divided into non-contagious and contagious groups by using the adjusted volatility regression model. The 16 key indicators which represent the five contagion channels including macroeconomic fundamentals, trade, financial, regional and debt are selected to conduct both sectional and panel logit regression. The sectional model finds regional, financial and debt channels determine the probability of contagion. The panel model shows that trade and financial channel are main transmission channels. Bank capital asset ratios are significant but with opposite sign in both models, indicating that in the short term, although with high capital assets rates a number of banks bust due to the rapid crisis broken but in the long term the higher the rates, the greater to resist contagion.

Based on the effectiveness of ensemble empirical mode decomposition (EEMD), we deal with nonlinear and non-stationary financial time series. EEMD is applied to decompose the sequences of investor sentiment and stock indices into several independent intrinsic mode functions (IMF) with different scales and a residual term, to extract the fluctuation characteristics in different time scales of the sequences. And then we reorganize them to three parts: High frequency represents the short-term market fluctuations, low frequency by major events, and long-term trend. Further combined with econometric models, we analyze the fluctuations between investor sentiment and stock indices in different time scales. The empirical results show that the fluctuation relationship between investor sentiment and stock indices in different time scales is significantly different: in short-term, it's a two-way influence; in the medium-term, investor sentiment is ahead of stock indices' fluctuation and in the long-term, it's reverse.

The highly perishable nature of fresh products during distribution creates additional challenges for firms engaged in the supply chain in the process of matching supply with demand. This paper considers a retailer who purchases a batch of fresh products, transports and sells them to multiple consecutive markets. The demand from each market and the quantity loss from perishability during transportation are both assumed to be stochastic. The retailer needs to determine the quantity to be unloaded when he arrives at each market, with the remaining inventory being transported to the next market. The unloading quantity depends on the trade-off among factors including the current inventory level, forecast of demands from the remaining markets, and the potential perishability of products. Based on a multi-period dynamic programming model and using a stochastic modeling approach, we provide an in-depth investigation towards the optimal unloading quantity decisions for the retailer and characterize the structure of the optimal policy. Moreover, the optimal initial inventory decision is studied. By conducting some numerical experiments, we analyze the potential inventory pooling effect from making dynamic decisions. Our results show that making unloading decisions in a dynamic way is more profitable when the inventory surviving factors are negatively correlated, when the products are more perishable, when the perishability risk is high, and when the demand is less uncertain. Our research results provide some interesting and important managerial insights for the management of fresh products across multiple consecutive markets.

Taking into account the sensitivity of demand to price, delivery time and the linear relationship between the price and delivery time, this paper established the decision-making model of optimal delivery time about two (make to order (MTO)) enterprises, and concerned alternative between products and faced different competition markets made the model more realistic situation. Firstly, the optimal model was analyzed. Secondly a numerical study explored effects on the optimal decision of customer preferences coefficient and rate of substitution, as well as the optimal choices in competition and cooperation mechanism. The conclusions showed that different customers preferred to make different decision results. The enterprises should take different decisions based on market segments and customer types. In the case of the alternative, the enterprises lack the motivation of cooperation, competition is widespread. However, if the two companies want to maintain a stable delivery time and price, cooperation mechanisms should be used. The conclusions can provide useful guidances for the scientific enterprise market competition.

This paper attempts to develop a real options-based approach to investigate the important implication of authoritarian mechanisms for economic development under uncertain environments, from the respective perspectives of central and local governments. By defining the concepts of transferrable costs and authoritarian rents, our model can shed new light on why authoritarian mechanisms can accelerate the economic growth, and provide new insights on the incentive problem of "collusion" or side contracts between local governments and enterprises. In order to maximize the social welfare, from the perspective of the central government we also allow for the optimal design of contract mechanisms between central and local governments. Our model results show that when the negative social costs caused by transferred costs are relatively little and if the "collusion" or side contracts between local governments and enterprises are allowed, authoritarian mechanisms can play important roles in accelerating economic growth and making macroeconomic policies should therefore take full account of the significant effect. Furthermore, our paper also provides constructive proposals concerning the future economic development in mainland China and facilitate improving the scientific conception of economic development in this country.

Based on the nonparametric cointegration test, this paper develops the smooth transition autoregression (STAR) model to forecast the short-run macroeconomic fluctuations using the leading indicators. It is found that the prediction error of actual output levels provided by Organization for Economic Co-operation and Development (OECD)'s leading indicators after the seasonally adjusted is within 1%, and the prediction error of year-on-year growth rate of Gross Domestic Product (GDP) provided by National Bureau of Statics of China (NBSC) is less than 5%. Forecasting result of GDP growth in 2011 showed that the forecasting results of two leading indicators are very close to the real GDP growth rate, and leading indicator provided by OECD is prior to that provided by NBSC.