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In this paper, we investigate the impact of 2010 World Expo on the economic growth of Shanghai via a dynamic VAR model. In this model we take the fixed assets investment, new employment population, new tourist income as influence variables, and take GDP (Gross Domestic Product) and House Hold Consumption as influence object. By establishing VAR model (Vector Autoregressive Model) and utilizing impulse response analysis, we investigate the persistent influence of 2010 World Expo to economic aggregate and House Hold Consumption of Shanghai. Finally, we adopt variance decomposition analytical method to describe the impact on tourism demand of each variable. Our findings indicate that the World Expo contributes much to GDP and House Hold Consumption of Shanghai after 10 quarters, with the contribution degrees of 7.46% and 18.4% respectively.

In this paper, we analyze the dynamic effects of fiscal shocks in China between 1990 and 2008. We adopt a Bayesian vector auto-regression framework. The main findings are as follows. First, government expenditure expansionary shocks are found to have positive effects on output. These effects become substantially weaker in the last 10 years. Second, the crowding out effects of government expenditure on consumption are found in the long-term other than in the short-term. Although the effects of government spending on prices are usually small and not always significant, they are positive. Third, tax revenues have negative effects on output, consumption, export and prices. The application of these results to the analysis of fiscal policy points to the conclusion that automatic stabilizers should play a more important role in China because the effect of an increase in spending or a cut in taxes would be multiplied by stimulating additional demand for consumption goods.

A framework of combining importance sampling with Structured Database Monte Carlo strategy is developed. The proposed method attempts to devise a generic method for designing importance sampling method. Firstly, evaluation function and objective function are expressed in a way that there is a linear relation between response estimator and majorized function. Order structure is imposed not only on sample paths but also on parameters of candidate density. Then the parameters are estimated by surrogate maximization algorithm. Secondly, cut-off point at which response function can maintain the same sample paths structure is obtained. Based on the low quadratic bound principle and the convexity of the second moment of the estimator, a quadratic surrogate function for objective function is derived. Finally, empirical results show that our approach is straightforward to implement and flexible to be applied in a generic Monte Carlo setting.