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This study examines the relationship between financial development and economic growth across Chinese provinces with switching causality. Four states are considered: bidirectional causality (state 1); one-way causality from growth to finance (state 2); one-way causality from finance to growth (state 3); and non-causality (state 4). While state 3 dominates in developed regions, states 1 and 3 occur intermittently in other regions. This implies that the demand for financial services induced by local economic growth plays a stronger role in driving financial development in under-developed regions. Consistent with prior research, bank loans negatively affect economic growth in China. [ABSTRACT FROM AUTHOR]

The issue of house price convergence in 34 Chinese cities is investigated. We augmented the convergence model with contemporaneous spatial dependence in house prices and found that price convergence and positive spatial spillover are both present. We explicitly addressed the endogeneity problem by introducing a Bayesian instrumental variable setup, which was estimated with particle filtering techniques. From a growth poles perspective, the empirical evidence indicates that the spread effect in regional house prices outweighs the backwash effect. The identified positive spatial spillover has two effects on the growth of house prices in Chinese cities. First, the spillover elevates the trajectories of the steady-state growth paths of house prices. Second, the spillover narrows the gaps between the growth paths of house prices in neighbouring cities. Shocks to the socio-economic variables of a city generate their own effects on domestic house prices that dominate the effects arising from cross-city price feedbacks, thus mitigating the prospect of level convergence. Our findings also suggest a collaborating role between time and spatial dependence parameters. The identification of inter-city spillover, which is a conditioning factor for regional house price convergence, offers implications to policies that are most likely to be effective in reducing regional disparity. [ABSTRACT FROM AUTHOR]

This paper introduces a Bayesian forecasting model that accommodates innovative outliers. The approach suggested differs from the well-known time varying parameter model and tries to capture instead innovative outliers. In terms of forecasting performance retains the edge over the standard V AR. It may underperform the standard BV AR, however, since an arbitrary shrinkage of the coefficients towards the prior mean may distort the posterior odds of identifying outlying observations. This shortcoming can be balanced by the added information regarding outliers. The hierarchical specification of prior distributions allows an identification of observations contaminated by these outliers and endogenously determines the hyperparameters of the Minnesota prior. Estimation and prediction are performed using Markov chain Monte Carlo methods. The model forecasts the Hong Kong economy more accurately than the standard V AR and performs in line with other complicated BV AR models. It is also shown that the model is capable of finding most of the outliers in various simulation experiments.