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物业税是地方政府行为,其开征势必将对地方经济产生深刻的影响。通过运用BP神经网络进行建模,确定重要参数后,运用PSO算法对BP神经网络的相关权值和阈值进行优化,建立更为精确和优异的PSO-BP神经网络模型,用于模拟上海市物业税开征,并结合Wavelet法分析物业税开征后对上海GDP的影响。结果表明,物业税的开征总体上小幅度地提高了上海市的生产总值,短期来看影响较明显,但长期来看逐渐减弱,并且在短期、中期和长期来看影响具有一定规律的波动。
Property tax is a local government behavior, its imposition will inevitably have a profound impact on the local economy. After modeling the BP neural network and determining the important parameters, PSO algorithm is used to optimize the weights and thresholds of the BP neural network to establish a more accurate and excellent PSO-BP neural network model for modeling the real estate in Shanghai Tax levy, combined with Wavelet analysis of property tax impact on Shanghai’s GDP. The results show that the property tax generally increases the GDP of Shanghai slightly, which is more obvious in the short term, but weakened in the long run and has certain regular fluctuations in the short, medium and long term .