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对我国居民消费及其影响因素的动态分析——基于VAR模型的实证研究 被引量:11

Dynamic Analysis to Chinese Resident’s Consumption and its Determinants——Empirical Studies based on VAR Model
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摘要 我们利用1978-2006年的时间序列数据,基于宏观和动态的视角,对我国居民消费及其影响因素进行了实证分析。Johansen协整检验的结果说明我国居民消费行为与其影响因素之间存在一种长期均衡关系。向量自回归VAR模型的动态分析表明,我国居民消费对自身的冲击产生持续响应,居民消费呈现出一定程度的"惯性"特征;经济增长和人口增长对消费的短期冲击是正向的,居民消费对二者的中长期响应均为负;通货膨胀和政府支出对消费的冲击持续为负,其他因素对居民消费的短期和中长期影响各不相同,而居民消费对它们冲击的响应也呈现出不同的趋势。方差分解的结果说明,消费自身的波动是我国居民消费变异的主要原因,经济增长、贸易条件、政府支出、经常帐户余额对居民消费波动的贡献要高于其他因素的贡献。 Employing time series data from 1978 to 2006, the paper conducts empirical studies on Chinese resident' s consumption and its determinants based on the dynamic and macroeconomic view. The result of Johansen co - integration test reveals that there is a long - term equilibrium relationship between Chinese resident' s consumption and its determinants. The dynamic analysis of Vector Auto - regression model indicates that Chinese resident' s consumption engender sustainable response to its own impulse and resident' s consumption presents some kind of "inertia" characteristic; the shortterm impulses of economic growth and population explosion to resident' s consumption are positive, while the response of resident' s consumption to the former two is both negative ; the impulse of inflation and government expenditure to consumption is always negative; the shortterm and longterm effects of other factors to resident' s consumption are different, while the responses of resident' s consumption to their impulses are uncertain. The outcome of variance decomposition reveals that, the fluctuation of consumption itself is the fundamental reason of consumption variance, and the contribution of economic growth, terms of trade, government expenditure, current account balance to the fluctuation of resident' s consumption is a little higher than that of other factors
出处 《统计教育》 2009年第7期3-14,共12页 Statistical education
基金 国家社会科学基金(07BJY021)
关键词 居民消费 影响消费的因素 向量自回归 脉冲响应函数 方差分解 Resident' s consumption, Determinants of consumption, Vector Auto - regression, Impulse response function, Variance decomoosition
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