【摘要】：本文使用H P濾波、時(shí)間趨勢(shì)平穩(wěn)、ARMA趨勢(shì)平穩(wěn)和狀態(tài)空間分解等趨勢(shì)分解方法 ,對(duì)我國(guó)GDP增長(zhǎng)率序列進(jìn)行了趨勢(shì)分解 ,并對(duì)各種周期成分進(jìn)行了對(duì)比檢驗(yàn)。我們發(fā)現(xiàn) ,這些分解方法得到的周期成分具有類似的統(tǒng)計(jì)性質(zhì) ,但就殘差序列的白噪聲檢驗(yàn)來(lái)說(shuō) ,雙變量狀態(tài)空間模型的分解效果最為顯著 ,因此應(yīng)該采用狀態(tài)空間模型進(jìn)一步分析我國(guó)的經(jīng)濟(jì)周期性質(zhì)
[Abstract]:In this paper, the trend decomposition methods such as H-P filter, stationary time trend, stationary ARMA trend and state space decomposition are used to decompose the growth rate series of GDP in China, and a comparative test of various periodic components is carried out. We find that the periodic components obtained by these decomposition methods have similar statistical properties, but as far as white noise test of residual sequence is concerned, the decomposition effect of the bivariate state space model is the most significant. Therefore, the state space model should be used to further analyze the economic cycle properties of our country.
【作者單位】： 吉林大學(xué)數(shù)量經(jīng)濟(jì)研究中心 吉林大學(xué)數(shù)量經(jīng)濟(jì)研究中心
【基金】：國(guó)家社會(huì)科學(xué)基金項(xiàng)目 ( 0 2BJY0 1 9) 教育部重大項(xiàng)目 ( 0 2JAZJD790 0 7)資助
【分類號(hào)】：F222

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本文編號(hào)：2440644