【摘要】：在利用計(jì)量模型進(jìn)行分析時(shí),由于數(shù)據(jù)可能是非平穩(wěn)的序列,對(duì)多個(gè)序列間的協(xié)整關(guān)系進(jìn)行診斷檢驗(yàn)非常關(guān)鍵。對(duì)一組非平穩(wěn)序列進(jìn)行協(xié)整檢驗(yàn)就是為了決定序列的線性組合是否存在長(zhǎng)期穩(wěn)定的均衡關(guān)系。只有存在穩(wěn)定的均衡關(guān)系時(shí),利用非平穩(wěn)序列建立計(jì)量模型才有實(shí)際經(jīng)濟(jì)意義,才不會(huì)出現(xiàn)“偽回歸”問(wèn)題。Johansen和Juselius(1990)共同提出的似然比檢驗(yàn)(本文使用JJ協(xié)整檢驗(yàn))是當(dāng)前檢驗(yàn)非平穩(wěn)序列協(xié)整關(guān)系的主流方法,不僅僅是因?yàn)檫@一檢驗(yàn)方法易于理解,便于編程,更是因?yàn)樵摲椒ǖ墓π奖容^高而扭曲水平相對(duì)較小的緣故。然而,隨著研究的深入,Cheng等(1993)發(fā)現(xiàn),在樣本長(zhǎng)度較小時(shí),JJ協(xié)整檢驗(yàn)常常會(huì)存在很大的偏差,以至于過(guò)多的接受存在協(xié)整關(guān)系的假設(shè),從而造成誤判；另外,JJ檢驗(yàn)統(tǒng)計(jì)量對(duì)擾動(dòng)項(xiàng)的分布形式是敏感的。Kleibergen和Paap (2006)在研究矩陣的秩檢驗(yàn)過(guò)程中引入了奇異值分解(SVD)方法,得到一個(gè)基于SVD的秩檢驗(yàn)統(tǒng)計(jì)量,同時(shí),他還證明這個(gè)秩檢驗(yàn)統(tǒng)計(jì)量的漸近分布形式與Johansen協(xié)整跡檢驗(yàn)相似。SVD秩檢驗(yàn)方法與JJ協(xié)整跡檢驗(yàn)相比,有哪些優(yōu)缺點(diǎn),文獻(xiàn)中并未做過(guò)多的說(shuō)明。 基于此,本文首先從JJ協(xié)整檢驗(yàn)和SVD的協(xié)整秩檢驗(yàn)的理論方面進(jìn)行比較研究,得到的結(jié)論是：JJ協(xié)整檢驗(yàn)的檢驗(yàn)統(tǒng)計(jì)量是基于序列的回歸擾動(dòng)項(xiàng)矩陣的,所以相對(duì)于直接對(duì)協(xié)整矩陣進(jìn)行秩化簡(jiǎn)的SVD協(xié)整檢驗(yàn)來(lái)說(shuō),JJ協(xié)整檢驗(yàn)更容易受到擾動(dòng)項(xiàng)序列分布形式的影響。在本文的模擬研究部分,首先利用有限樣本進(jìn)行數(shù)據(jù)模擬實(shí)驗(yàn),對(duì)數(shù)據(jù)生成過(guò)程中的隨機(jī)擾動(dòng)項(xiàng)分別設(shè)定為服從標(biāo)準(zhǔn)高斯分布、泊松分布、Skewed-t分布、廣義差分誤差分布(GED)和混合泊松—高斯分布五種形式,然后分別利用這兩種協(xié)整檢驗(yàn)方法進(jìn)行分析比較；其次,本文結(jié)合實(shí)際模型中的隨機(jī)擾動(dòng)項(xiàng)的分布可能存在“尖峰”,“厚尾”和“有偏”的特征,分別在上述五種形式的分布假定下建立GARCH模型和Realized GARCH模型并生成非平穩(wěn)序列,然后利用JJ跡檢驗(yàn)和SVD的秩檢驗(yàn)方法研究協(xié)整檢驗(yàn)的扭曲水平和功效水平,得到如下結(jié)論：在非高斯分布的假定下,SVD秩檢驗(yàn)方法相對(duì)于JJ協(xié)整跡檢驗(yàn)方法的檢驗(yàn)扭曲水平較小,檢驗(yàn)功效水平較大而在高斯分布的假定下兩者表現(xiàn)差不多。在這兩種協(xié)整檢驗(yàn)過(guò)程中引入Wild Bootstrap方法,其檢驗(yàn)的功效水平均得到明顯的提高,這兩種協(xié)整秩檢驗(yàn)方法在檢驗(yàn)效果上是差不多的。 本文最后利用中國(guó)原油價(jià)格水平和迪拜原油(東南亞原油的代表)價(jià)格水平建立VAR模型,利用上述兩種協(xié)整檢驗(yàn)方法進(jìn)行分析,結(jié)果表明均接受存在一個(gè)協(xié)整關(guān)系的假設(shè)檢驗(yàn)。
[Abstract]:When using the econometric model to analyze, because the data may be a non-stationary sequence, it is very important to diagnose and test the cointegration relationship between multiple sequences. The cointegration test of a group of nonstationary sequences is to determine whether the linear combination of the sequence has a stable equilibrium relationship for a long time. Only when there is a stable equilibrium relationship, it is of practical economic significance to establish econometric models by using non-stationary sequences. The likelihood ratio test proposed by Johansen and Juselius (1990) (this paper uses JJ cointegration test) is the main method to test non-stationary sequence cointegration, not only because it is easy to understand. Easy to program, but also because the effectiveness of the method is higher and the distortion level is relatively small. However, with the further study of Cheng et al. (1993), it is found that the JJ cointegration test often has a large deviation when the sample length is small, so that too much acceptance of the hypothesis of cointegration relationship exists, resulting in misjudgment. In addition, JJ test statistics are sensitive to the distribution form of perturbation terms. Kleibergen and Paap (2006) introduced a singular value decomposition (SVD) method in the rank test of the study matrix, and obtained a rank test statistic based on SVD. At the same time, He also proves that the asymptotic distribution of the rank test statistic is similar to that of the Johansen cointegration test. The advantages and disadvantages of the rank test method compared with the JJ cointegration test are not explained too much in the literature. Based on this, this paper makes a comparative study on the theory of JJ cointegration test and SVD's cointegration rank test. The conclusion is that the test statistics of the WJJ cointegration test are based on the regression perturbation term matrix of the sequence. Therefore, compared with the SVD cointegration test which directly simplifies the rank of the cointegration matrix, the JJ cointegration test is more easily affected by the distribution form of the perturbed term sequence. In the part of the simulation research of this paper, first of all, we use finite samples to carry out data simulation experiments, and set the random disturbance terms in the process of data generation as standard Gao Si distribution, Poisson distribution and Skewed-t distribution, respectively. The generalized difference error distribution (GED) and Poisson Gaussian mixture distribution are analyzed and compared respectively by using these two cointegration test methods. Secondly, the distribution of random disturbance terms in the actual model may have "spikes". For the characteristics of "thick tail" and "biased", the GARCH model and the Realized GARCH model are established under the above five forms of distribution assumptions, respectively, and the non-stationary sequences are generated. Then the distortion level and efficacy level of cointegration test are studied by using JJ trace test and SVD rank test method. The following conclusions are obtained: under the assumption of non-Gao Si distribution, the distortion level of SVD rank test method is smaller than that of JJ cointegration test method. Test efficacy level is larger and in Gao Si distribution under the assumption that the two performance is similar. When the Wild Bootstrap method is introduced into the two cointegration tests, the efficiency level of the two cointegration test methods is obviously improved, and the two cointegration rank test methods have the same effect. In the end, the VAR model is established by using the price level of Chinese crude oil and Dubai crude oil (the representative of Southeast Asian crude oil), and the above two cointegration test methods are used to analyze the model. The results show that both of them accept a hypothesis test of cointegration relationship.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F426.22

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