本文選題：單位根檢驗 + 經驗似然比統(tǒng)計量��； 參考：《浙江工商大學》2017年碩士論文

【摘要】：單位根檢驗是計量經濟學中十分重要的研究內容之一。尤其在實際的金融時間序列多為非線性,并且大多數是含有單位根的非平穩(wěn)序列的背景下,平穩(wěn)性檢驗在研究時間序列數據中已是必不可少的一個步驟。但是傳統(tǒng)的單位根檢驗是基于線性模型提出的,針對現今眾多非線性模型的檢驗效果并不是十分有效,容易造成過分接受非平穩(wěn)的假設,從而引起誤判�；诖�,本文針對實際應用較多的非線性STAR模型進行了相應的單位根檢驗研究�？紤]到STAR模型在實際擬合時間序列數據時,模型的殘差項常服從GARCH過程,因此本文在前人的基礎上構建了檢驗似然比檢驗統(tǒng)計量l(δ)。似然比檢驗統(tǒng)計量極大的提高了 STAR模型的單位根檢驗功效,并且與汪盧俊(2014)提出的針對LSTAR-GARCH模型的單位根檢驗統(tǒng)計量tNG相比,避免了計算估計方差,有效的降低了計算復雜度,提高了估計統(tǒng)計量的穩(wěn)定性。本文首先是對單位根檢驗的歷史和理論進行了介紹,然后基于汪盧俊(2014)提出的針對LSTAR-GARCH模型的單位根檢驗統(tǒng)計量tNG,在文中第三章創(chuàng)造性的提出了基于LSTAR-GARCH模型的經驗似然比檢驗統(tǒng)計量l(δ),并推導出其極限分布。其中關于時間序列的條件方差時變性特征(GARCH項),tNG的極限分布在推導過程中需要考慮到tNG的估計方差,這樣會增加tNG的不穩(wěn)定性和計算復雜度,而經驗似然比檢驗統(tǒng)計量可以有效地避免計算統(tǒng)計量的估計方差,從而提高單位根檢驗的效果。為了驗證第三章中的理論,本文第四章通過蒙特卡羅和Bootstrap方法進行模擬和功效比較,在模擬的角度進一步的說明這一情況。更進一步,第五章結合我國上證指數股票數據進行實證分析,通過擬合情況來比較,說明使用經驗似然比檢驗統(tǒng)計量檢驗,構建的STAR模型最為準確,能夠為投資者提供更可靠的信息。
[Abstract]:Unit root test is one of the most important research contents in econometrics. Especially under the background that the actual financial time series are mostly nonlinear and most of them are non-stationary sequences with unit roots, the stationary test is an essential step in the study of time series data. However, the traditional unit root test is based on the linear model. The test effect for many nonlinear models is not very effective. It is easy to overaccept the assumption of non-stationary, thus causing misjudgment. Based on this, this paper studies the unit root test of nonlinear STAR model which is widely used in practice. Considering that the residual term of the STAR model is usually followed by the GARCH process when the time series data are fitted, the test likelihood ratio test statistic L (未) is constructed on the basis of previous studies. Likelihood ratio test statistics greatly improve the efficiency of unit root test of STAR model, and compared with the unit root test statistic tNG for LSTAR-GARCH model proposed by Wang Lujun 2014, it avoids the estimated variance and reduces the computational complexity effectively. The stability of estimation statistics is improved. This paper first introduces the history and theory of unit root test. Then, based on the unit root test statistic for LSTAR-GARCH model proposed by Wang Lujun (2014), the empirical likelihood ratio test statistic based on LSTAR-GARCH model is creatively proposed in chapter 3, and its limit distribution is deduced. For the conditional variance of time series, it is necessary to take into account the estimated variance of tNG in the derivation of the limit distribution of the term GARCH, which will increase the instability and computational complexity of tNG. The empirical likelihood ratio test statistics can effectively avoid calculating the estimated variance of the statistics and thus improve the effect of unit root test. In order to verify the theory in the third chapter, the fourth chapter of this paper uses Monte Carlo and Bootstrap methods to simulate and compare the effectiveness of the simulation, in order to further explain this situation in the perspective of simulation. Furthermore, the fifth chapter combines the stock data of Shanghai Stock Exchange of China to carry on the empirical analysis, through the comparison of the fitting situation, shows that using the empirical likelihood ratio test statistic test, the STAR model is the most accurate. To provide investors with more reliable information.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F224;F832.51

【參考文獻】

相關期刊論文 前2條

1 ;Estimation for nearly unit root processes with GARCH errors[J];Applied Mathematics:A Journal of Chinese Universities(Series B);2010年03期

2 劉雪燕;張曉峒;;非線性LSTAR模型中的單位根檢驗[J];南開經濟研究;2009年01期

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本文編號：1836535