【摘要】：非平穩(wěn)面板數(shù)據(jù)研究是目前計(jì)量經(jīng)濟(jì)學(xué)領(lǐng)域中的前沿問(wèn)題,其中,面板單位根和協(xié)整研究,作為時(shí)間序列單位根與傳統(tǒng)協(xié)整理論在面板數(shù)據(jù)中的發(fā)展和延伸,更具有重要意義。由于全球國(guó)際趨勢(shì)和國(guó)際經(jīng)濟(jì)周期等共同驅(qū)動(dòng)的影響,宏觀經(jīng)濟(jì)、管理或金融面板數(shù)據(jù)尤其是國(guó)家(地區(qū)或個(gè)體單元)的面板數(shù)據(jù)之間通常存在截面相依特征,因此,考慮截面相依假設(shè)條件的面板協(xié)整更加符合實(shí)際應(yīng)用背景,也是面板數(shù)據(jù)研究中亟待解決的一個(gè)熱點(diǎn)問(wèn)題。與傳統(tǒng)的面板協(xié)整不同,本文針對(duì)具有截面相依條件的面板協(xié)整進(jìn)行研究,在貝葉斯理論框架中,假設(shè)各個(gè)截面?zhèn)�體具有截面相依特征,結(jié)合貝葉斯分位回歸估計(jì)方法,提出了面板數(shù)據(jù)的貝葉斯分位協(xié)整模型。貝葉斯分位協(xié)整模型可以充分發(fā)揮貝葉斯方法考慮了參數(shù)不確定性風(fēng)險(xiǎn)的優(yōu)勢(shì),并且體現(xiàn)了分位回歸方法不僅可以刻畫(huà)響應(yīng)變量的中心趨勢(shì),還可以刻畫(huà)變量尾部行為的優(yōu)點(diǎn),從而為更全面地刻畫(huà)響應(yīng)變量與協(xié)變量的長(zhǎng)期均衡關(guān)系提供了方法和工具支撐,在理論上擴(kuò)展面板協(xié)整的研究方法和研究視角,在實(shí)踐上為經(jīng)濟(jì)管理問(wèn)題的定量分析和決策提供技術(shù)支持和有力依據(jù)。 針對(duì)面板數(shù)據(jù)之間通常存在截面相依性,首先應(yīng)用動(dòng)態(tài)公共因子結(jié)構(gòu)刻畫(huà)面板數(shù)據(jù)的截面相依特征,結(jié)合貝葉斯決策理論,提出一類(lèi)考慮了截面相依假設(shè)條件的協(xié)整模型,利用貝葉斯分位回歸方法,通過(guò)把非對(duì)稱(chēng)Laplace分布表示成指數(shù)分布和正態(tài)分布的線性組合,獲得了條件分位函數(shù)后驗(yàn)估計(jì)量的解析表達(dá)形式,并設(shè)計(jì)Kalman濾波與Gibbs抽樣算法對(duì)模型參數(shù)進(jìn)行估計(jì)和協(xié)整檢驗(yàn)。同時(shí),Monte Carlo仿真實(shí)驗(yàn)結(jié)果表明,貝葉斯分位協(xié)整可以更加全面地對(duì)變量間的協(xié)整關(guān)系進(jìn)行判斷。 經(jīng)濟(jì)金融變量因?yàn)閼?zhàn)爭(zhēng),政府政策以及自然災(zāi)害等因素的影響,往往表現(xiàn)出結(jié)構(gòu)突變性,這種結(jié)構(gòu)性變化的發(fā)生會(huì)影響傳統(tǒng)線性協(xié)整檢驗(yàn)的判斷。放松線性假設(shè)條件,本文提出一類(lèi)考慮了結(jié)構(gòu)變化特征的面板協(xié)整模型—平滑變結(jié)構(gòu)面板協(xié)整模型,利用傅立葉級(jí)數(shù)展開(kāi)形式來(lái)刻畫(huà)變結(jié)構(gòu)特征,并采用去除截面均值的方法消除面板數(shù)據(jù)的截面相依性,以避免參數(shù)過(guò)多的問(wèn)題,進(jìn)而結(jié)合貝葉斯分位回歸方法得到相應(yīng)條件分位函數(shù)后驗(yàn)估計(jì)量的解析形式,并設(shè)計(jì)MCMC抽樣算法對(duì)模型進(jìn)行參數(shù)后驗(yàn)估計(jì)和協(xié)整檢驗(yàn)。仿真實(shí)驗(yàn)結(jié)果表明,貝葉斯分位變結(jié)構(gòu)協(xié)整能夠有效全面地刻畫(huà)各個(gè)分位水平下的變結(jié)構(gòu)長(zhǎng)期關(guān)系。 與變結(jié)構(gòu)協(xié)整不同,門(mén)限協(xié)整主要研究協(xié)整回歸模型是線性,而其相應(yīng)的誤差修正項(xiàng)是非對(duì)稱(chēng)時(shí)的情形。針對(duì)傳統(tǒng)門(mén)限協(xié)整模型由于似然函數(shù)具有多峰、不連續(xù)特征,導(dǎo)致冗余參數(shù)識(shí)別存在困難,最優(yōu)化計(jì)算相對(duì)復(fù)雜的問(wèn)題,本文從貝葉斯的角度出發(fā),提出面板數(shù)據(jù)的貝葉斯分位門(mén)限協(xié)整模型,通過(guò)去除截面均值以消除面板數(shù)據(jù)間潛在的相依性,并對(duì)參數(shù)的先驗(yàn)分布進(jìn)行靈敏度分析以選擇合適的參數(shù)先驗(yàn),結(jié)合貝葉斯分位回歸方法對(duì)面板門(mén)限協(xié)整模型進(jìn)行參數(shù)估計(jì),得到條件分位函數(shù)后驗(yàn)估計(jì)量的解析表達(dá)式,同時(shí),利用MCMC算法對(duì)協(xié)整模型的參數(shù)進(jìn)行估計(jì),計(jì)算出協(xié)整檢驗(yàn)的后驗(yàn)概率以進(jìn)行更加全面的門(mén)限協(xié)整檢驗(yàn)。 將上述考慮了面板數(shù)據(jù)截面相依特征的貝葉斯分位協(xié)整方法應(yīng)用到原油與股票市場(chǎng)的關(guān)系研究中,并與傳統(tǒng)面板協(xié)整方法進(jìn)行比較,發(fā)現(xiàn)貝葉斯分位協(xié)整方法對(duì)原油與股票市場(chǎng)之間聯(lián)動(dòng)性關(guān)系的刻畫(huà)更加全面,驗(yàn)證了貝葉斯分位協(xié)整方法的可行性和有效性,說(shuō)明貝葉斯分位方法能夠提供全方面的便捷的模型參數(shù)估計(jì)和協(xié)整檢驗(yàn)信息。
[Abstract]:Nonstationary panel data is a frontier issue in econometrics. Among them, panel unit root and cointegration, as the development and extension of time series unit root and traditional cointegration theory in panel data, are of great significance. The cross-sectional dependence between economic, management or financial panel data, especially the panel data of a country (region or individual unit), is a common feature. Therefore, panel co-integration considering the assumption of cross-sectional dependence is more suitable for practical application and is also a hot issue in panel data research. In this paper, we study the panel co-integration with cross-section dependence. In the Bayesian framework, we assume that each section has cross-section dependence characteristics. Combined with Bayesian quantile regression estimation method, we propose a Bayesian quantile co-integration model for panel data. Bayesian quantile co-integration model can give full play to Bayesian method. The advantages of parametric uncertainties and the advantages of quantile regression not only can depict the central trend of the response variables, but also can depict the tail behavior of the variables are illustrated. The method and tool support are provided for describing the long-term equilibrium relationship between the response variables and the covariates more comprehensively, and the research on Panel Cointegration is expanded theoretically. Research methods and research perspectives, in practice, provide technical support and strong basis for quantitative analysis and decision-making of economic management issues.
Aiming at the cross-section dependence between panel data, a class of co-integration model considering the assumption of cross-section dependence is proposed by using the cross-section dependence characteristics of dynamic common factor structural panel data and Bayesian decision theory. The asymmetric Laplace distribution is expressed as exponential by Bayesian quantile regression method. The linear combination of distribution and normal distribution obtains the analytical expression of conditional quantile function posterior estimator, and designs Kalman filter and Gibbs sampling algorithm to estimate and test the model parameters. At the same time, Monte Carlo simulation results show that Bayesian quantile co-integration can be more comprehensive to the co-integration relationship between variables. Make a judgement.
Economic and financial variables often exhibit structural catastrophe because of war, government policies and natural disasters. The occurrence of such structural changes will affect the judgment of traditional linear cointegration test. In the co-integration model, the Fourier series expansion is used to characterize the variable structure features, and the cross-section dependence of panel data is eliminated by removing the cross-section mean, so as to avoid the problem of too many parameters. The simulation results show that Bayesian fractional variable structure co-integration can effectively and comprehensively describe the long-term relationship of the variable structure at each fractional level.
Unlike variable structure co-integration, threshold co-integration mainly studies the case when the co-integration regression model is linear and the error correction term is asymmetric. In this paper, a Bayesian thresholding co-integration model for panel data is proposed. The potential dependence between panel data is eliminated by removing the cross-sectional mean, and the prior distribution of parameters is analyzed to select the appropriate prior parameters. The parameters of the model are estimated by Bayesian quantile regression method. At the same time, the MCMC algorithm is used to estimate the parameters of the co-integration model, and the posterior probability of the co-integration test is calculated to conduct a more comprehensive threshold co-integration test.
The Bayesian fractional cointegration method considering the cross-sectional dependence of panel data is applied to the study of the relationship between crude oil and stock market. Compared with the traditional panel cointegration method, it is found that the Bayesian fractional cointegration method is more comprehensive in describing the linkage relationship between crude oil and stock market, which verifies the Bayesian fractional cointegration. The feasibility and validity of the whole method show that Bayesian grading method can provide all-round and convenient information of model parameter estimation and co-integration test.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F831.51;F416.22;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 劉金全;劉志剛;;具有Markov區(qū)制轉(zhuǎn)移的向量誤差修正模型及其應(yīng)用[J];管理科學(xué)學(xué)報(bào);2006年05期

2 張曉峒;王貴鵬;聶巧平;;一般序列相關(guān)下面板虛假回歸研究——估計(jì)量的漸進(jìn)分布和小樣本性質(zhì)[J];南開(kāi)經(jīng)濟(jì)研究;2006年02期

3 劉印旭,張世英;基于門(mén)限協(xié)整系統(tǒng)的預(yù)測(cè)方法研究[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2005年08期

4 吳強(qiáng);彭方平;;動(dòng)態(tài)門(mén)檻面板模型及我國(guó)經(jīng)濟(jì)增長(zhǎng)收斂性研究[J];統(tǒng)計(jì)研究;2007年06期

5 歐陽(yáng)志剛;;協(xié)整平滑轉(zhuǎn)移回歸中的線性檢驗(yàn)——基于完全修正最小二乘法的擴(kuò)展[J];統(tǒng)計(jì)研究;2010年03期

6 林謙;黃浩;黎實(shí);;考慮截面相關(guān)條件下的異質(zhì)性面板數(shù)據(jù)協(xié)整回歸模型的估計(jì)[J];統(tǒng)計(jì)研究;2010年09期

7 羅幼喜;田茂再;;面板數(shù)據(jù)的分位回歸方法及其模擬研究[J];統(tǒng)計(jì)研究;2010年10期

8 楊寶臣,張世英;變結(jié)構(gòu)協(xié)整問(wèn)題研究[J];系統(tǒng)工程學(xué)報(bào);2002年01期

9 王少平;歐陽(yáng)志剛;;中國(guó)城鄉(xiāng)收入差距對(duì)實(shí)際經(jīng)濟(jì)增長(zhǎng)的閾值效應(yīng)[J];中國(guó)社會(huì)科學(xué);2008年02期

，

本文編號(hào)：2195588