基于貝葉斯分位回歸理論的截面相依面板協(xié)整研究
發(fā)布時間：2018-08-21 11:35
【摘要】：非平穩(wěn)面板數(shù)據(jù)研究是目前計量經濟學領域中的前沿問題,其中,面板單位根和協(xié)整研究,作為時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。由于全球國際趨勢和國際經濟周期等共同驅動的影響,宏觀經濟、管理或金融面板數(shù)據(jù)尤其是國家(地區(qū)或個體單元)的面板數(shù)據(jù)之間通常存在截面相依特征,因此,考慮截面相依假設條件的面板協(xié)整更加符合實際應用背景,也是面板數(shù)據(jù)研究中亟待解決的一個熱點問題。與傳統(tǒng)的面板協(xié)整不同,本文針對具有截面相依條件的面板協(xié)整進行研究,在貝葉斯理論框架中,假設各個截面?zhèn)�體具有截面相依特征,結合貝葉斯分位回歸估計方法,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)不確定性風險的優(yōu)勢,并且體現(xiàn)了分位回歸方法不僅可以刻畫響應變量的中心趨勢,還可以刻畫變量尾部行為的優(yōu)點,從而為更全面地刻畫響應變量與協(xié)變量的長期均衡關系提供了方法和工具支撐,在理論上擴展面板協(xié)整的研究方法和研究視角,在實踐上為經濟管理問題的定量分析和決策提供技術支持和有力依據(jù)。 針對面板數(shù)據(jù)之間通常存在截面相依性,首先應用動態(tài)公共因子結構刻畫面板數(shù)據(jù)的截面相依特征,結合貝葉斯決策理論,提出一類考慮了截面相依假設條件的協(xié)整模型,利用貝葉斯分位回歸方法,通過把非對稱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)后驗估計量的解析表達形式,并設計Kalman濾波與Gibbs抽樣算法對模型參數(shù)進行估計和協(xié)整檢驗。同時,Monte Carlo仿真實驗結果表明,貝葉斯分位協(xié)整可以更加全面地對變量間的協(xié)整關系進行判斷。 經濟金融變量因為戰(zhàn)爭,政府政策以及自然災害等因素的影響,往往表現(xiàn)出結構突變性,這種結構性變化的發(fā)生會影響傳統(tǒng)線性協(xié)整檢驗的判斷。放松線性假設條件,本文提出一類考慮了結構變化特征的面板協(xié)整模型—平滑變結構面板協(xié)整模型,利用傅立葉級數(shù)展開形式來刻畫變結構特征,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)過多的問題,進而結合貝葉斯分位回歸方法得到相應條件分位函數(shù)后驗估計量的解析形式,并設計MCMC抽樣算法對模型進行參數(shù)后驗估計和協(xié)整檢驗。仿真實驗結果表明,貝葉斯分位變結構協(xié)整能夠有效全面地刻畫各個分位水平下的變結構長期關系。 與變結構協(xié)整不同,門限協(xié)整主要研究協(xié)整回歸模型是線性,而其相應的誤差修正項是非對稱時的情形。針對傳統(tǒng)門限協(xié)整模型由于似然函數(shù)具有多峰、不連續(xù)特征,導致冗余參數(shù)識別存在困難,最優(yōu)化計算相對復雜的問題,本文從貝葉斯的角度出發(fā),提出面板數(shù)據(jù)的貝葉斯分位門限協(xié)整模型,通過去除截面均值以消除面板數(shù)據(jù)間潛在的相依性,并對參數(shù)的先驗分布進行靈敏度分析以選擇合適的參數(shù)先驗,結合貝葉斯分位回歸方法對面板門限協(xié)整模型進行參數(shù)估計,得到條件分位函數(shù)后驗估計量的解析表達式,同時,利用MCMC算法對協(xié)整模型的參數(shù)進行估計,計算出協(xié)整檢驗的后驗概率以進行更加全面的門限協(xié)整檢驗。 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)整方法應用到原油與股票市場的關系研究中,并與傳統(tǒng)面板協(xié)整方法進行比較,發(fā)現(xiàn)貝葉斯分位協(xié)整方法對原油與股票市場之間聯(lián)動性關系的刻畫更加全面,驗證了貝葉斯分位協(xié)整方法的可行性和有效性,說明貝葉斯分位方法能夠提供全方面的便捷的模型參數(shù)估計和協(xié)整檢驗信息。
[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.
【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2012
【分類號】：F831.51;F416.22;F224
本文編號：2195588
[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.
【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2012
【分類號】：F831.51;F416.22;F224
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