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基于貝葉斯動態(tài)面板數(shù)據(jù)模型的中國出口貿易地區(qū)結構研究

2014-06-28 07:27:36朱慧明歐陽文靜游萬海??
財經理論與實踐 2014年2期
關鍵詞:匯率波動

朱慧明++歐陽文靜++游萬海??

摘 要:運用貝葉斯動態(tài)面板數(shù)據(jù)模型考量中國出口貿易的地區(qū)結構差異性,結果表明:基于Gibbs抽樣算法的貝葉斯動態(tài)面板回歸模型能有效刻畫各地區(qū)出口競爭力的路徑依賴特征;FDI對出口競爭力的影響具有一定的時滯性,而匯率波動對出口競爭力的影響則具有明顯的地域差異性。

關鍵詞: 出口貿易;動態(tài)隨機效應;貝葉斯分析;FDI;匯率波動

中圖分類號:F746.12;O212.8 文獻標識碼: A 文章編號:1003-7217(2014)02-0109-07

一、引 言

面板數(shù)據(jù)由Mundlak引入到計量經濟學研究中,結合了截面數(shù)據(jù)和時間序列數(shù)據(jù)的優(yōu)點,通過截距項刻畫個體差異在數(shù)據(jù)調整過程中的動態(tài)變化,有效減少了數(shù)據(jù)生成過程中由于加總產生的偏誤,充分利用更多數(shù)據(jù)的信息,提高參數(shù)估計的有效性和準確性[1-2]。Balestra和Nerlove發(fā)現(xiàn)大量經濟變量表現(xiàn)出動態(tài)滯后效應,即經濟變量數(shù)據(jù)除了受當期因素的影響,還會受到非本期因素的影響。為刻畫動態(tài)滯后效應,在靜態(tài)面板數(shù)據(jù)模型中引入滯后被解釋變量,構建動態(tài)面板數(shù)據(jù)模型。該模型可以用于描述多個經濟變量之間的動態(tài)關系,被廣泛應用于金融、經濟、管理等領域[3]。Egger P建立動態(tài)面板數(shù)據(jù)模型發(fā)現(xiàn)雙邊貿易和FDI之間的關系互補[4-5]。Chiara認為垂直型FDI把生產的不同階段放到不同國家,由此帶動中間投入品的進口和產成品的出口[6]。Marc Auboin用最大似然估計法發(fā)現(xiàn),匯率的不確定性對出口有顯著的負面影響[7]。Tang Kin Boon用異質面板協(xié)整檢驗驗證了出口需求函數(shù)的變量之間的協(xié)整關系,研究發(fā)現(xiàn)外債對出口的影響因貨幣貶值的幅度而改變[8,9]。Ciarret構建面板數(shù)據(jù)模型研究發(fā)現(xiàn)能源價格和GDP電力消費有雙向因果關系[10]。為揭示經濟學理論的動態(tài)關系,一些學者研究動態(tài)面板數(shù)據(jù)模型的參數(shù)估計問題,提出三種主要的參數(shù)估計方法:廣義矩(Generalized Methods of Moments,GMM)方法,校正的最小二乘虛擬變量估計法(Least Squares with Dummy Variable,LSDV)和層次貝葉斯估計法。Anderson 使用工具變量方法對含有一階差分變量的動態(tài)面板模型進行估計,得到了模型參數(shù)的一致性估計[11]。但工具變量法在估計時忽視了隨機誤差項的結構,因此估計量不具有有效性。Holtz-Eakin在Anderson工具變量估計法的基礎上,研究了時變參數(shù)的向量自回歸模型的參數(shù)估計問題[12]。Arrelano將一階差分廣義矩(GMM)估計方法引入到動態(tài)面板數(shù)據(jù)的估計中[13]。Maurice用局內變量或者前定變量的滯后值作為工具變量來估計參數(shù)[14],提高了模型參數(shù)估計的有效性。HsinChen研究GMM估計量的大樣本性質,發(fā)現(xiàn)當動態(tài)面板數(shù)據(jù)模型中存在高度序列自相關性時,GMM估計量是有偏差的[15]。Hahn采用校正的最小二乘虛擬變量估計量去估計參數(shù),發(fā)現(xiàn)當時間維數(shù)比較小時,校正的最小二乘虛擬變量估計量比GMM方法更精確[16]。Giovanni針對模型存在的個體異質性提出廣義最小二乘法,并通過蒙特卡洛研究發(fā)現(xiàn)廣義最小二乘估計有最小的偏差和均方根誤差[17]。

貝葉斯統(tǒng)計推斷技術特別是馬爾科夫鏈蒙特卡洛(MCMC)穩(wěn)態(tài)模擬技術的發(fā)展,為動態(tài)面板數(shù)據(jù)模型的研究提供有效的途徑[18,19]。Hsiao 指出,使用層次貝葉斯方法估計隨機效應自回歸面板數(shù)據(jù)模型時,在大面板數(shù)據(jù)條件下,貝葉斯估計量與組平均估計量漸進等價。貝葉斯分析方法在動態(tài)面板數(shù)據(jù)模型參數(shù)估計的應用,可以避免GMM估計方法和最小二乘虛擬變量估計法存在的參數(shù)估計不準確、有偏的問題[20]。

本文將利用貝葉斯統(tǒng)計推斷理論,構建含外生變量的動態(tài)隨機效應面板模型,通過參數(shù)的分層先驗分布研究貝葉斯模型參數(shù)的后驗分布(posterior distribution),設計相應的馬爾科夫蒙特卡洛(Markov chain Monte Carlo,簡稱MCMC)抽樣算法進行模型參數(shù)估計,并對中國出口貿易地區(qū)結構的差異性進行實證分析。

二、貝葉斯動態(tài)面板數(shù)據(jù)模型構建

(一)模型結構

動態(tài)面板模型是一類重要的經濟計量模型,其數(shù)學表達形式如下:

四、結 論

本文應用動態(tài)面板數(shù)據(jù)模型研究我國地區(qū)出口貿易結構差異性,結果表明,出口貿易競爭力具有很強的路徑依賴特征。因此要縮小地區(qū)發(fā)展差距需要一個長期的過程,需要不斷引導外商直接投資向中西部地區(qū)轉移,加強和完善中西部地區(qū)的優(yōu)惠政策和基礎設施建設。同時,F(xiàn)DI的流入在促進出口時存在一定的時滯性,即FDI的流入須經過早期投入和生產過程后才能促進出口。從地域差別的角度來看,F(xiàn)DI的流入加劇了地區(qū)出口貿易發(fā)展的不平衡,說明東部、中部和西部地區(qū)出口貿易競爭力對人民幣匯率波動的敏感性存在顯著差異,即越開放的地區(qū)對人民幣匯率波動越敏感。

參考文獻:

[1]Rahman, Sajjadur & Serletis, Apostolos. The effects of exchange rate uncertainty on exports [J]. Journal of Macroeconomics, 2009, 31(3):500-507.

[2]Rfat Bar Tekin. Economic growth, exports and foreign direct investment in least developed countries:a panel granger causality analysis [J]. Economic Modelling, 2012, 29(3): 868-878.

[3]Balestra P, M Nerlove. Pooling cross section and time series data in the estimation of a dynamie model: the demand for natural Gas [J]. Econometrica, 1966, 34(3): 585-612.

[4]Egger P. European exports and outward foreign direct investment: a dynamic panel data approach [J]. Review of World Economics, 2009, 37(3): 427-449.

[5]蔣仁愛, 馮根福. 貿易、FDI、無形技術外溢與中國技術進步 [J]. 管理世界, 2012, 15(9): 49-60.

[6]Chiara Franco. Exports and FDI motivations:empirical evidence from US foreign subsidiaries [J]. International Business Review, 2013, 22(4): 47-62.

[7]Marc Auboin, Michel Ruta. The relationship between exchange rates and international trade: a literature review [J]. World Trade Review, 2012, 12(3): 60-77.

[8]Tang Kin Boon, Tan Hui Boon. The effect of foreign currency borrowing and financial development on exports: a dynamic panel analysis on asiapacific countries [J]. Journal of the Asia Pacific Economy, 2012, 18(3): 460-476.

[9]Tsay R S, Ando T. Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market [J]. Computational Statistics and Data Analysis, 2012, 56(11): 3345-3365.

[10]A Ciarret, A Zarraga. Economic growthelectricity consumption causality in 12 european countries: a dynamic panel data approach [J]. Energy Policy, 2010, 38(7): 3790-3796.

[11]Anderson T W, Hsiao C. Formulation and estimation of dynamic models using panel data [J]. Joumal of Econometrics, 1982, 18(3): 47-82.

[12]Holtz Eakin D W, Neweyand H S. Estimating vector autoregressions with panel data [J]. Econometrica, 1988, 56(10): 1371-1395.

[13]Arrelano M, S R Bond. Another look at the instrumental variable estimation of errorcomponents models [J]. Journal of Econometrics, 1995, 68(3): 29-51.

[14]Maurice J G, Frank Windmeijer. The weak instrument problem of the system GMM estimator in dynamic panel data models [J]. The Econometrics Journal, 2010, 13(1): 95-126.

[15]Hsin Chen Chang, BwoNung Huang, Chin Wei Yang. Military expenditure and economic growth across different groups:a dynamic panel grangercausality approach [J]. Economic Modelling, 2011, 28(6): 416-423.

[16]Hahn J, Kuersteiner G. Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large [J]. Econometrica, 2002, 70(5): 639-657.

[17]Giovanni S, Bruno. Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models [J]. Economics Letters, 2005, 87(3): 361-366.

[18]李鯤鵬, 文益俊. 交互效應面板模型EM算法和MCMC算法 [J]. 數(shù)理統(tǒng)計與管理, 2012, 29(4): 150-161.

[19]吳俊, 賓建成. 中國商業(yè)銀行操作風險損失分布甄別與分析: 基于貝葉斯MCMC頻率方法 [J]. 財經理論與實踐,2011,32(5):8-14.

[20]Cheng Hsiao, A K Tahmiscioglu. Estimation of dynamic panel data models with both individual and timespecific effects [J]. Journal of Statistical Planning and Inference, 2008, 138(9): 698-721.

(責任編輯:姚德權)

Regional Structure of Chinese Export Trade Based on Bayesian Dynamic Panel Data Model

ZHU Huiming,OU YANG Wenjing,YOU Wanhai

. (School of Business Administration,Hunan University,Changsha 410082,China).

Abstract:This paper constructs a Bayesian dynamic panel data model to address uncertain risk of inference in random coefficient, sets the corresponding prior distribution, and uses the Bayesian theorem to infer the posterior distribution of the parameters. In order to investigate the regional structure of China's export trade, we calculate the indicators export competitiveness (ECI) to measure the regional structure of China's export trade, and then conduct an empirical analysis. The results show that the Bayesian dynamic panel regression model based on Gibbs sampling algorithm can effectively capture the characteristics of the path dependence in export competitiveness of all regions. In addition, the effect of the FDI on the export competitiveness will have a lag, while the impact of the exchange rate fluctuations on the export competitiveness has obvious regional differences.

Key words:Export Trade;Dynamic Random Effects;Bayesian Analysis;Foreign Direct Investment (FDI);Exchange Rate Fluctuation

[20]Cheng Hsiao, A K Tahmiscioglu. Estimation of dynamic panel data models with both individual and timespecific effects [J]. Journal of Statistical Planning and Inference, 2008, 138(9): 698-721.

(責任編輯:姚德權)

Regional Structure of Chinese Export Trade Based on Bayesian Dynamic Panel Data Model

ZHU Huiming,OU YANG Wenjing,YOU Wanhai

. (School of Business Administration,Hunan University,Changsha 410082,China).

Abstract:This paper constructs a Bayesian dynamic panel data model to address uncertain risk of inference in random coefficient, sets the corresponding prior distribution, and uses the Bayesian theorem to infer the posterior distribution of the parameters. In order to investigate the regional structure of China's export trade, we calculate the indicators export competitiveness (ECI) to measure the regional structure of China's export trade, and then conduct an empirical analysis. The results show that the Bayesian dynamic panel regression model based on Gibbs sampling algorithm can effectively capture the characteristics of the path dependence in export competitiveness of all regions. In addition, the effect of the FDI on the export competitiveness will have a lag, while the impact of the exchange rate fluctuations on the export competitiveness has obvious regional differences.

Key words:Export Trade;Dynamic Random Effects;Bayesian Analysis;Foreign Direct Investment (FDI);Exchange Rate Fluctuation

[20]Cheng Hsiao, A K Tahmiscioglu. Estimation of dynamic panel data models with both individual and timespecific effects [J]. Journal of Statistical Planning and Inference, 2008, 138(9): 698-721.

(責任編輯:姚德權)

Regional Structure of Chinese Export Trade Based on Bayesian Dynamic Panel Data Model

ZHU Huiming,OU YANG Wenjing,YOU Wanhai

. (School of Business Administration,Hunan University,Changsha 410082,China).

Abstract:This paper constructs a Bayesian dynamic panel data model to address uncertain risk of inference in random coefficient, sets the corresponding prior distribution, and uses the Bayesian theorem to infer the posterior distribution of the parameters. In order to investigate the regional structure of China's export trade, we calculate the indicators export competitiveness (ECI) to measure the regional structure of China's export trade, and then conduct an empirical analysis. The results show that the Bayesian dynamic panel regression model based on Gibbs sampling algorithm can effectively capture the characteristics of the path dependence in export competitiveness of all regions. In addition, the effect of the FDI on the export competitiveness will have a lag, while the impact of the exchange rate fluctuations on the export competitiveness has obvious regional differences.

Key words:Export Trade;Dynamic Random Effects;Bayesian Analysis;Foreign Direct Investment (FDI);Exchange Rate Fluctuation

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