發(fā)布時(shí)間：2018-06-03 11:14

  本文選題：空間面板模型 + 殘差Bootstrap方法��； 參考：《華中科技大學(xué)》2016年碩士論文

【摘要】：空間計(jì)量經(jīng)濟(jì)學(xué)是研究如何處理截面自相關(guān)問題并對(duì)其進(jìn)行建模分析的理論,傳統(tǒng)的計(jì)量分析假定截面單元獨(dú)立同分布,這往往是不符合實(shí)際的。自Anselin(1988)的開創(chuàng)性文獻(xiàn)以來,空間計(jì)量經(jīng)濟(jì)學(xué)迅速發(fā)展,已經(jīng)成為了計(jì)量經(jīng)濟(jì)學(xué)領(lǐng)域的一個(gè)重要的分支學(xué)科,它也由單純處理區(qū)域經(jīng)濟(jì)學(xué)的問題而擴(kuò)展到了勞動(dòng)經(jīng)濟(jì)學(xué)、教育經(jīng)濟(jì)學(xué)以及金融學(xué)等多個(gè)重要的領(lǐng)域。本文將以實(shí)證研究中廣泛采用的空間面板模型為分析框架,在學(xué)習(xí)和理解基本的空間面板模型的設(shè)定與估計(jì)方法、空間自相關(guān)檢驗(yàn)等問題的基礎(chǔ)上,從空間面板模型的識(shí)別、應(yīng)用、估計(jì)這三個(gè)角度各選擇一個(gè)問題進(jìn)行深入分析與探討,力求完善現(xiàn)有的空間面板模型的分析框架,并嘗試解決中國(guó)經(jīng)濟(jì)的現(xiàn)實(shí)問題。本文首先回顧了空間計(jì)量經(jīng)濟(jì)學(xué)的基本理論,介紹了利用空間模型分析問題的必要性、空間權(quán)重矩陣的建立、空間面板模型的設(shè)定,從截面空間模型開始介紹了空間模型估計(jì)的矩方法與極大似然方法,介紹了空間面板模型的估計(jì)方法及應(yīng)注意的問題,簡(jiǎn)要梳理了空間相關(guān)性檢驗(yàn)的步驟和應(yīng)注意的問題。在識(shí)別部分,本文將Sargan(1964)提出的共同因子約束(COMFAC)檢驗(yàn)引入了空間面板杜賓模型(SDM)的識(shí)別問題中,探討了SDM模型與空間面板誤差模型(SEM)模型的識(shí)別問題。通過仿真我們發(fā)現(xiàn),基于漸近臨界值的Wald檢驗(yàn)雖然有著良好的檢驗(yàn)功效,但卻存在著較為嚴(yán)重的尺度扭曲。采用殘差Bootstrap方法能夠有效解決這一問題。在應(yīng)用部分,本文詳細(xì)收集并測(cè)算了中國(guó)分省域1997-2012年的碳排放數(shù)據(jù),構(gòu)建空間面板模型分析了產(chǎn)業(yè)結(jié)構(gòu)、能源結(jié)構(gòu)和技術(shù)因素對(duì)分省域人均碳排放的影響因素和空間效應(yīng),通過識(shí)別檢驗(yàn)發(fā)現(xiàn)建立SEM模型是最優(yōu)的。實(shí)證分析的結(jié)果充分表明,在碳減排過程中要加強(qiáng)區(qū)域合作,要特別重視改善能源消費(fèi)結(jié)構(gòu)。在估計(jì)部分,我們以在微觀計(jì)量經(jīng)濟(jì)學(xué)中廣泛存在的短面板數(shù)據(jù)結(jié)構(gòu)為框架,分析了短動(dòng)態(tài)面板SEM模型的估計(jì)問題。我們給出了針對(duì)這個(gè)模型的三步系統(tǒng)廣義矩(GMM)估計(jì)方法,并與擬極大似然估計(jì)(QMLE)估計(jì)方法的有限樣本性質(zhì)進(jìn)行了比較。通過仿真我們發(fā)現(xiàn),兩種估計(jì)方法在不同情形下的表現(xiàn)各有優(yōu)劣,但在一般情形下,QMLE的有限樣本表現(xiàn)更好。
[Abstract]:Spatial econometrics is a theory to study how to deal with cross-section autocorrelation and to model and analyze it. The traditional econometric analysis assumes that the cross-section units are distributed independently, which is often not in line with the reality. Since the pioneering literature of Anselin (1988), spatial econometrics has developed rapidly and has become an important branch of the econometrics field. It has also expanded from simply dealing with the problems of regional economics to labor economics. There are many important fields such as educational economics and finance. In this paper, the spatial panel model, which is widely used in the empirical research, is used as the analysis framework. Based on the study and understanding of the basic spatial panel model setting and estimation methods, spatial autocorrelation test and so on, the paper will identify the spatial panel model. It is estimated that each of the three angles should choose one problem for further analysis and discussion, and try to improve the existing analysis framework of spatial panel model and try to solve the real problems of China's economy. This paper first reviews the basic theory of spatial econometrics, introduces the necessity of using spatial model to analyze the problem, the establishment of spatial weight matrix, and the setting of spatial panel model. This paper introduces the moment method and maximum likelihood method of spatial model estimation, introduces the estimation method of spatial panel model and the problems that should be paid attention to, and briefly combs the steps of spatial correlation test and the problems that should be paid attention to. In the recognition part, the common factor constraint (COMFAC) test proposed by Sargan-1964 is introduced into the recognition problem of the spatial panel Dobbin model, and the recognition problem of the SDM model and the spatial panel error model is discussed. The simulation results show that although the Wald test based on asymptotic critical value has good performance, it has serious scale distortion. The residual Bootstrap method can effectively solve this problem. In the application part, the paper collects and calculates the carbon emission data in China from 1997 to 2012 in detail, and constructs a spatial panel model to analyze the influence factors and spatial effects of industrial structure, energy structure and technology factors on per capita carbon emissions. The identification test shows that the SEM model is optimal. The results of empirical analysis show that regional cooperation should be strengthened and energy consumption structure should be improved in the process of carbon reduction. In the estimation part, we analyze the estimation problem of the short dynamic panel SEM model based on the short panel data structure, which is widely used in microeconometrics. In this paper, we give the generalized moment GMMs estimation method for the three-step system, and compare the finite sample properties of the QMLE-based estimator with the quasi-maximum likelihood estimator. The simulation results show that the two estimation methods have their own advantages and disadvantages in different cases, but in general, the finite samples of QMLE are better.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F224

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