本文選題：空間計(jì)量模型 + 空間權(quán)重矩陣�。� 參考：《江西財(cái)經(jīng)大學(xué)》2015年博士論文

【摘要】：空間計(jì)量模型的研究得到越來越被廣泛的重視。在現(xiàn)實(shí)世界中,觀測值實(shí)際上存在獨(dú)立和非獨(dú)立兩種可能,傳統(tǒng)的計(jì)量理論是建立在獨(dú)立觀測值假定基礎(chǔ)之上的,而地理區(qū)域空間之間及其經(jīng)濟(jì)現(xiàn)象之間的空間依賴性的存在打破了經(jīng)典計(jì)量經(jīng)濟(jì)學(xué)模型關(guān)于樣本相互獨(dú)立的基本假設(shè)。這時(shí)要準(zhǔn)確的提取這些數(shù)據(jù)的空間關(guān)系,恰當(dāng)?shù)拿枋龊瓦\(yùn)用空間特性對(duì)空間交互作用進(jìn)行研究尤為必要。事實(shí)上,空間單元上的某種經(jīng)濟(jì)現(xiàn)象或某一屬性總是與相鄰空間單元上的同現(xiàn)象或?qū)傩韵嚓P(guān),我們必須通過空間計(jì)量模型來分析這種關(guān)系。將空間效應(yīng)納入計(jì)量經(jīng)濟(jì)模型分析框架,將會(huì)面臨著三個(gè)重要問題。一是如何合理地將空間效應(yīng)引入既有的計(jì)量經(jīng)濟(jì)模型,或者根據(jù)空間效應(yīng)的特殊性構(gòu)造特定的計(jì)量經(jīng)濟(jì)模型；二是如何在具體的分析中恰當(dāng)?shù)剡x擇空間計(jì)量模型以及特定的空間計(jì)量經(jīng)濟(jì)模型如何估計(jì)；三是如何利用空間計(jì)量模型進(jìn)行規(guī)范的實(shí)證分析。第一個(gè)問題涉及到空間權(quán)重矩陣的合理設(shè)定。第二個(gè)問題涉及到空間計(jì)量模型的選擇方法與估計(jì)方法。第三個(gè)問題實(shí)際上是前兩個(gè)問題的解決方法與實(shí)證分析的結(jié)合。基于上面三個(gè)問題,本文從空間計(jì)量最為重要的研究對(duì)象——空間權(quán)重矩陣出發(fā),將經(jīng)典計(jì)量方法和MCMC方法相結(jié)合,分析了空間計(jì)量模型的選擇與帶未知異方差的廣義空間模型的有效估計(jì)方法,并給出了實(shí)證分析上的應(yīng)用。本文的研究結(jié)論如下：(1)空間權(quán)重矩陣是連接理論分析上的空間計(jì)量經(jīng)濟(jì)模型與真實(shí)世界中空間效應(yīng)的紐帶。能否構(gòu)建并選擇恰當(dāng)?shù)目臻g權(quán)重矩陣直接關(guān)系到模型的最終估計(jì)結(jié)果和解釋力。不同的空間權(quán)重矩陣反映的是研究對(duì)象背后不同的經(jīng)濟(jì)學(xué)原理與視角,同時(shí)也對(duì)應(yīng)著研究者對(duì)于空間效應(yīng)的不同認(rèn)識(shí)�？臻g權(quán)重矩陣的錯(cuò)誤選擇將會(huì)嚴(yán)重地干擾空間計(jì)量的各種檢驗(yàn)分析,從而對(duì)空間計(jì)量模型的進(jìn)一步研究產(chǎn)生很大影響。(2)空間計(jì)量模型選擇是空間計(jì)量分析的重要研究課題。Moran指數(shù)檢驗(yàn)、LM檢驗(yàn)、似然函數(shù)、三大信息準(zhǔn)則、貝葉斯后驗(yàn)概率、馬爾可夫鏈蒙特卡羅方法對(duì)空間計(jì)量模型選擇有很大的差異。通過模擬分析表明：在擴(kuò)充的空間計(jì)量模型簇中進(jìn)行模型選擇時(shí),基于OLS殘差的Moran指數(shù)與LM檢驗(yàn)均存在較大的局限性,對(duì)數(shù)似然值最大原則缺少區(qū)分度,LM檢驗(yàn)只針對(duì)SEM和SAR模型的區(qū)分有效,信息準(zhǔn)則對(duì)大多數(shù)模型有效,但是也會(huì)出現(xiàn)誤選。而當(dāng)給出恰當(dāng)?shù)腗-H算法時(shí),充分利用了似然函數(shù)和先驗(yàn)信息的MCMC方法,具有更高的檢驗(yàn)效度,特別是在較大的樣本條件下得到了較準(zhǔn)確的判斷。并且它對(duì)不同階空間鄰接矩陣的空間計(jì)量模型的選擇也非常有效。(3)空間異方差問題也是空間計(jì)量分析中的一個(gè)重要問題�？臻g單元大小以及其它的經(jīng)濟(jì)特征上的差異,常常會(huì)導(dǎo)致空間異方差問題。對(duì)于廣義空間模型包含異方差時(shí),估計(jì)方法相對(duì)復(fù)雜,本文給出了三種不同的估計(jì)方法。第一種方法是將異方差形式參數(shù)化,來克服自由度的不足,使用ML估計(jì)進(jìn)行實(shí)現(xiàn)。而針對(duì)異方差形式未知時(shí),分別采用了基于2SLS的迭代GMM估計(jì)和更加直接的MCMC抽樣方法加以解決,特別是MCMC方法表現(xiàn)得更加優(yōu)美。蒙特卡羅模擬表明,給定異方差形式條件下,ML估計(jì)通過異方差參數(shù)化的方法依然可以獲得較好的估計(jì)效果。而異方差形式未知的情況下,另外兩種方法隨著樣本數(shù)的增大時(shí)也可以與ML的估計(jì)結(jié)果趨于一致。(4)結(jié)合空間權(quán)重矩陣的分析、空間計(jì)量模型的選擇、帶未知異方差的廣義空間計(jì)量模型的估計(jì)和方向性距離函數(shù)GML超效率模型,對(duì)資源環(huán)境約束下我國省際全要素能源效率問題進(jìn)行研究。從實(shí)證的過程得出如下結(jié)論：在做全要素能源效率的測度時(shí)應(yīng)該考慮資源與環(huán)境的約束,這樣得出的結(jié)果才能更加符合我國的實(shí)際情況。進(jìn)而,在做省際全要素能源效率的影響因素分析時(shí),應(yīng)當(dāng)考慮空間效應(yīng)的影響,忽略空間效應(yīng)的影響將會(huì)得出有偏誤的估計(jì)。特別是在考慮空間效應(yīng)時(shí),還需要根據(jù)研究的空間計(jì)量理論與方法選擇恰當(dāng)?shù)目臻g權(quán)重矩陣和合適的空間計(jì)量模型,以及使用合理的模型估計(jì)方法。只有把這些過程合理地整合在一起,才能構(gòu)成全要素能源效率分析的一個(gè)完整框架體系。從實(shí)證分析的結(jié)果得出如下結(jié)論：資源環(huán)境約束下我國省際全要素能源效率持續(xù)走低,趨勢不容樂觀；資源和環(huán)境約束的條件下過多地依賴煤炭資源將會(huì)大大降低我國的能源效率,煤炭消費(fèi)所帶來的負(fù)面影響確實(shí)不容忽視；“污染天堂假說”在我國是成立的；服務(wù)業(yè)的比重增加是有利于能源效率總體上的提高；外資企業(yè)相對(duì)國內(nèi)來說會(huì)采用更加先進(jìn)的能源技術(shù),且對(duì)國內(nèi)企業(yè)存在正向溢出效應(yīng),對(duì)我國的能源效率存在正面影響。本文的整個(gè)研究具有一定的理論價(jià)值和實(shí)踐價(jià)值。首先,本文首次比較系統(tǒng)地研究了空間權(quán)重矩陣,且通過圖形作了一定程度的可視化分析,完善了空間權(quán)重矩陣的體系化認(rèn)識(shí)。在空間計(jì)量模型選擇方法的分析中給出了很少被使用而又很有效的MCMC方法。在空間計(jì)量模型的估計(jì)中,又研究了一種較為常見的空間計(jì)量模型——廣義空間計(jì)量模型,且在考慮帶未知異方差條件下給出了它的MCMC有效估計(jì)方法。最后都對(duì)抽象的理論分析都進(jìn)行了蒙特卡羅模擬,給出了方法的有效性對(duì)比。這些都對(duì)空間計(jì)量經(jīng)濟(jì)的實(shí)證分析提供了重要的參考。正是基于此,本文最后結(jié)合理論分析進(jìn)行了實(shí)證研究,整個(gè)實(shí)證過程做到了規(guī)范嚴(yán)謹(jǐn)。
[Abstract]:In the real world, there are two possibilities of independent and non independent observational values in the real world. The traditional econometric theory is based on the assumption of independent observational values, and the existence of spatial dependence between geographical space and its economic image has broken the classic. The basic assumption that the econometric model is independent of each other is necessary. It is necessary to accurately extract the spatial relations of these data, to describe and apply the spatial characteristics properly to study the spatial interaction. In fact, some economic phenomena or some attributes on the space unit are always the same as those on the adjacent space units. We have to analyze this relationship through the spatial econometric model. It will be faced with three important problems to incorporate the spatial effect into the econometric model analysis framework. One is how to rationally introduce the spatial effect into the existing econometric model, or to construct a specific econometric model according to the particularity of the spatial effect. The two is how to choose the spatial econometric model properly and how to estimate the specific spatial econometric model in the specific analysis. Three is how to use the spatial econometric model to carry out the normative empirical analysis. The first problem involves the rational setting of the spatial weight matrix. The second questions involve the selection of the spatial econometric model. In fact, the third problem is the combination of the solution method and the empirical analysis of the first two problems. Based on the above three problems, this paper, based on the space weighting matrix, combines the classical measurement method and the MCMC method, and analyzes the selection and unknowns of the spatial econometric model. The effective estimation method of the generalized spatial model of heteroscedasticity is given and the application of the empirical analysis is given. The conclusions of this paper are as follows: (1) the spatial weight matrix is the link between the spatial econometric model and the real world space effect in the theoretical analysis. The different spatial weight matrix reflects the different economic principles and perspectives behind the research object, and also corresponds to the different understanding of the spatial effect. The error selection of the spatial weight matrix will seriously interfere with the various tests and analyses of the space measure, thus to the space measurement. The further research of the model has great influence. (2) the selection of spatial econometric model is an important research topic of spatial econometric analysis:.Moran index test, LM test, likelihood function, three information criterion, Bayesian posteriori probability, Markov chain Monte Carlo method, which has great difference in the selection of spatial calculation model. The Moran index and LM test based on the OLS residuals have great limitations in the selection of the extended spatial econometric model clusters. The maximum principle of the logarithmic likelihood is lacking, and the LM test is only effective for the SEM and SAR models. When the M-H algorithm takes full advantage of the MCMC method of likelihood function and prior information, it has a higher test validity, especially in the larger sample condition. And it is also very effective for the selection of spatial econometric models of different order space adjacency matrices. (3) spatial heteroscedasticity is also a spatial econometric problem. An important problem in the analysis is that the space element size and the difference in other economic characteristics often lead to the spatial heteroscedasticity problem. For the generalized spatial model including the heteroscedasticity, the estimation method is relatively complex. In this paper, three different estimation methods are given. The first method is to parameterize the form of Heteroscedasticity to overcome the degree of freedom. The ML estimation is used to implement it. When the heteroscedasticity is unknown, the iterative GMM estimation based on 2SLS and the more direct MCMC sampling are used respectively, especially the MCMC method is more graceful. The Monte Carlo simulation shows that the ML estimation is parameterized by the heteroscedasticity under the condition of the given heteroscedasticity. Better estimation results can still be obtained. In the case of unknown variance, the other two methods can also be consistent with the estimated results of ML as the number of samples increase. (4) combining the analysis of the spatial weight matrix, the selection of the spatial econometric model, the estimation of the generalized spatial econometric model with the unknown ISO difference and the direction distance. The GML super efficiency model is used to study the energy efficiency of China's inter provincial total factor under the constraints of resource and environment. The following conclusions are drawn from the empirical process: the constraints of resources and environment should be considered when measuring the total factor energy efficiency, so that the results can be more consistent with the actual situation in China. In the analysis of factors affecting all factor energy efficiency, the effect of space effect should be considered. Ignoring the effect of space effect, there will be an error estimation. Especially when considering the spatial effect, the appropriate spatial weight matrix and suitable spatial measurement model should be selected according to the spatial measurement theory and method. The rational model estimation method. Only by integrating these processes properly can we form a complete framework for the analysis of all factors energy efficiency. From the results of empirical analysis, the following conclusions are drawn: under the constraints of resource and environment, the energy efficiency of China's inter provincial total factor is low, the trend is not optimistic; resources and environmental constraints are restricted. The excessive dependence of coal resources under the conditions will greatly reduce the energy efficiency of our country. The negative effects of coal consumption can not be ignored; the hypothesis of "pollution paradise" is established in China; the increase in the proportion of service industry is beneficial to the overall improvement of energy efficiency; foreign enterprises will adopt more first than domestic. The forward energy technology has positive spillover effect on domestic enterprises and has a positive impact on energy efficiency in China. The whole study of this paper has some theoretical and practical value. First, this paper systematically studies the spatial weight matrix for the first time, and has made a certain degree of visualization analysis through the graphics, and improved the space. The systematic understanding of the weight matrix. In the analysis of the selection method of the spatial econometric model, the MCMC method which is rarely used but very effective is given. In the estimation of the spatial econometric model, a more common spatial econometric model, the generalized spatial econometric model, is also studied, and it is given under the condition of considering the unknown heteroscedasticity. MCMC effective estimation method. Finally, the Monte Carlo simulation of abstract theoretical analysis is carried out, and the effectiveness of the method is compared. These all provide important reference for the empirical analysis of the spatial econometrics.

【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：F224.0

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