本文選題：變量選擇　切入點(diǎn)：空間計(jì)量　出處：《上海師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：空間計(jì)量經(jīng)濟(jì)學(xué)作為分析空間經(jīng)濟(jì)數(shù)據(jù)的主流方法,經(jīng)過三十年的發(fā)展,目前已成為計(jì)量經(jīng)濟(jì)分析重要的組成部分;變量選擇方法一直都是計(jì)量經(jīng)濟(jì)學(xué)研究的熱點(diǎn)問題,上述兩者都沿著各自的軌跡發(fā)展,很少出現(xiàn)交叉。然而在信息技術(shù)高速發(fā)展的今天,我們能收集到的時(shí)空數(shù)據(jù)越來越豐富,但由于空間相關(guān)性的引入,使得Gauss-Markov假設(shè)不再成立,基于經(jīng)典線性模型構(gòu)建的變量選擇不再適用,因而在進(jìn)行空間計(jì)量建模時(shí),如何選擇解釋變量,構(gòu)造最優(yōu)的模型是一個(gè)亟待解決的問題。本文將變量選擇的方法引入空間計(jì)量模型,提出了一系列空間計(jì)量模型的變量選擇方法,并對(duì)其有效性進(jìn)行了論證,還將理論研究的結(jié)果應(yīng)用于影響股票收益率的財(cái)務(wù)因素變量選擇研究。在理論研究部分,本文首先基于空間自回歸模型(SAR模型),在模型殘差服從正態(tài)分布的條件下,分別利用K-L信息量和貝葉斯方法,將經(jīng)典線性模型的AIC準(zhǔn)則和BIC準(zhǔn)則推廣到空間模型,提出并論證了基于SAR模型的空間AIC準(zhǔn)則(SAIC準(zhǔn)則)和空間BIC準(zhǔn)則(SBIC準(zhǔn)則),并證明了在一定條件下,基于SAR模型的SAIC準(zhǔn)則和SBIC準(zhǔn)則在其變量選擇中具有一致性。其次,本文將上述方法進(jìn)一步推廣到更為一般化的空間計(jì)量經(jīng)濟(jì)模型,空間自相關(guān)誤差自相關(guān)模型(SARAR模型),也證明了在一定條件下,基于SARAR模型的SAIC準(zhǔn)則和SBIC準(zhǔn)則在其變量選擇中具有一致性。再次,本文放松對(duì)殘差的假設(shè),在僅僅假設(shè)殘差獨(dú)立同分布的基礎(chǔ)上,基于SARAR模型構(gòu)造廣義空間信息準(zhǔn)則(SGIC準(zhǔn)則),將SAIC準(zhǔn)則和SBIC準(zhǔn)則納入統(tǒng)一的分析框架,通過SGIC準(zhǔn)則大樣本性質(zhì)的不同,將空間信息準(zhǔn)則分為空間AIC類準(zhǔn)則和空間BIC類準(zhǔn)則。在理論推導(dǎo)的同時(shí),本文還設(shè)計(jì)計(jì)算機(jī)仿真實(shí)驗(yàn),利用Monte Carlo模擬對(duì)空間模型變量選擇的有限樣本性質(zhì)進(jìn)行研究,研究發(fā)現(xiàn):針對(duì)于空間數(shù)據(jù),相較于經(jīng)典線性模型模型的變量選擇方法,本文提出的方法在空間模型的變量選擇中更加有效。在實(shí)證應(yīng)用部分,本文將理論研究的得到空間計(jì)量模型變量選擇方法應(yīng)用于影響股票收益率的財(cái)務(wù)指標(biāo)變量選擇研究。本文首先分析股票市場空間效應(yīng)來源的基礎(chǔ)上,構(gòu)造板塊-金融空間權(quán)重矩陣;其次,本文利用Moran’s I檢驗(yàn)和不含解釋變量的空間自回歸模型對(duì)股票收益率的空間效應(yīng)進(jìn)行測算,發(fā)現(xiàn)我國股票收益率具有顯著的空間效應(yīng);再次,本文利用理論研究部分得到的方法對(duì)影響股票收益率的財(cái)務(wù)指標(biāo)進(jìn)行變量選擇,從選擇結(jié)果中,可以看出反映公司盈利能力和發(fā)展能力的財(cái)務(wù)指標(biāo)對(duì)股票收益率的影響最大,反映公司償債能力的財(cái)務(wù)指標(biāo)次之,反映公司運(yùn)營能力的財(cái)務(wù)指標(biāo)最小;最后,本文進(jìn)行穩(wěn)健性檢驗(yàn),發(fā)現(xiàn)本文得到的結(jié)論是穩(wěn)健的。
[Abstract]:Spatial econometrics, as the mainstream method of analyzing spatial economic data, has become an important part of econometric analysis after 30 years of development, and variable selection method has always been a hot topic in econometrics research. However, with the rapid development of information technology, we can collect more and more space-time data, but because of the introduction of spatial correlation, the Gauss-Markov hypothesis is no longer true. The variable selection based on the classical linear model is no longer applicable, so how to choose the explanatory variable in the spatial metrological modeling, It is an urgent problem to construct the optimal model. In this paper, the method of variable selection is introduced into the spatial metrology model, and a series of variable selection methods of spatial metrology model are put forward, and its validity is proved. The results of the theoretical study are also applied to the selection of financial factors that affect the stock returns. In the theoretical research part, firstly, based on the spatial autoregressive model and SAR model, under the condition that the residual error of the model is normal distribution, By using K-L information and Bayesian method, the AIC criterion and BIC criterion of classical linear model are extended to spatial model, respectively. In this paper, the spatial AIC criterion based on SAR model and the spatial BIC criterion are proposed and proved. It is proved that the SAIC criterion and SBIC criterion based on SAR model are consistent in their variable selection under certain conditions. In this paper, the above method is further extended to the more general spatial econometric model, the spatial autocorrelation error autocorrelation model and the SARAR model are also proved under certain conditions. The SAIC criterion and SBIC criterion based on SARAR model are consistent in the selection of variables. Thirdly, the assumption of residual error is relaxed in this paper. Based on the SARAR model, the generalized spatial information criterion is constructed. The SAIC criterion and the SBIC criterion are brought into the unified analysis framework, and the large sample properties of the SGIC criterion are different. The spatial information criterion is divided into spatial AIC class criterion and spatial BIC class criterion. At the same time, the computer simulation experiment is designed, and the finite sample property of spatial model variable selection is studied by Monte Carlo simulation. It is found that the method proposed in this paper is more effective in the selection of spatial models than in the classical linear models. In the part of empirical application, it is found that the proposed method is more effective in the selection of variables in spatial models than in the classical linear models. In this paper, the method of variable selection of spatial econometric model is applied to the selection of financial index variables that affect the stock return rate. Firstly, the source of spatial effect of stock market is analyzed in this paper. Secondly, this paper uses Moran's I test and spatial autoregressive model without explanatory variables to measure the spatial effect of stock yield. Thirdly, this paper makes use of the method of theoretical research to select the financial index which affects the stock return rate, and from the selection result, It can be seen that the financial indicators reflecting the profitability and development ability of the company have the greatest impact on the stock return rate, followed by the financial indicators reflecting the solvency of the company, and the financial indicators reflecting the operating ability of the company are the least; finally, In this paper, we test the robustness and find that the conclusion obtained in this paper is robust.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F832.51;F275

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本文編號(hào)：1590768