【摘要】：在計(jì)量經(jīng)濟(jì)學(xué)中,建立多元線性回歸模型時(shí),經(jīng)常會(huì)出現(xiàn)模型所選取的變量之間存在多重共線性的情況,這可能導(dǎo)致建立的模型得到的參數(shù)不穩(wěn)定,模型不能充分地解釋所要研究的問題。由于模型存在共線性的變量,線性回歸模型不滿足計(jì)量經(jīng)濟(jì)學(xué)經(jīng)典的假設(shè)條件,此時(shí)如何有效的處理模型變量之間的共線性成為模型面臨的重要問題之一。而常用的解決模型變量之間多重共線性的方法都存在一定的缺陷,即不能很好的利用模型變量信息。如何能更好的充分利用模型變量的有用信息,不損失變量之間的內(nèi)在聯(lián)系去解決變量之間的共線性關(guān)系,嶺回歸就是在這樣的背景下產(chǎn)生的。嶺回歸可以更好地保留變量的內(nèi)在信息,不破壞它們之間的內(nèi)涵關(guān)系,并且能夠更好的抓住主要矛盾,去分析解決問題,進(jìn)而是解決模型多重共線性的一個(gè)重要途徑。因此,文章對(duì)嶺回歸問題進(jìn)行研究具有理論意義與應(yīng)用價(jià)值。在建模過程中使用嶺回歸方法時(shí),計(jì)算嶺參數(shù)是重要的關(guān)鍵問題。因此文章在研究嶺參數(shù)計(jì)算上做了深入研究。首先,回顧了與嶺回歸相關(guān)的理論,從減小模型變量方差擴(kuò)大因子角度出發(fā),結(jié)合生物工程領(lǐng)域的遺傳算法,計(jì)算最優(yōu)的嶺參數(shù),然后根據(jù)得到的嶺參數(shù)建立嶺回歸模型。其次,文章通過蒙特卡洛模擬方法對(duì)其進(jìn)行了模擬實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,基于遺傳算法確定嶺參數(shù)的嶺估計(jì)可以有較好的估計(jì)特性。最后,文章采用所提出的方法,進(jìn)行了關(guān)于我國衛(wèi)生總費(fèi)用問題的實(shí)證分析,得到了一些有價(jià)值的結(jié)論并給出一些相關(guān)的政策建議。文章通過遺傳算法與方差擴(kuò)大因子法相結(jié)合計(jì)算了最優(yōu)嶺參數(shù),得到的模型參數(shù)更加穩(wěn)定。這樣計(jì)算嶺參數(shù)一方面剔除了嶺參數(shù)在確定過程中摻雜的人為主觀性,另一方面把變量由于多重共線性導(dǎo)致其不合理的方差擴(kuò)大因子控制在合理的區(qū)間內(nèi),穩(wěn)定了模型的估計(jì)參數(shù),并且提高了參數(shù)的估計(jì)精度。文章的研究工作為嶺回歸選取嶺參數(shù)提供了一種新的選取方法,可以使得嶺回歸模型更好的應(yīng)用到實(shí)際經(jīng)濟(jì)問題當(dāng)中。其次,利用研究的成果,研究了影響我國衛(wèi)生總費(fèi)用變量之間的多重共線性問題,得出了結(jié)論并給出了有價(jià)值的政策意見。
[Abstract]:In econometrics, in the establishment of multivariate linear regression models, multiple collinearity often occurs between the variables selected by the model, which may lead to the instability of the parameters obtained by the established models. The model can not fully explain the problem to be studied. Because there are co-linear variables in the model, the linear regression model does not meet the classical econometric assumptions. How to deal with the collinearity between the model variables effectively becomes one of the important problems facing the model. However, the common methods to solve the multiple collinearity between model variables have some defects, that is, they can not make good use of model variables information. How to make full use of the useful information of model variables and not lose the internal relations between variables to solve the collinear relationship between variables, Ridge regression is produced in such a background. Ridge regression can better retain the internal information of variables, do not destroy the connotative relationship between them, and can better grasp the main contradiction to analyze and solve the problem, and then it is an important way to solve the multiple collinearity of the model. Therefore, the study of Ridge regression is of theoretical significance and practical value. When using ridge regression method in modeling, calculating ridge parameters is an important key problem. Therefore, the paper makes a deep study on the calculation of ridge parameters. Firstly, the theory related to ridge regression is reviewed, and the optimal ridge parameter is calculated from the angle of reducing the extended factor of variance of model variables and combining with genetic algorithm in the field of bioengineering, and then the ridge regression model is established according to the obtained ridge parameters. Secondly, the paper carries on the simulation experiment through the Monte Carlo simulation method. The experimental results show that the ridge estimation based on genetic algorithm can have better estimation characteristics. Finally, by using the proposed method, the paper makes an empirical analysis on the total health expenditure in China, obtains some valuable conclusions and gives some relevant policy suggestions. In this paper, the optimal ridge parameters are calculated by combining genetic algorithm with variance expansion factor method, and the model parameters are more stable. On the one hand, the artificial subjectivity of the ridge parameter is eliminated in the determination of the ridge parameter, on the other hand, the unreasonable variance expansion factor of the variable is controlled in a reasonable range due to multiple collinearity. The estimation parameters of the model are stabilized and the estimation accuracy of the parameters is improved. The research work in this paper provides a new method for selecting ridge parameters by ridge regression, which can make the ridge regression model better applied to practical economic problems. Secondly, by using the results of the research, this paper studies the multiple collinearity problem affecting the variables of the total health expenditure in China, and draws a conclusion and gives some valuable policy suggestions.
【學(xué)位授予單位】：天津財(cái)經(jīng)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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