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  本文選題：半?yún)?shù)面板數(shù)據(jù)模型 + 隨機效應(yīng)��； 參考：《北京化工大學(xué)》2016年碩士論文

【摘要】：面板數(shù)據(jù)(Panel Data)是時點、個體兩個維度的數(shù)據(jù)呈現(xiàn)形式,相對于橫截面數(shù)據(jù)和時間序列優(yōu)點突出,在應(yīng)用實踐中發(fā)揮了很大作用。半?yún)?shù)面板數(shù)據(jù)模型,綜合了參數(shù)和非參數(shù)模型的優(yōu)點,同時又避免了參數(shù)模型的“限制性強”與非參數(shù)模型的“維數(shù)災(zāi)難”的問題,近年來得到廣泛關(guān)注。本文介紹了半?yún)?shù)面板數(shù)據(jù)模型中帶有個體效應(yīng)的部分線性模型和變系數(shù)模型的估計問題,采用截面最小二乘方法和修正的局部常數(shù)最小二乘方法對模型中的未知量進(jìn)行了求解。我們知道個體固定效應(yīng)和個體隨機效應(yīng)下,模型的估計方法和所得結(jié)果都有所不同。為了確定個體效應(yīng),提高估計、預(yù)報的精確度,本文針對部分線性面板數(shù)據(jù)模型,提出了參數(shù)Hausman檢驗和非參數(shù)Hausman檢驗方法。通過證明可知,在原假設(shè)(個體隨機效應(yīng))成立時,參數(shù)Hausman檢驗統(tǒng)計量漸近服從卡方(χ2)分布,非參數(shù)Hausman檢驗統(tǒng)計量漸近服從正態(tài)分布。Monte Carlo模擬結(jié)果顯示,參數(shù)Hausman檢驗在模擬中表現(xiàn)良好,且相比于非參數(shù)Hausman檢驗穩(wěn)健性和可靠性更高。進(jìn)一步,我們在地區(qū)生產(chǎn)總值影響因素的實證分析中,運用本文提出的估計方法和參數(shù)Hausman檢驗方法完成了數(shù)據(jù)的統(tǒng)計推斷。通過數(shù)值模擬結(jié)果我們發(fā)現(xiàn),在小樣本下Hausman檢驗統(tǒng)計量的分布存在不確定性,為了避免可能造成的錯誤判斷,本文引入Bootstrap抽樣方法,構(gòu)造了參數(shù)Bootstrap-Hausman檢驗統(tǒng)計量以及非參數(shù)Bootstrap-Hausman檢驗統(tǒng)計量,先求得統(tǒng)計量的分位點后再構(gòu)造假設(shè)檢驗的拒絕域。模擬結(jié)果顯示,小樣本下,兩種檢驗方法均能識別出個體效應(yīng),但參數(shù)Bootstrap-Hausman方法的穩(wěn)健性和可靠性更高。最后,將參數(shù)Bootstrap-Hausman檢驗應(yīng)用到經(jīng)濟(jì)增長與居民消費關(guān)系的實證研究中。
[Abstract]:Panel data (Panel data) is a time point, the data presentation form of two dimensions of individuals, compared with cross-section data and time series, has outstanding advantages, and has played a great role in application practice. The semi-parametric panel data model, which combines the advantages of parametric and non-parametric models and avoids the problem of "restrictive" and "dimensionality disaster" of parametric models, has been paid more and more attention in recent years. In this paper, the problem of estimating partial linear model and variable coefficient model with individual effect in semi-parametric panel data model is introduced. The unknowns in the model are solved by using the cross-section least square method and the modified local constant least squares method. We know that the estimation method and the results of the model are different under individual fixed effect and individual random effect. In order to determine individual effect and improve the accuracy of estimation and prediction, this paper presents parametric Hausman test and nonparametric Hausman test for partial linear panel data model. It is proved that when the original hypothesis (individual random effect) is established, the parameter Hausman test statistic is asymptotically distributed from chi-square (蠂 ~ 2), and the non-parametric Hausman test statistic is asymptotically obeyed from the normal distribution. Monte Carlo simulation results show that, The parametric Hausman test performs well in the simulation and is more robust and reliable than the non-parametric Hausman test. Furthermore, in the empirical analysis of the influencing factors of regional GDP, we use the estimation method and the parameter Hausman test method proposed in this paper to complete the statistical inference of the data. By numerical simulation, we find that there is uncertainty in the distribution of Hausman test statistics in small samples. In order to avoid possible misjudgment, Bootstrap sampling method is introduced in this paper. The parameter Bootstrap-Hausman test statistics and the nonparametric Bootstrap-Hausman test statistics are constructed. The sub-sites of the statistics are obtained first and then the rejection domain of the hypothesis test is constructed. The simulation results show that both methods can identify individual effects in small samples, but the parametric Bootstrap-Hausman method is more robust and reliable. Finally, the parametric Bootstrap-Hausman test is applied to the empirical study of the relationship between economic growth and consumption.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：O212.1

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本文編號：1949441