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面板數(shù)據(jù)回歸樣條線混合效應(yīng)模型的應(yīng)用

發(fā)布時(shí)間:2018-01-03 18:43

  本文關(guān)鍵詞:面板數(shù)據(jù)回歸樣條線混合效應(yīng)模型的應(yīng)用 出處:《湖北工業(yè)大學(xué)》2017年碩士論文 論文類型:學(xué)位論文


  更多相關(guān)文章: 回歸樣條線混合效應(yīng)模型 蒙特卡洛模擬 城鄉(xiāng)收入差距 庫(kù)茲涅茨曲線


【摘要】:在醫(yī)學(xué)、生物學(xué)、計(jì)量經(jīng)濟(jì)學(xué)、金融學(xué)以及農(nóng)業(yè)等領(lǐng)域的研究中,通常會(huì)遇到面板數(shù)據(jù)。在對(duì)面板數(shù)據(jù)進(jìn)行統(tǒng)計(jì)建模時(shí),學(xué)者們提出了面板數(shù)據(jù)的非參數(shù)回歸模型。它是面板數(shù)據(jù)模型中非常重要的一種統(tǒng)計(jì)模型。在解決實(shí)際問(wèn)題時(shí),面板數(shù)據(jù)非參數(shù)模型更接近真實(shí)模型,更能充分利用數(shù)據(jù)中所提供的信息。回歸樣條線混合效應(yīng)模型,是眾多面板數(shù)據(jù)非參數(shù)模型中可操作性最強(qiáng),應(yīng)用最為廣泛的一種模型。全文共分為五章。第一章是緒論部分,闡述了面板數(shù)據(jù)的非參數(shù)回歸模型的研究背景、目的以及研究意義,并介紹了面板數(shù)據(jù)混合效應(yīng)模型的建模假設(shè)和模型的基本形式。第二章是理論部分,系統(tǒng)闡述了回歸樣條線混合效應(yīng)(MERS)模型。包括:MERS模型的構(gòu)建方法,求解模型的方法和平滑參數(shù)的確定等問(wèn)題。第三章是模擬比較部分,針對(duì)回歸樣條線混合效應(yīng)(MERS)模型與多項(xiàng)式方法進(jìn)行蒙特卡洛模擬比較,最后得出結(jié)論:MERS模型在總體函數(shù)估計(jì)方面優(yōu)于多項(xiàng)式方法,但在個(gè)體函數(shù)估計(jì)方面,表現(xiàn)不如多項(xiàng)式方法。另外,當(dāng)預(yù)測(cè)變量與相應(yīng)變量的關(guān)系未知,且兩者可能存在復(fù)雜關(guān)系的情況下,MERS模型估計(jì)要比多項(xiàng)式方法的估計(jì)更加穩(wěn)健,因而適用性更強(qiáng)。第四章為實(shí)證研究部分,第一個(gè)實(shí)證分析針對(duì)我國(guó)城鄉(xiāng)收入差距與經(jīng)濟(jì)增長(zhǎng)的問(wèn)題,闡述了城鄉(xiāng)收入差距的演變趨勢(shì)。實(shí)證分析通過(guò)構(gòu)建城鄉(xiāng)收入差距與經(jīng)濟(jì)增長(zhǎng)關(guān)系的模型,驗(yàn)證了我國(guó)經(jīng)濟(jì)增長(zhǎng)與城鄉(xiāng)收入差距確實(shí)存在倒U型關(guān)系。并估計(jì)了我國(guó)總體和各省個(gè)體到達(dá)峰值點(diǎn)的時(shí)間和峰值點(diǎn)的人均GDP。最后提出了縮小城鄉(xiāng)收入差距的經(jīng)濟(jì)和社會(huì)政策。第二個(gè)實(shí)證分析基于MERS模型,對(duì)BMI—年齡數(shù)據(jù)集進(jìn)行實(shí)證分析。得到總體均值函數(shù)的估計(jì)和總體均值函數(shù)導(dǎo)數(shù)的估計(jì),通過(guò)對(duì)總體分析,估計(jì)出13.7歲為BMI增速最快的年齡;并對(duì)肥胖、超重個(gè)體的個(gè)體函數(shù)進(jìn)行估計(jì),發(fā)現(xiàn)肥胖和超重的觸發(fā)年齡均值分別為20.41歲和21.17歲。通過(guò)分析超重、肥胖個(gè)體,發(fā)現(xiàn)大多數(shù)超重、肥胖個(gè)體的觸發(fā)年齡在15-25周歲階段,因此有必要在中學(xué)階段、大學(xué)階段,對(duì)其進(jìn)行飲食、運(yùn)動(dòng)干預(yù)。第五章為全文總結(jié)部分。
[Abstract]:In medicine, biology, econometrics, finance, and agriculture, panel data are usually encountered. Scholars have proposed a non-parametric regression model of panel data, which is a very important statistical model in panel data model. In solving the practical problems, the non-parametric model of panel data is closer to the real model. The regression spline mixed effect model is the most operable among many panel data non-parametric models. The most widely used model. The full text is divided into five chapters. The first chapter is the introduction part, elaborated the panel data non-parametric regression model research background, purpose and research significance. And introduced the panel data mixed effect model modeling hypothesis and the basic form of the model. The second chapter is the theoretical part. The regression spline mixed effect (MERS) model is systematically expounded, including the construction method of the MERS model, the method of solving the model and the determination of smooth parameters. The third chapter is the part of simulation and comparison. Comparing the regression spline mixed effect (MERS) model with polynomial method in Monte Carlo simulation, the conclusion is drawn that the MERS model is superior to the polynomial method in the estimation of population function. But in the aspect of individual function estimation, the performance is not as good as polynomial method. In addition, when the relationship between predictive variables and corresponding variables is unknown, and there may be a complex relationship between them. The MERS model is more robust than the polynomial method, so it is more applicable. Chapter 4th is the empirical research part, the first empirical analysis is aimed at the problem of urban-rural income gap and economic growth in China. This paper expounds the evolution trend of urban-rural income gap, and establishes the model of the relationship between urban-rural income gap and economic growth. The relationship between economic growth and urban-rural income gap is proved to be inversely U-shaped, and the time of reaching the peak point and the per capita GDP at the peak point are estimated. Finally, it is proposed to reduce the income gap between urban and rural areas. The second empirical analysis is based on MERS model. Through the empirical analysis of BMI-age data set, we get the estimation of the total mean function and the derivative of the total mean function. Through the overall analysis, we estimate that the age of 13.7 years old is the fastest growing age of BMI; The average trigger age of obesity and overweight is 20.41 years old and 21.17 years old, respectively. By analyzing overweight and overweight individuals, we find that most of them are overweight. The trigger age of obese individuals is 15-25 years old, so it is necessary to carry out diet and exercise intervention in middle school and college stage. Chapter 5th is the summary of the full text.
【學(xué)位授予單位】:湖北工業(yè)大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類號(hào)】:F224;F124.7

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