發(fā)布時(shí)間：2018-01-17 21:09

  本文關(guān)鍵詞：多維貧困指數(shù)測(cè)定中權(quán)重設(shè)定的比較研究　出處：《山西財(cái)經(jīng)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： 多維貧困 多維貧困指數(shù)MPI 權(quán)重 蒙特卡洛模擬

【摘要】：對(duì)貧困的研究已經(jīng)越來越從單一的收入維度向多維考察轉(zhuǎn)變,而在多維貧困指數(shù)的測(cè)算過程當(dāng)中,不同指標(biāo)的權(quán)重分配是一個(gè)非常重要的問題。不同的權(quán)重分配有可能會(huì)產(chǎn)生截然不同的測(cè)算結(jié)果,這就必然會(huì)使得最終的測(cè)算結(jié)果損失了部分說服力。因此,對(duì)多維貧困測(cè)算中同權(quán)重設(shè)定的比較是非常重要且有意義的。為了對(duì)各種權(quán)重設(shè)定方法進(jìn)行比較,本文研究了多維貧困研究中權(quán)重設(shè)定的五種加權(quán)方法,包括全指標(biāo)等權(quán)、維度等權(quán)后指標(biāo)等權(quán)、主成分分析、多元對(duì)應(yīng)分析和模糊集方法�；贑HNS2011年的數(shù)據(jù)先計(jì)算出各加權(quán)方法下各指標(biāo)的權(quán)重值,再分別對(duì)全國(guó)12個(gè)省市的多維貧困進(jìn)行測(cè)算并排序得到五個(gè)排序結(jié)果。與此同時(shí)本文借鑒了蒙特卡羅模擬的思想進(jìn)行了隨機(jī)模擬實(shí)驗(yàn),計(jì)算得到大量隨機(jī)實(shí)驗(yàn)后各省市的累計(jì)排序均值并以此得到穩(wěn)定的12個(gè)省市的排序結(jié)果。通過設(shè)定總誤差函數(shù)為單個(gè)省市的絕對(duì)離差的加總形式,計(jì)算得到各加權(quán)方法的排序結(jié)果的總誤差�？傉`差越大的排序結(jié)果所對(duì)飲的加權(quán)方法越不推薦使用,反之總誤差越小則認(rèn)定為其是更合理的加權(quán)方法。按照以上思路并通過實(shí)證研究,本文發(fā)現(xiàn),模糊集方法和多元對(duì)應(yīng)分析所得到的總誤差最大,達(dá)到了12。而全指標(biāo)等權(quán)和主成分分析的總誤差較小,分別為4和6�；谠摲治鼋Y(jié)果,本文認(rèn)為在多維貧困指數(shù)測(cè)算時(shí)應(yīng)該盡量避免使用模糊集方法和多元對(duì)應(yīng)分析,應(yīng)該盡可能的使用全指標(biāo)等權(quán)方法或者主成分分析方法。
[Abstract]:The study of poverty has changed from a single income dimension to a multidimensional investigation, and in the process of measuring the multidimensional poverty index. Weight distribution of different indicators is a very important issue. Different weight distribution may produce different results, which will inevitably make the final calculation results lose some persuasiveness. It is very important and meaningful to compare the same weight setting in multidimensional poverty measurement. In order to compare various weight setting methods, this paper studies five weighting methods of weight setting in multidimensional poverty research. It includes full index equal weight, dimension equal weight index equal weight, principal component analysis, multivariate correspondence analysis and fuzzy set method. Based on the data of CHNS2011 year, the weight value of each index under each weighted method is first calculated. Then the multi-dimensional poverty of 12 provinces and cities in China are measured and sorted to get five ranking results. At the same time, this paper uses the idea of Monte Carlo simulation to carry out random simulation experiment. After a large number of random experiments, the accumulative ranking mean of each province and city is calculated and the results of 12 provinces and cities are obtained. By setting the total error function as the summation form of absolute deviation of a single province and city. The total error of the ranking results of each weighted method is calculated. The greater the total error, the less the weighted method of drinking is recommended. On the contrary, the smaller the total error is, the more reasonable the weighted method is. According to the above thinking and through the empirical study, this paper finds that the fuzzy set method and multivariate correspondence analysis can get the largest total error. The total error of total index iso-weight and principal component analysis is smaller, 4 and 6, respectively. Based on the results of the analysis. This paper holds that the fuzzy set method and multivariate correspondence analysis should be avoided in the calculation of multidimensional poverty index, and the whole index equal weight method or principal component analysis method should be used as far as possible.
【學(xué)位授予單位】：山西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F323.8

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 高帥;;社會(huì)地位、收入與多維貧困的動(dòng)態(tài)演變——基于能力剝奪視角的分析[J];上海財(cái)經(jīng)大學(xué)學(xué)報(bào);2015年03期

2 高艷云;馬瑜;;多維貧困測(cè)度方法比較及其展望[J];蘭州商學(xué)院學(xué)報(bào);2014年04期

3 張全紅;周強(qiáng);;中國(guó)多維貧困的測(cè)度及分解:1989～2009年[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2014年06期

4 張全紅;周強(qiáng);;多維貧困測(cè)量及述評(píng)[J];經(jīng)濟(jì)與管理;2014年01期

5 高艷云;;中國(guó)城鄉(xiāng)多維貧困的測(cè)度及比較[J];統(tǒng)計(jì)研究;2012年11期

6 郭建宇;吳國(guó)寶;;基于不同指標(biāo)及權(quán)重選擇的多維貧困測(cè)量——以山西省貧困縣為例[J];中國(guó)農(nóng)村經(jīng)濟(jì);2012年02期

7 蔣翠俠;許啟發(fā);李亞琴;;中國(guó)家庭多維貧困的統(tǒng)計(jì)測(cè)度[J];統(tǒng)計(jì)與決策;2011年22期

8 羅楚亮;;農(nóng)村貧困的動(dòng)態(tài)變化[J];經(jīng)濟(jì)研究;2010年05期

9 吳瑞林;祖霽云;;多分格相關(guān)系數(shù)的估計(jì)與應(yīng)用[J];統(tǒng)計(jì)與決策;2010年03期

10 王小林;Sabina Alkire;;中國(guó)多維貧困測(cè)量:估計(jì)和政策含義[J];中國(guó)農(nóng)村經(jīng)濟(jì);2009年12期

，

本文編號(hào)：1437970