發(fā)布時(shí)間：2018-07-12 16:29

  本文選題：分位數(shù)回歸 + 時(shí)間序列分析��； 參考：《溫州大學(xué)》2013年碩士論文

【摘要】：分位數(shù)回歸是Koenker和Bassett針對(duì)最小二乘的不足提出來的一種新的估計(jì)方法，它不僅能夠度量回歸變量對(duì)分布中心的影響，而且能度量回歸變量對(duì)分布上尾和下尾的影響，在不同的分位數(shù)下進(jìn)行預(yù)測(cè)，得到的信息更為全面和精確。時(shí)間序列分析是對(duì)時(shí)間序列數(shù)據(jù)建立模型,分析數(shù)據(jù)的內(nèi)部結(jié)構(gòu)和自身規(guī)律,從而對(duì)未來的發(fā)展進(jìn)行預(yù)測(cè)。用分位數(shù)回歸方法來估計(jì)時(shí)間序列模型時(shí),對(duì)隨機(jī)誤差的分布不做要求,能更加全面的刻畫分布的特征。 本論文首先介紹了分位數(shù)回歸的基本理論和性質(zhì)。其次應(yīng)用線性分位數(shù)回歸理論，并結(jié)合多元統(tǒng)計(jì)的相關(guān)知識(shí)，給出了房地產(chǎn)行業(yè)發(fā)展影響因素的模型，從定量角度把握各指標(biāo)之間的數(shù)量關(guān)系，得出在房?jī)r(jià)不同的地區(qū)，經(jīng)濟(jì)，，環(huán)境，房地產(chǎn)自身和人口因素對(duì)房?jī)r(jià)的影響程度也不同的結(jié)論。第三詳細(xì)介紹了ARMA模型的相關(guān)理論與建模過程，為ARMA模型在后面的實(shí)際應(yīng)用中提供了理論基礎(chǔ)。最后結(jié)合分位數(shù)回歸與ARMA模型對(duì)國房景氣指數(shù)進(jìn)行實(shí)證分析，得到不同分位數(shù)下的AR (1)模型，從而可以針對(duì)不同水平的數(shù)據(jù)采用不同的模型進(jìn)行序列的模擬和預(yù)測(cè)，文章還通過對(duì)比預(yù)測(cè)值和真實(shí)值的差異，明確指出相比最小二乘估計(jì)，用分位數(shù)回歸估計(jì)出的AR (1)模型更為準(zhǔn)確。
[Abstract]:Quantile regression is a new estimation method proposed by Koenker and Bassett for the deficiency of least squares. It can not only measure the influence of regression variables on the distribution center, but also measure the influence of regression variables on the upper tail and lower tail of distribution. The information obtained is more comprehensive and accurate when the prediction is carried out under different quantiles. Time series analysis is to build a model of time series data, analyze the internal structure of the data and their own laws, so as to predict the future development. When the quantile regression method is used to estimate the time series model, the distribution of random errors is not required, and the characteristics of the distribution can be described more comprehensively. In this paper, we first introduce the basic theory and properties of quantile regression. Secondly, by applying the linear quantile regression theory and combining the related knowledge of multivariate statistics, the paper gives the model of the influencing factors of the development of the real estate industry, from the quantitative angle to grasp the quantitative relationship between the various indicators, and draws the conclusion that in the regions with different housing prices, the economy. The influence of environment, real estate and population on house price is different. The third part introduces the theory and modeling process of ARMA model in detail, which provides a theoretical basis for the practical application of ARMA model in the future. Finally, combining the quantile regression and ARMA model, we get the AR (1) model under different quantiles, so we can use different models to simulate and predict the different levels of data. By comparing the difference between the predicted value and the real value, it is clearly pointed out that the AR (1) model estimated by quantile regression is more accurate than the least square estimation.
【學(xué)位授予單位】：溫州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F299.23;F224;O212.1

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本文編號(hào)：2117757