本文關(guān)鍵詞：基于動(dòng)態(tài)模型平均的中國(guó)大中城市房?jī)r(jià)預(yù)測(cè)　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 動(dòng)態(tài)模型平均 房?jī)r(jià)預(yù)測(cè) 模型信度檢驗(yàn) 滾動(dòng)預(yù)測(cè)

【摘要】：近二十年來(lái),我國(guó)房地產(chǎn)市場(chǎng)經(jīng)歷了較長(zhǎng)時(shí)期的蓬勃發(fā)展,但同時(shí)也遭遇了若干次嚴(yán)厲的調(diào)控,大中城市的房?jī)r(jià)出現(xiàn)了一些較大幅度的波動(dòng),房?jī)r(jià)成為媒體、人民群眾和政府關(guān)注的焦點(diǎn)。因此,如何對(duì)未來(lái)的房?jī)r(jià)走勢(shì)進(jìn)行科學(xué)和有效的預(yù)判,也是眾多房地產(chǎn)經(jīng)濟(jì)學(xué)者和業(yè)界普遍關(guān)心的重要問(wèn)題。本文率先引入了動(dòng)態(tài)模型平均(DMA)方法及其特例-動(dòng)態(tài)模型選擇(DMS),對(duì)于全國(guó)三十個(gè)省會(huì)城市和直轄市的房?jī)r(jià)進(jìn)行了預(yù)測(cè)分析。相對(duì)于傳統(tǒng)模型,DMA方法允許模型變量設(shè)置和變量系數(shù)的時(shí)變性,充分考慮了不同變量、不同時(shí)間對(duì)于房?jī)r(jià)影響的大小。同時(shí)本文使用等權(quán)重平均、自回歸、貝恩斯平均、貝恩斯選擇以及信息理論平均等多種模型作為對(duì)比,充分討論房?jī)r(jià)預(yù)測(cè)的表現(xiàn)。本文不僅使用了傳統(tǒng)研究中大量使用的擴(kuò)展窗口預(yù)測(cè)模式,同時(shí)添加滾動(dòng)窗口模式作為參照對(duì)比,既解決了時(shí)間序列中可能存在的結(jié)構(gòu)突變問(wèn)題,同時(shí)也在多種預(yù)測(cè)模式之下,全面穩(wěn)健地對(duì)于房?jī)r(jià)進(jìn)行預(yù)測(cè)。此外,在使用傳統(tǒng)宏觀經(jīng)濟(jì)變量作為預(yù)測(cè)變量時(shí),本文也考慮了大數(shù)據(jù)環(huán)境下,互聯(lián)網(wǎng)搜索指數(shù)包含更多需求信息,對(duì)于房?jī)r(jià)的預(yù)測(cè)會(huì)產(chǎn)生新的幫助作用。隨后,區(qū)別于其他預(yù)測(cè)研究只采用簡(jiǎn)單統(tǒng)計(jì)指標(biāo)評(píng)價(jià)預(yù)測(cè)表現(xiàn),本文采用更加高級(jí)的模型信度設(shè)定方法(MCS),進(jìn)一步避免了一類統(tǒng)計(jì)錯(cuò)誤,并且強(qiáng)調(diào)在不同標(biāo)準(zhǔn)和統(tǒng)計(jì)指標(biāo)下,多角度全方面檢驗(yàn)房?jī)r(jià)預(yù)測(cè)模型的精度。實(shí)證結(jié)果顯示,無(wú)論是擴(kuò)展窗口,還是滾動(dòng)窗口,DMA方法在全國(guó)三十個(gè)大中城市,在樣本內(nèi)估計(jì)精度較高的基礎(chǔ)上,樣本外預(yù)測(cè)方面也能夠有效地降低全國(guó)各個(gè)大中城市的房?jī)r(jià)預(yù)測(cè)誤差,比傳統(tǒng)自回歸等方法縮小50%以上。此外,本文也發(fā)現(xiàn)DMA能夠有效篩選變量,降低計(jì)算負(fù)荷,并且發(fā)現(xiàn)搜索指數(shù)對(duì)于房?jī)r(jià)影響在近些年逐漸增大,傳統(tǒng)變量預(yù)測(cè)作用式微,表現(xiàn)不及預(yù)期。本文嘗試提出了需求端和政策不確定性兩方面的合理解釋。最后,基于穩(wěn)健性的分析證明,預(yù)測(cè)三、六期及延長(zhǎng)樣本外預(yù)測(cè)區(qū)間,與前續(xù)結(jié)果均一致性地均支持DMA方法的優(yōu)越預(yù)測(cè)表現(xiàn)。最后,DMA方法為房?jī)r(jià)預(yù)測(cè)提供了新的思路,給予購(gòu)房者、業(yè)界以及政府管理部門(mén)更好的房?jī)r(jià)決策和預(yù)判。
[Abstract]:In the past two decades, the real estate market of our country has experienced a long period of vigorous development, but at the same time, it has also encountered a number of strict regulation and control. The housing prices in large and medium-sized cities have experienced some relatively large fluctuations, and the housing prices have become the media. The focus of attention of the people and the government. Therefore, how to predict the future trend of housing prices scientifically and effectively. This paper first introduces the dynamic model averaging (DMA) method and its special case-dynamic model selection (DMS). The housing prices of 30 provincial capitals and municipalities in China are predicted and analyzed. Different variables are fully considered compared with the traditional model / DMA method, which allows the model variables to be set and variable coefficients to be time-varying. At the same time, this paper uses the equal-weight average, autoregressive, Baines average, Baines selection and information theory average as a comparison. This paper not only uses the extended window prediction model, which is widely used in traditional research, but also adds the rolling window model as a reference comparison. It not only solves the problem of structural mutation in time series, but also makes a comprehensive and robust prediction of house prices under various forecasting models. In addition, when using traditional macroeconomic variables as forecasting variables. This article also considers that in big data environment, the Internet search index contains more information on demand, which will help the forecast of house prices. Different from other prediction studies only using simple statistical indicators to evaluate the performance of the prediction, this paper uses a more advanced model reliability setting method to further avoid a class of statistical errors. And emphasizes that under different standards and statistical indicators, multi-angle and all-sided test of the accuracy of the housing price forecasting model. Empirical results show that, whether extended window or rolling window. DMA method can effectively reduce the error of house price prediction in 30 large and medium-sized cities in the whole country on the basis of high estimation accuracy in the sample. In addition, this paper also found that DMA can effectively screen variables, reduce the computational load, and find that the impact of search index on house prices has gradually increased in recent years. The traditional variable forecasting function is declining and the performance is not as expected. This paper tries to put forward two reasonable explanations of demand side and policy uncertainty. Finally, based on robust analysis, forecast three. Six periods and extended prediction interval outside the samples are consistent with the previous results to support the superior performance of the DMA method. Finally, the DMA method provides a new way of thinking for house price prediction, giving home buyers. Industry as well as government management better house price decision making and forecast.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F299.23

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本文編號(hào)：1408793