本文關鍵詞：基于動態(tài)模型平均的中國大中城市房價預測　出處：《西南交通大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 動態(tài)模型平均 房價預測 模型信度檢驗 滾動預測

【摘要】：近二十年來,我國房地產(chǎn)市場經(jīng)歷了較長時期的蓬勃發(fā)展,但同時也遭遇了若干次嚴厲的調(diào)控,大中城市的房價出現(xiàn)了一些較大幅度的波動,房價成為媒體、人民群眾和政府關注的焦點。因此,如何對未來的房價走勢進行科學和有效的預判,也是眾多房地產(chǎn)經(jīng)濟學者和業(yè)界普遍關心的重要問題。本文率先引入了動態(tài)模型平均(DMA)方法及其特例-動態(tài)模型選擇(DMS),對于全國三十個省會城市和直轄市的房價進行了預測分析。相對于傳統(tǒng)模型,DMA方法允許模型變量設置和變量系數(shù)的時變性,充分考慮了不同變量、不同時間對于房價影響的大小。同時本文使用等權(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比,充分討論房價預測的表現(xiàn)。本文不僅使用了傳統(tǒng)研究中大量使用的擴展窗口預測模式,同時添加滾動窗口模式作為參照對比,既解決了時間序列中可能存在的結(jié)構(gòu)突變問題,同時也在多種預測模式之下,全面穩(wěn)健地對于房價進行預測。此外,在使用傳統(tǒng)宏觀經(jīng)濟變量作為預測變量時,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,對于房價的預測會產(chǎn)生新的幫助作用。隨后,區(qū)別于其他預測研究只采用簡單統(tǒng)計指標評價預測表現(xiàn),本文采用更加高級的模型信度設定方法(MCS),進一步避免了一類統(tǒng)計錯誤,并且強調(diào)在不同標準和統(tǒng)計指標下,多角度全方面檢驗房價預測模型的精度。實證結(jié)果顯示,無論是擴展窗口,還是滾動窗口,DMA方法在全國三十個大中城市,在樣本內(nèi)估計精度較高的基礎上,樣本外預測方面也能夠有效地降低全國各個大中城市的房價預測誤差,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,降低計算負荷,并且發(fā)現(xiàn)搜索指數(shù)對于房價影響在近些年逐漸增大,傳統(tǒng)變量預測作用式微,表現(xiàn)不及預期。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,預測三、六期及延長樣本外預測區(qū)間,與前續(xù)結(jié)果均一致性地均支持DMA方法的優(yōu)越預測表現(xiàn)。最后,DMA方法為房價預測提供了新的思路,給予購房者、業(yè)界以及政府管理部門更好的房價決策和預判。
[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.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F299.23

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本文編號：1408793