本文選題：小波分析 + 奇異點　； 參考：《西南財經(jīng)大學(xué)》2014年碩士論文

【摘要】：近二十年來,我國宏觀經(jīng)濟高速發(fā)展,與此同時國內(nèi)房地產(chǎn)市場也快速的發(fā)展。近幾年來我國房價持續(xù)上漲,超高的房價嚴(yán)重影響了居民的生活質(zhì)量。政府對于房市的調(diào)控政策陷入兩難。一方面,嚴(yán)格的控制房地產(chǎn)價格將會以犧牲經(jīng)濟增長速度為代價,不利于我國國民經(jīng)濟的建設(shè)；另一方面,過高的房價嚴(yán)重影響了我國居民的生活質(zhì)量。本文通過對房地產(chǎn)市場和經(jīng)濟增長的相關(guān)性研究,力求在調(diào)控房價和經(jīng)濟增長之間找到一個平衡點,為政府進行房地產(chǎn)市場調(diào)控提供有力的參考。 傅里葉分析和統(tǒng)計相結(jié)合是研究經(jīng)濟數(shù)據(jù)的重要方法之一,但因為傅里葉分析不具備“空間局部性”,因此也束縛了它在分析經(jīng)濟數(shù)據(jù)中的應(yīng)用。在傅里葉變換的基礎(chǔ)上,小波變換被提出�；谛〔ㄗ儞Q良好的“自適應(yīng)性”和“變焦性”等特性,小波變換非常適合于對房市和經(jīng)濟增長這樣的“非平穩(wěn)數(shù)據(jù)”進行分析。本文以2000年至2013年商品房銷售額和房地產(chǎn)投資額的月增長率以及GDP的月增長率為分析數(shù)據(jù)。 (1)首先我們對上各個數(shù)據(jù)進行預(yù)處理,然后利用MATLAB軟件的小波工具箱,對其進行小波分解。房市和經(jīng)濟增長波動的原因是由噪聲(即突變因素)造成的,因此對每層的高頻分解圖進行觀察分析,可得出它們的奇異點。并結(jié)合現(xiàn)實找出奇異點發(fā)生的原因。 (2)利用小波變換其分形的特性,可以將房市和經(jīng)濟增長中的噪聲信號進行剔除,從而使得它們的發(fā)展趨勢顯現(xiàn)出來。通過對各層低頻信號進行重構(gòu)。對各自進行周期性的分析。 (3)方差和相關(guān)系數(shù)是相關(guān)性研究的兩個主要指標(biāo)。本文結(jié)合最大重復(fù)離散小波變換,分別計房市和經(jīng)濟增長的小波方差,及兩者之間的小波相關(guān)系數(shù)。并對這兩個指標(biāo)進行分析,從而得出它們之間長期和短期的相關(guān)系數(shù)。
[Abstract]:In the past twenty years, the macro-economy of our country has developed rapidly, at the same time, the domestic real estate market has also developed rapidly. In recent years, housing prices in China continue to rise, the super-high housing prices seriously affect the quality of life of residents. The government's regulation of the housing market is in a dilemma. On the one hand, strict control of real estate prices will be at the expense of economic growth rate, which is not conducive to the construction of our national economy; on the other hand, excessive housing prices seriously affect the quality of life of Chinese residents. Through the research on the correlation between real estate market and economic growth, this paper tries to find a balance between housing price control and economic growth, and provides a powerful reference for the government to regulate the real estate market. The combination of Fourier analysis and statistics is one of the important methods to study economic data, but because Fourier analysis does not have "spatial locality", it also restricts its application in the analysis of economic data. Wavelet transform is proposed on the basis of Fourier transform. Based on the good "adaptive" and "zoom" characteristics of wavelet transform, wavelet transform is very suitable for the analysis of "non-stationary data" such as housing market and economic growth. This paper analyzes the monthly growth rate of commercial housing sales and real estate investment from 2000 to 2013 and the monthly growth rate of GDP. Firstly, we preprocess the above data, then use the wavelet toolbox of MATLAB software to decompose them. The reason of the fluctuation of housing market and economic growth is caused by noise (that is, sudden change factor), so the singularity of each layer can be obtained by observing and analyzing the high-frequency decomposition diagram of each layer. Combined with reality, the reason of singularity point is found out. 2) by using the fractal characteristic of wavelet transform, the noise signals in housing market and economic growth can be eliminated, and their development trend can be revealed. The low frequency signals of each layer are reconstructed. To carry on the periodic analysis to each. Variance and correlation coefficient are two main indexes of correlation study. In this paper, the wavelet variance of housing market and economic growth and the wavelet correlation coefficient between them are calculated by using the maximum repeated discrete wavelet transform. The long-term and short-term correlation coefficients between them are obtained by analyzing the two indexes.
【學(xué)位授予單位】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F124;F299.23

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本文編號：1964369