基于小波分析的我国经济运行特征研究
|
摘要
小波分析理论作为一门新兴的数学理论和方法,已经被应用到各个领域的研究之中。近年来,国内外的学者们把小波分析方法应用在经济学上,内容包括金融学的证券市场,股票分析,期货分析等方面,也包括宏观经济周期分析,经济政策分析,产业政策分析,经济增长规律分析,市场效率分析等。小波方法将经济时间序列由单纯的相域分析扩展到了相域与频域相结合的频域-相域分析,国外将小波分析应用于经济学始于20世纪80年代中期,我国关于小波分析在经济学的应用研究始于20世纪90年代,随即发展成了一种重要的经济分析工具,得到了许多有意义的分析结论。本文在借鉴了国内外关于小波在经济学中的应用研究的基础之上,结合我国经济自身发展特点,分析了我国宏观经济的周期波动,并对我国GDP缺口进行了分析模拟,用小波神经网络对我国股票市场进行了聚类分析,对我国期货市场建立了分形协整自回归模型,总之用小波分析对我国经济运行的特征进行了分析,并得到了一些有意义的结论。
全文分为七章,具体结构安排与研究结论如下：
第一章,小波方法在我国经济研究的意义,介绍了本文研究的主要内容。本文主要使用了小波方法,GARCH模型,ARFIMA模型,ARMA模型,及参数,半参数,非参数估计方法。对我国GDP波动,股票债券市场,期货市场,进行了研究。
第二章,小波分析与经济理论的研究综述,首先回顾了国外用小波对经济进行研究的各种经济理论与实证检验；接着,回顾了我国国内经济领域中小波的应用分析,具体包括小波在经济周期中的应用,小波在证券市场中的应用,小波在期货市场中的应用；然后回顾了小波神经网络在我国国内经济研究中的相关理论方法和研究成果以及小波方法在我国其他经济领域中的应用。
第三章,介绍了小波理论的基本原理。包括小波变换,小波重构,小波分析的优点,小波包变换,双正交多分辨小波分析,小波变换与长记忆过程分析,小波分解后的时间序列的性质,谱分析,小波方差分析,分形差分过程的小波极大似然估计,小波变换下方差齐次检验,基于小波的时间序列估计,样本方差采样性质,小波神经网络,小波混沌序列分析等,以及这些理论与经济分析的联系,研究现状等。
第四章,用小波方法对我国国内生产总值进行了分析及模拟。使用了单小波变换,频谱分析,高斯频谱合成法,长记忆过程模拟等方法,结合经济序列的传统分析方法,GARCH模型等。结合小波方法与GSSM方法对美国GDP非周期波动成分进行了模拟。通过对我国GDP的小波分解分析,发现我国经济波动由三个周期构成,主要为四到八个季度的短周期波动及十六到三十二个季度的中长周期波动。对我国GDP序列进行了模拟分析,该分析结合了小波频谱高斯合成法与GARCH模型,此模型对短期的预测与对长期的预测精度一样,所以非常适合于长期预测,发现未来我国经济强劲增势不会减小。其次,用小波方法测量了我国的核心通货膨胀率,实证发现,该方法优于目前计算核心通货膨胀率的几种常用方法。
第五章,对我国的股票市场和投资市场进行了研究。首先得到了我国股票市场中分尺度行为的证据,然后对我国股票市场上几支股票进行了长记忆性检验,发现上证指数符合几何布朗运动,而其他几支股票的长记忆参数也非常小,在选取的几支股票中,包钢稀土的长记忆参数最大,为我国股票波动的模拟提供了依据：其次使用小波对投资市场中的一些基金的收益率进行了平滑处理和阀值分析,并对选取的几种股票型基金建立了AR-GARCH模型,得到了我国投资市场基金收益率的波动原理,利用小波神经网络对我国股票市场的安全进行了研究,发现我国股票市场安全状况整体螺旋上升;还通过将小波神经网络方法与聚类方法结合,对股票市场中投资者的信心(市场情绪)对股票市场的影响进行了研究,发现市场的心理因素是决定市场波动的重要力量,并为投资者进行股票买卖提供了一种理论方法。
第六章,用小波方法对我国商品期货市场进行了分析。发现我国期货市场上的金融数据的长记忆性是非常显著的,估计了我国期货市场的长记忆参数,还用马尔科夫区制转移模型检验长记忆过程,即估计高,低两种状态,估计交易量对价格的波动是否存在影响,便利收益是影响商品期货价格的主要因素。结合小波去除噪声的计算方法,用卡吗滤波二阶段模型分别估计1-2因素模型。
第七章,介绍了最新的小波理论——提升小波理论,并用提升小波对我国GDP序列进行趋势和波动分解,结合实证发现提升小波比传统小波具有更大的灵活性和可选择性。
本文的创新点有结合GSSM方法与GARCH (1,1)模型对我国GDP序列进行了分析与预测；基于小波方法提出了计算我国核心通货膨胀率的一种新方法；通过小波自组织神经网络发现了我国股票市场上投资者信心对股票市场波动的影响,并提出了风险-回报率分析的一种新的方法；使用ARFIMA模型对我国期货市场进行了分析,发现便利收益是期货市场价格变动的主要因素之一；用提升小波对我国宏观经济进行了分析。由于时间的限制以及作者自身水平的局限,本文的研究难免存在较多的不足与疏漏,敬请各位专家和同仁多批评指正。
Wavelet analysis theory, as a new mathematical theory and methods has been applied to various fields of study. In recent years, scholars at home and abroad applied to wavelet analysis in economics, which includes aspects of finance, macroeconomics, the economic time series analysis from a simple phase field extends to a common phase and frequency domain analysis. The wavelet analysis used in economics abroad began in the mid-20th century, the wavelet analysis used in China economics began in the recent 20 years.
The first chapter, Wavelet significance of economic research in China, introduced the main contents of this paper, this paper uses the wavelet method, GARCH models, ARFIMA models, ARMA models, and parameters, semi-parametric, nonparametric estimation on China's GDP volatility, stock and bond market, futures market, were studied.
The second chapter, wavelet analysis and economic theory research summary, first reviewed the foreign economic research with wavelet various economic theories and empirical test, then, reviewed the domestic economy of the application of wavelet analysis, specifically including wavelets the application of the economic cycle, the application of wavelets in the stock market, wavelet application in the futures market, and then review the wavelet neural network in China's domestic economic research methods and related theories of wavelet method results and other economic areas in China application.
The third chapter introduces the basic principles of wavelet theory, including the wavelet transform, wavelet reconstruction, wavelet packet transform, long memory process analysis, spectrum analysis, wavelet variance analysis, wavelet neural networks, and their theoretical links with the economic analysis, economic research and so on.
Chapter IV is to use wavelet method on our gross domestic product analysis and simulation, using a single wavelet transform, spectral analysis, long memory process simulation and other methods, combined with traditional methods of economic series, GARCH model. China's economic fluctuations are mainly found in three cycles of composition, the strong growth tendency in China's economic future will not be reduced.
Chapter V of the stock market and investment markets were studied. First, the stock market has been carved-scale behavior of the evidence, and then a few of the stock market, stocks were on the long memory test and found that the Shanghai index consistent with geometric Brownian motion, and several other stocks in the long memory parameter is very small In a few selected stocks, the Baotou Rare Earth's long memory parameter the largest fluctuation of the stock provided the basis for the simulation. Secondly, the wavelet in the investment market rate of return of some funds were smoothed and threshold analysis, and selection of several stock funds established AR-GARCH model, has been China's investment market fund yields fluctuate principle. Wavelet neural network security in China stock market has been studied and found that the overall security situation of the stock market spiral. Through the wavelet neural network combined with the clustering method, on the confidence of investors in the stock market (market sentiment) the impact on the stock market and found that the market psychology is an important force in the decision to market fluctuations and investment by the stock trading provides a theoretical method.
ChapterⅥwith the wavelet method of analysis of commodity futures markets and found that China's futures market on the long memory of the financial data is very significant, got the estimation of the long memory parameters of the futures market, but also with a Markov regime switching model inspect long memory process, the estimated high and low status, reducing the volatility of long-term commodity; convenience yield is the impact of commodity futures prices composition's main factor. Remove the noise wavelet method, using two-stage model of c estimate 1-2 factor models.
ChapterⅦis about the latest wavelet theory-lifting wavelet theory, and with the lifting wavelet theory, got China GDP's trends and volatility decomposition, combined with empirical evidence that the lifting wavelet is much greater in flexibility and selectivity comparing with traditional wavelet.
Innovation of this paper are using the GSSM method and GARCH(1,1) model of GDP in China, stock sequences were analyzed by wavelet neural network self-organization, found that psychology factors leaven China's stock market, propose a new method to decide when to buy and when to sail. Convenience yield is found to be the futures prices" one of the main factors.
全文分为七章,具体结构安排与研究结论如下：
第一章,小波方法在我国经济研究的意义,介绍了本文研究的主要内容。本文主要使用了小波方法,GARCH模型,ARFIMA模型,ARMA模型,及参数,半参数,非参数估计方法。对我国GDP波动,股票债券市场,期货市场,进行了研究。
第二章,小波分析与经济理论的研究综述,首先回顾了国外用小波对经济进行研究的各种经济理论与实证检验；接着,回顾了我国国内经济领域中小波的应用分析,具体包括小波在经济周期中的应用,小波在证券市场中的应用,小波在期货市场中的应用；然后回顾了小波神经网络在我国国内经济研究中的相关理论方法和研究成果以及小波方法在我国其他经济领域中的应用。
第三章,介绍了小波理论的基本原理。包括小波变换,小波重构,小波分析的优点,小波包变换,双正交多分辨小波分析,小波变换与长记忆过程分析,小波分解后的时间序列的性质,谱分析,小波方差分析,分形差分过程的小波极大似然估计,小波变换下方差齐次检验,基于小波的时间序列估计,样本方差采样性质,小波神经网络,小波混沌序列分析等,以及这些理论与经济分析的联系,研究现状等。
第四章,用小波方法对我国国内生产总值进行了分析及模拟。使用了单小波变换,频谱分析,高斯频谱合成法,长记忆过程模拟等方法,结合经济序列的传统分析方法,GARCH模型等。结合小波方法与GSSM方法对美国GDP非周期波动成分进行了模拟。通过对我国GDP的小波分解分析,发现我国经济波动由三个周期构成,主要为四到八个季度的短周期波动及十六到三十二个季度的中长周期波动。对我国GDP序列进行了模拟分析,该分析结合了小波频谱高斯合成法与GARCH模型,此模型对短期的预测与对长期的预测精度一样,所以非常适合于长期预测,发现未来我国经济强劲增势不会减小。其次,用小波方法测量了我国的核心通货膨胀率,实证发现,该方法优于目前计算核心通货膨胀率的几种常用方法。
第五章,对我国的股票市场和投资市场进行了研究。首先得到了我国股票市场中分尺度行为的证据,然后对我国股票市场上几支股票进行了长记忆性检验,发现上证指数符合几何布朗运动,而其他几支股票的长记忆参数也非常小,在选取的几支股票中,包钢稀土的长记忆参数最大,为我国股票波动的模拟提供了依据：其次使用小波对投资市场中的一些基金的收益率进行了平滑处理和阀值分析,并对选取的几种股票型基金建立了AR-GARCH模型,得到了我国投资市场基金收益率的波动原理,利用小波神经网络对我国股票市场的安全进行了研究,发现我国股票市场安全状况整体螺旋上升;还通过将小波神经网络方法与聚类方法结合,对股票市场中投资者的信心(市场情绪)对股票市场的影响进行了研究,发现市场的心理因素是决定市场波动的重要力量,并为投资者进行股票买卖提供了一种理论方法。
第六章,用小波方法对我国商品期货市场进行了分析。发现我国期货市场上的金融数据的长记忆性是非常显著的,估计了我国期货市场的长记忆参数,还用马尔科夫区制转移模型检验长记忆过程,即估计高,低两种状态,估计交易量对价格的波动是否存在影响,便利收益是影响商品期货价格的主要因素。结合小波去除噪声的计算方法,用卡吗滤波二阶段模型分别估计1-2因素模型。
第七章,介绍了最新的小波理论——提升小波理论,并用提升小波对我国GDP序列进行趋势和波动分解,结合实证发现提升小波比传统小波具有更大的灵活性和可选择性。
本文的创新点有结合GSSM方法与GARCH (1,1)模型对我国GDP序列进行了分析与预测；基于小波方法提出了计算我国核心通货膨胀率的一种新方法；通过小波自组织神经网络发现了我国股票市场上投资者信心对股票市场波动的影响,并提出了风险-回报率分析的一种新的方法；使用ARFIMA模型对我国期货市场进行了分析,发现便利收益是期货市场价格变动的主要因素之一；用提升小波对我国宏观经济进行了分析。由于时间的限制以及作者自身水平的局限,本文的研究难免存在较多的不足与疏漏,敬请各位专家和同仁多批评指正。
Wavelet analysis theory, as a new mathematical theory and methods has been applied to various fields of study. In recent years, scholars at home and abroad applied to wavelet analysis in economics, which includes aspects of finance, macroeconomics, the economic time series analysis from a simple phase field extends to a common phase and frequency domain analysis. The wavelet analysis used in economics abroad began in the mid-20th century, the wavelet analysis used in China economics began in the recent 20 years.
The first chapter, Wavelet significance of economic research in China, introduced the main contents of this paper, this paper uses the wavelet method, GARCH models, ARFIMA models, ARMA models, and parameters, semi-parametric, nonparametric estimation on China's GDP volatility, stock and bond market, futures market, were studied.
The second chapter, wavelet analysis and economic theory research summary, first reviewed the foreign economic research with wavelet various economic theories and empirical test, then, reviewed the domestic economy of the application of wavelet analysis, specifically including wavelets the application of the economic cycle, the application of wavelets in the stock market, wavelet application in the futures market, and then review the wavelet neural network in China's domestic economic research methods and related theories of wavelet method results and other economic areas in China application.
The third chapter introduces the basic principles of wavelet theory, including the wavelet transform, wavelet reconstruction, wavelet packet transform, long memory process analysis, spectrum analysis, wavelet variance analysis, wavelet neural networks, and their theoretical links with the economic analysis, economic research and so on.
Chapter IV is to use wavelet method on our gross domestic product analysis and simulation, using a single wavelet transform, spectral analysis, long memory process simulation and other methods, combined with traditional methods of economic series, GARCH model. China's economic fluctuations are mainly found in three cycles of composition, the strong growth tendency in China's economic future will not be reduced.
Chapter V of the stock market and investment markets were studied. First, the stock market has been carved-scale behavior of the evidence, and then a few of the stock market, stocks were on the long memory test and found that the Shanghai index consistent with geometric Brownian motion, and several other stocks in the long memory parameter is very small In a few selected stocks, the Baotou Rare Earth's long memory parameter the largest fluctuation of the stock provided the basis for the simulation. Secondly, the wavelet in the investment market rate of return of some funds were smoothed and threshold analysis, and selection of several stock funds established AR-GARCH model, has been China's investment market fund yields fluctuate principle. Wavelet neural network security in China stock market has been studied and found that the overall security situation of the stock market spiral. Through the wavelet neural network combined with the clustering method, on the confidence of investors in the stock market (market sentiment) the impact on the stock market and found that the market psychology is an important force in the decision to market fluctuations and investment by the stock trading provides a theoretical method.
ChapterⅥwith the wavelet method of analysis of commodity futures markets and found that China's futures market on the long memory of the financial data is very significant, got the estimation of the long memory parameters of the futures market, but also with a Markov regime switching model inspect long memory process, the estimated high and low status, reducing the volatility of long-term commodity; convenience yield is the impact of commodity futures prices composition's main factor. Remove the noise wavelet method, using two-stage model of c estimate 1-2 factor models.
ChapterⅦis about the latest wavelet theory-lifting wavelet theory, and with the lifting wavelet theory, got China GDP's trends and volatility decomposition, combined with empirical evidence that the lifting wavelet is much greater in flexibility and selectivity comparing with traditional wavelet.
Innovation of this paper are using the GSSM method and GARCH(1,1) model of GDP in China, stock sequences were analyzed by wavelet neural network self-organization, found that psychology factors leaven China's stock market, propose a new method to decide when to buy and when to sail. Convenience yield is found to be the futures prices" one of the main factors.
引文
[1]J.Morlet, G.Arens, E.Foruteau, et al. Wave propagation and sampling theory and complex waves.[J] Geophysics,1982.47(2):222-236.
[2]Y.Meyer, Ondelettes, et al. Functions splines. Seminaire EDP [J]. Ecole Ploy Technique, Paris, 1986.
[3]I.Daubechies. The wavelets transform,time-frequency localization and signal analysis[J]. IEEE Trans on PAMI,1989,11(7):674-693.
[4]Christiano L.J.,Fitzgerald T.J."The Band Pass Filter"[J].International Economic Review,June 2003,435-465.
[5]Conway Paul and Frame David."Spectral Analysis of New Zealand Output GapsUsing Fourier and Wavelet Techniques"[w].Reserve Bank of New Zealand Discussion Paper,DP2000/06, June 2000.
[6]I.Daubechies, Ten Lectures on Wavelets, CBMS-NSF regional series in applied mathematics. Philadelphia, PA:SIAM,1992.
[7]Wang Jian, Xu Denin and He Yuyao, Multidimensional Wavelet Networks Based on a Tensor Product Structure,控制理论与应用,2002,19(3)：381-386.
[8]Schleicher C."An introduction to Wavelets for Economists"[W].Bank of Canada Working Paper,2002,3.
[9]James B, Marco, Mauro and Willi. Instrumental variables and wavelet decompositions. Economic Modelling,2010,27:1498-1513.
[10]S. Raja Sethu, Saumitra N. Stock prices, inflation and output:Evidence from wavelet analysis. Economic Modelling.2009,26:1089-1092.
[11]Atilla, Sait and Elif. Analysis of sectoral credit default cycle dependency with wavelet networks:Evidence from Turkey. Economic Modelling.2009,26:1382-1388.
[12]Jeyanthi, Cornelis. Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997. International Review of Financial Analysis.2005,14:211-246.
[13]Mansur Mohammed and Omar. Systematic risk and time scales:New evidence from an application of wavelet approach to the emerging Gulf stock markets. International Review of Financial Analysis.2010,19:10-18.
[14]Victor, H.C. and Joseph. Wavelet domain correlation between the futures prices of natural gas and oil. The Quarterly Review of Economic and Finance.2010,50:408-414.
[15]Joseph, Robert, Hakan and His. Tests of long-range dependence in interest rates using wavelets. The Quarterly Review of Economic and Finance.2004,44:180-189.
[16]Larry. Using the Haar wavelet transform in the semiparametric specification of time series. Economic Modelling.2009,26:392-403.
[17]LIU Hongbo, ZHANG Hongwei. Wavelet Analytical Forecasting Method of Water Consumption. Transactions of Tianjin University.2004,9:206-208.
[18]Liu Bin, Dong Qinxi. WAVELET MODELING AND FORECASTING AND ITS APPLICATION IN THE CHINESE MONETARY MULTIPLIER.1999,8:917-923.
[19]L.Zunino,D.G.Pere,M.Garavaglia,O.A.Rosso. Wavelet entropy of stochastic processes. Statistical Mechanics and its Applications.2007,379(2):503-512.
[20]Mark J.Jensen. An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets. Journal of economic Dynamics and Control. 2000,24(3):361-387.
[21]Larry W.Taylor. Using the Haar wavelet transform in the semiparametric specification of time series. Economic Modelling.2009,26(2):392-403.
[22]L.Zunimo,D.G.Perez,M.Garavaglia,Osvaldo A.Rosso. Characterization of laser propagation through turbulent media by quantifiers based on the wavelet transform:Dynamic study. Statistic Mechanics and its Applications.2006,364(15):79-86.
[23]D.G.Perez, L.Zunimo,M.Garavaglia,O. A.Rosso. Wavelet entropy and fractional Brownian motion time series. Statistical Mechanics and its Applications.2006,365(2):282-288.
[24]Ding-wei Huang. Wavelet analysis in a traffic model. Statistical Mechanics and its Applications.2003,329(1-2):298-308.
[25]David Baqaee. Using wavelets to measure core inflation:The case of New Zealand Original Research Article. The North American Journal of Economics and Finance.2010,21(3):241-255.
[26]Emmanuel Haven,Xiaoquan Liu,Chenghu Ma,Liya Shen. Revealing the implied risk-neutral MGF from options:The wavelet method Original Research Article. Journal of Economic Dynamics and Control.2009,33(3):692-709.
[27]James B. Ramsey,Marco Gallegati,Mauro Gallegati,Willi Semmler. Instrumental variables and wavelet decompositions Original Research Article. Economic Modelling,2010,27(6): 1498-1513.
[28]Lui's Aguiar-Conraria,Maria Joana Soares. Business cycle synchronization and the Euro:A wavelet analysis Original Research Article. Journal of Macroeconomics,InPress,Corrected Proof, Available online.2011,21.
[29]Viviana Fernandez. Wavelet and SVM-based forecasts:An analysis of the U.S.metal and materials manufacturing industry Original Research Article. Resources Policy.2007,32(1-2): 80-89.
[30]Gilles Fay,Eric Moulines,Francois Roueff,Murad S.Taqqu. Estimators of long-memory: Fourier versus wavelets Original Research Article. Journal of Econometrics. 2009,151(2):159-177.
[31]Antonio Rua, Luis C. Nunes. International comovement of stock market returns:A wavelet analysis Original Research Article. Journal of Empirical Finance.2009,16(4):632-639.
[32]Luis Aguiar-Conraria,Nuno Azevedo,Maria Joana Soares. Using wavelets to decompose the time-frequency effects of monetary policy Original Research Article. Statistical Mechanics and its Applications.2008,387(12):2863-2878.
[33]Atilla Cifter,Sait Yilmazer,Elif Cifter. Analysis of sectoral credit default cycle dependency with wavelet networks:Evidence from Turkey Original Research Article. Economic Modelling. 2009,26(6):1382-1388.
[34]F.E.A.Leite,Raul Montagne,G.Corso,G.L.Vasconcelos,L.S.Lucena. Optimal wavelet filter for suppression of coherent noise with an application to seismic data Original Research Article. Statistical Mechanics and its Applications.2008,387(7):1439-1445.
[35]Mercedes Esteban-Bravo,Jose M.Vidal-Sanz. Computing continuous-time growth models with boundary conditions via wavelets Original Research Article. Journal of Economic Dynamics and Control.2007,31(11):3614-3643.
[36]Francis In,Sangbae Kim. Multiscale hedge ratio between the Australian stock and futures markets:Evidence from wavelet analysis Original Research Article. Journal of Multinational Financial Management.2006,16(4):411-423.
[37]Fabio Principato,Gaetano Ferrante.1/f Noise decomposition in random telegraph signals using the wavelet transform Original Research Article. Statistical Mechanics and its Applications. 2007,380(1):75-97.
[38]Jeyanthi Karuppiah.Cornelis A.Los. Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997 Original Research Article. International Review of Financial Analysis.2005,14(2):211-246.
[39]Sangbae Kim,Francis In. The relationship between stock returns and inflation:new evidence from wavelet analysis Original Research Article. Journal of Empirical Finance.2005,12(3): 435-444.
[40]Motohiro Yogo. Measuring business cycles:A wavelet analysis of economic time series. Economics Letters.2008,100(2):208-212.
[41]Zbigniew R.Struzik,Arno P.J.M.Siebes. Wavelet transform based multifractal formalism in outlier detection and localisation for financial time series Original Research. Article. Statistical Mechanics and its Applications.2002,15(3-4):388-402.
[42]Mansur Masih,Mohammed Alzahrani,Omar Al-Titi. Systematic risk and time scales:New evidence from an application of wavelet approach to the emerging Gulf stock markets Original Research Article. International Review of Financial Analysis.2010,19(1):10-18.
[43]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Differentiating intraday seasonalities through wavelet multi-scaling Original Research Article. Statistical Mechanics and its Applications.2001,289(3-4):543-556.
[44]Zbigniew R.Struzik. Wavelet methods in (financial) time-series processing Original Research Article. Statistical Mechanics and its Applications.2001,296(1-2):307-319. Atilla Cifter. Value-at-risk estimation with wavelet-based extreme value theory:Evidence from emerging markets. Statistical Mechanics and its Applications.2011,28.
[45]Antonios Antoniou,Constantinos E.Vorlow. Recurrence quantification analysis of wavelet pre-filtered index returns. Statistical Mechanics and its Applications.2004,344(1-2):257-262.
[46]V.Ibarra-Junquera,J.S.Murguia,P.Escalante-Minakata,H.C.Rosu. Application of multifractal wavelet analysis to spontaneous fermentation processes. Statistical Mechanics and its Applications.2008,387(12):2802-2808.
[47]Gongmeng Chen,Yoon K.Choi,Yong Zhou. Detections of changes in return by a wavelet smoother with conditional heteroscedastic volatility. Journal of Econometrics.2008,143(2): 227-262.
[48]S.Raja Sethu Durai,Saumitra N.Bhaduri.Stock prices. Inflation and output:Evidence from wavelet analysis. Economic Modelling.2009,26(5):1089-1092.
[49]E.Serrano,A.Figliola. Wavelet Leaders:A new method to estimate the multifractal singularity spectra. Statistical Mechanics and its Applications.2009,388(14):2793-2805.
[50]P.Manimaran,Prasanta K.Panigrahi,Jitendra C.Parikh. Multiresolution analysis of fluctuations in non-stationary time series through discrete wavelets. Statistical Mechanics and its Applications.2009,388(12):2306-2314.
[51]Sangbae Kim. Francis In On the relationship between changes in stock prices and bond yields in the G7 countries:Wavelet analysis. Journal of International Financial Markets. 2007,17(2):167-179.
[52]O.A.Rosso,M.T.Martin,A.Plastino. Brain electrical activity analysis using wavelet-based informational tools. Statistical Mechanics and its Applications.2002,313(3-4):587-608.
[53]Gabriel J.Power,Calum G.Turvey. Long-range dependence in the volatility of commodity futures prices:Wavelet-based evidence. Statistical Mechanics and its Applications.2010,389(l): 79-90.
[54]Victor Lux Tonn,H.C.Li,Joseph McCarthy. Wavelet domain correlation between the futures prices of natural gas and oil. The Quarterly Review of Economics and Finance. 2010,50(4):408-414.
[55]Rong Jiang,Hong Yan. Studies of spectral properties of short genes using the wavelet subspace Hilbert-Huang transform (WSHHT). Statistical Mechanics and its Applications. 2008,387(16-17):4223-4247.
[56]Silva,Luiz F.L.,Edmundo A. Forecasting oil price trends using wavelets and hidden Markov models. Energy Economics,2010,32(6):1507-1519.
[57]O.A., M.T. Martin, A.Evidence of self-organization in brain electrical activity using wavelet-based informational tools. Statistical Mechanics and its Applications.2005,347(1): 444-464.
[58]L.G.,A.Plastino,M. E. TorresWavelet analysis and nonlinear dynamics in a nonextensive setting. Statistical and Theoretical Physics.1997,246(3-4):487-509.
[59]T.Conlon,M.Crane,H.J.Ruskin. Wavelet multiscale analysis for Hedge Funds:Scaling and strategies. Statistical Mechanics and its Applications.2008,387(21):5197-5204.
[60]V.Ibarra-Junquera,P.Escalante-Minakata,J.S. Murguia,H-C.Rosu. Inferring mixed-culture growth from total biomass data in a wavelet approach. Statistical Mechanics and its Applications. 2006,370(2):777-792.
[61]Dj.Stratimirovi,S.Milo evi,S.Blesi,M.Ljubisavljevi. Wavelet analysis of discharge dynamics of fusimotor neurons. Statistical Mechanics and its Applications.2001,291(2-3):13-23.
[62]I.Antoniou,Vi.V.Ivanov,Va.V.Ivanov,P.V.Zrelov. Wavelet filtering of network traffic measurements. Statistical Mechanics and its Applications.2003,324(3-4):733-753.
[63]Marco Antonio Leonel Caetano,Takashi Yoneyama. Characterizing abrupt changes in the stock prices using a wavelet decomposition method. Statistical Mechanics and its Applications. 2007,383(2):519-526.
[64]Theo Naccache. Oil price cycles and wavelets. Energy Economics.2011,33(2):338-352.
[65]Fatemeh Ebrahimi, Muhammad Sahimi. Multiresolution wavelet coarsening and analysis of transport in heterogeneous media. Statistical Mechanics and its Applications.2002,316(1-4): 160-188.
[66]G.Corso,P.S.Kuhn,L.S.Lucena,Z.D. ThomeSeismic ground roll time-frequency filtering using the gaussian wavelet transform. Statistical Mechanics and its Applications.2003,318(3-4): 551-561.
[67]M.E.Pereyra,P.W.Lamberti,O.A.Rosso. Wavelet Jensen-Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures. Statistical Mechanics and its Applications.2007,379(1):122-132.
[68]Adel Sharkasi,Martin Crane,Heather J. Ruskin,Jose A. Matos. The reaction of stock markets to crashes and events:A comparison study between emerging and mature markets using wavelet transforms. Statistical Mechanics and its Applications.2006,368(2):511-521.
[69]Ingve Simonsen. Measuring anti-correlations in the nordic electricity spot market by wavelets. Statistical Mechanics and its Applications.2003,322:597-606.
[70]Christian Genest,Esterina Masiello,Karine Tribouley. Estimating copula densities through wavelets. Mathematics and Economics.2009,44(2):170-181.
[71]Ashok Razdan. Wavelet correlation coefficient of strongly correlated time series. Statistical and Theoretical Physics.2004,333:335-342.
[72]A.M.Korol,R.J.Rasia,O.A.Rosso.Alterations of thalassemic erythrocytes detected by wavelet entropy. Statistical Mechanics and its Applications.2007,375(1):257-264.
[73]Dj.Stratimirovic,S.Milosevic,S.Blesic,M.Ljubisavljevic. Wavelet transform analysis of time series generated by the stimulated neuronal activity. Statistical Mechanics and its Applications. 2007,374(2):699-706.
[74]A.Arneodo.What can we learn with wavelets about DNA sequences. Statistical Mechanics and its Applications.1998,249(1-4):439-448.
[75]Vera Pancaldi,Peter R.King,Kim Christensen. Wavelet-based upscaling of advection equations. Statistical Mechanics and its Applications.2008,387(19-20):4760-4770.
[76]C Rodrigues Neto,A Zanandrea,F.M Ramos, R.R Rosa,M.J.A Bolzan,L.D.A So. Multiscale analysis from turbulent time series with wavelet transform. Statistical Mechanics and its Applications.2001,295(1-2):215-218.
[77]Jan W.Kantelhardt,Diego Rybski,Stephan A.Zschiegner,Peter Braun,Eva Koscielny-Bunde, Valerie Livina, Shlomo Havlin,Armin Bunde. Multifractality of river runoff and precipitation: comparison of fluctuation analysis and wavelet methods. Statistical Mechanics and its Applications.2003,330(1-2):240-245.
[78]P.A.Ritto,J.J.Alvarado-Gil,J.G.Contreras. Scaling and wavelet-based analyses of the long-term heart rate variability of the Eastern Oyster. Statistical Mechanics and its Applications. 2005,349(1-2):291-301.
[79]O.A.Rosso,M.T.Martin,A.Plastino. Brain electrical activity analysis using wavelet-based informational tools(II):Tsallis non-extensivity and complexity measures. Statistical Mechanics and its Applications.2003,320:497-511.
[80]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Wavelets for Variance-Covariance Estimation. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. 2002:235-271.
[81]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Discrete Wavelet Transforms. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics.2002:96-160.
[82]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Wavelets and Stationary Processes. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics.2002:161-201.
[83]A.Arneodo,B-Audit,E.Bacry,S.Manneville,J.F.Muzy,S.G.Roux. Thermodynamics of fractal signals based on wavelet analysis:application to fully developed turbulence data and DNA sequences. Statistical and Theoretical Physics.1998,254(1-2):24-45.
[84]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Wavelet Denoising. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics.2002:202-234.
[85]A.Arneodo,E.Bacry,J.F.Muzy. The thermodynamics of fractals revisited with wavelets. Statistical and Theoretical Physics.1995,213(1-2):232-275.
[86]Yong Zhou,Alan T.K.Wan,Shangyu Xie,Xiaojing Wang.Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance. Journal of Econometrics.2010,159(1): 183-201.
[87]Jens Giesemann,Martin Greiner,Peter Lipa.Wavelet cascades Statistical and Theoretical Physics.1997,247(1-4):41-58.
[88]Esben P.Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods.Modeling and Control of Economic.2001:187-189.
[89]Arnold J.Mandell,Karen A.Selz,Michael F.Shlesinger.Wavelet transformation of protein hydrophobicity sequences suggests their memberships in structural families. Statistical and Theoretical Physics.1997,244(1-4):254-262.
[90]W. Korneta.The orthonormal wavelet representation of complex structures. Statistical Mechanics and its Applications.1992,185(1-4):111-115.
[91]A.Marrone,A.D.Polosa,GScioscia,S.Stramaglia,A.Zenzola.Wavelet analysis of blood pressure waves in vasovagal syncope.Statistical Mechanics and its Applications.1999,271(3-4): 458-469.
[92]Jan W.Kantelhardt,H.Eduardo Roman,Martin Greiner.Discrete wavelet approach to multifractality. Statistical and Theoretical Physics.1995,220(3-4):219-238.
[93]C.F.Caiafa,M.P.Sassano,A.N.Proto. Wavelet and Karhunen Loeve transformations applied to SAR signals and images. Statistical Mechanics and its Applications.2005,356(1):172-177.
[94]V.Ibarra-Junquera.Corrigendum to:"Application of multifractal wavelet analysis to spontaneous fermentation processes".Statistical Mechanics and its Applications.2008,387(18): 4731.
[95]Tests of long-range dependent in interest rates using wavelets.The Quarterly Review of Economics and Finance.2004,44(1):180-189.
[96]Stephan Schlueter.A long-term/short-term model for daily electricity prices with dynamic volatility.Energy Economics.2010,32(5):1074-1081.
[97]Viviana Fernandez. The CAPM and value at risk at different time-scales.International Review of Financial Analysis.2006,15(3):203-219.
[98]Antonio Rua. Measuring comovement in the time-frequency space.Journal of Macroeconomics.2010,32(2):685-691.
[99]Liljana Ferbar. Demand forecasting methods in a supply chain:Smoothing and denoising. International Journal of Production Economics.2009,118(1):49-54.
[100]Viviana Fernandez. The war on terror and its impact on the long-term volatility of financial markets. International Review of Financial Analysis.2008,17(1):1-26.
[101]Viviana Fernandez. The impact of major global events on volatility shifts:Evidence from the Asian crisis and 9/11.Economic Systems.2006,30(1):79-97.
[102]Enrico Capobianco. Empirical volatility analysis:feature detection and signal extraction with function dictionaries.Statistical Mechanics and its Applications.2003,319:495-518.
[103]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Scaling properties of foreign exchange volatility. Statistical Mechanics and its Applications.2001,289(1-2):249-266.
[104]Sangbae Kim,Francis In.A note on the relationship between industry returns and inflation through a multiscaling approach.Finance Research Letters.2006,3(1):73-78.
[105]P.Manimaran,Prasanta K.Panigrahi,Jitendra C.Parikh.Difference in nature of correlation between NASDAQ and BSE indices.Statistical Mechanics and its Applications.2008,387(23): 5810-5817.
[106]D.M.Nachane,Amlendu Kumar Dubey.The vanishing role of money in the macro-economy: An empirical investigation for India.Economic Modelling.2011,28(3):859-869.
[107]Cosmin Sinescu.Quantitative parameters which describe speech sound distortions due to inadequate dental mounting.Statistical Mechanics and its Applications.2008,387(5-6):1205-1217.
[108]P.Manimaran,Prasanta K.Panigrahi. Statistics of event by event fluctuations.Statistical Mechanics and its Applications.2010,389(18):3703-3710.
[109]M.E.Torres,G.Schlotthauer. Slight parameter changes detection in biological models:a multiresolution approach. Statistical Mechanics and its Applications.2003,324(3-4):645-664.
[110]Alan Kirman,Gilles Teyssiere. Testing for bubbles and change-points. Journal of Economic Dynamics and Control.2005,29(4):765-799.
[111]Francis In,Sangbae Kim,Ramazan Gencay. Investment horizon effect on asset allocation between value and growth strategies. Economic Modelling, In Press, Corrected Proof, Available online.2011,3.
[112]Osvaldo A.Rosso,Maria Liliana Mairal. Characterization of time dynamical evolution of electroencephalographic epileptic records. Statistical Mechanics and its Applications. 2002,312(3-4):469-504.
[113]R.Bellotti,F.De Carlo,M.de Tommaso,M.Lucente. Classification of spontaneous EEG signals in migraine. Statistical Mechanics and its Applications.2007,382(2):549-556.
[114]NyoNyo A.Kyaw,Cornelis A.Los,Sijing Zong. Persistence characteristics of Latin American financial markets. Journal of Multinational Financial Management.2006,16(3):269-290.
[115]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Multiscale systematic risk Journal of International Money and Finance.2005,24(1):55-70.
[116]Robert DiSario,Hakan Saraoglu,Joseph McCarthy,Hsi Li. Long memory in the volatility of an emerging equity market:The case of Turkey. Journal of International Financial Markets, Institutions and Money.2008,18(4):305-312.
[117]Viviana Fernandez,Brian M.Lucey. Portfolio management under sudden changes in volatility and heterogeneous investment horizons. Statistical Mechanics and its Applications. 2007,375(2):612-624.
[1]石柱鲜、刘俊生、邓创.应用局面转移模型对我国2005年经济景气的分析与预测[J].吉大数量经济研究(2005卷),经济科学出版社,2007年
[2]石柱鲜,黄红梅,刘俊生,王立勇.我国经济周期波动分析与2006年～2007年主要经济指标预[A].经济蓝皮书2007.
[3]刘金全、刘志刚.具有Markov区制转移的向量误差修正模型及其应用[J].管理科学学报,2006,(5)12-20.
[4]石柱鲜、黄红梅、石庆华：关于中国潜在GDP与景气波动、通货膨胀的经验研究[J],《世界经济》,2004年第8期.
[5]孙巍,张屹山.全要素生产率的非参数测度与分解研究[A].21世纪数量经济学(第一卷)[C].北京：2000.256-264.
[6]孙巍.生产资源配置效率生产前沿面理论及其应用[M],北京：社会科学出版社,2000.
[7]崔锦泰著,程正兴译.《小波分析导论》[M]西安交通大学出版社,1995.
[8]徐佩霞,孙功宪,小波分析与应用实例,合肥：中国科学技术大学出版社,1996.
[9]刘贵忠等《小波分析及应用》[M]西安电子科技大学出版社,1995.
[10]姜彬,杨柱元.基于小波神经网络的经济增长预测.云南民族大学学报.2009,1：81-84.
[11]张鸿彦,林辉.基于小波神经网络的期权定价模型.云南大学学报.2007,7：716-720.
[12]汪克亮,杨力.基于小波神经网络的企业技术创新能力模糊综合评价.技术经济.2007,8：34-38.
[13]辛秀.基于小波神经网络的上市公司财务危机预测.计算机系统应用.2010,19(8)：191-194,127.
[14]钟满田,苏云.基于小波网络的非线性逼近股票分析方法.武汉工程大学学报.2007,1：81-83.
[15]刘兰娟,谢美萍.数据挖掘方法研究—对我国石油产量的预测分析.财经研究.2006,3：114-120.
[16]张新红.经济时间序列的连续参数小波网络预测模型.运筹与管理.2007,4：72-77.
[17]向小东.小波神经网络预测方法在石油期货价格预测中的应用研究.技术经济.2006,6：121-124.
[18]张鸿彦,林辉,姜彩楼.用混合小波网络和遗传算法对期权定价的研究.系统工程学报.2010,2：43-49.
[19]李全亮,李怀祖.正交尺度小波网络在我国税收预测中的应用.系统工程理论方法应用.2006,2：54-56,70.
[20]张新红,郑丕谔.正交小波网络及其在经济预测中的应用.系统工程学报.2006,4：196-200.
[21]黄师娟等.国际黄金价格的小波变换FAR预测模型.西安工业大学学报.2009,2：84-88.
[22]姚恩营,周玉国,孙国栋.国民收入的小波—非参数自回归预测模型.计算机工程与设计.2009,30(20)：4774-4776.
[23]余鹏翼,刘娜,施佳庆.国内上市公司“周内效应”的小波多分辨研究.中国证券期货.2009,8：8-9,22.
[24]刘薇,常振海.基于小波Mallat算法的外汇汇率预测模型.河北北方学院学报.2009, 10：69-72.
[25]许启发,蒋翠侠.基于小波变换的金融市场持续性特征研究.统计观察.2006,1(205)：85-87.
[26]徐梅,张世英.基于小波变换的时变长记忆SV模型估计方法研究.系统工程学报.2006,2：12-17,23.
[27]徐梅,张世英.基于小波变换的长记忆随机波动模型估计方法研究.中国管理科学.2006,2：7-14.
[28]陆克从,赵刚,王静.基于小波分析的AR-GREY预测模型及其应用.理论新探.2009,21(297)：20-22.
[29]侯守国,张世英.基于小波分析的股市高频互相关研究.中国管理科学.2006,6：1-6.
[30]刘娜,郑小洋,李为平.基于小波分析的经济数据预测.重庆工学院学报(自然科学),2009,11：169-172.
[31]高雷,任慧玉.基于小波分析的上证综指预测.财经论坛.2006,7(下)：116-117.
[32]侯守国,张世英.基于小波分析的中国股市高频长记忆研究.理论新探.2006,1(下)：9-11.
[33]龚亚琴.基于小波分析的住宅房产均价预测.陕西理工学院院报.2006,9：25-28.
[34]陈童,李积源,赵刚.基于小波分析方法的装备价格指数变化研究.微计算机信息(管控一体化).2006,22(2-3)：70-72.
[35]周潮.基于小波降噪技术的中国居民储蓄波动周期分析.西部金融.2009,11：61-62.
[36]吴礼斌,崔岩岩.基于小波协方差的中国股市波动序列相关性的实证分析.皖西学院学报.2009,10：5-8.
[37]谷政,江惠坤,褚保金.农业保险费率厘定的小波——非参数统计方法极其实证分析.系统工程.2008,8：39-43.
[38]杨凌.上海股票市场的基于小波去噪的混沌性检验.统计与信息论坛.2006,5：86-89,106.
[39]杨凌.深圳证券市场混沌性探测的前提：小波去噪.理论新探.2006,4(下)：21-23.
[40]杨凌,颜日初.我国证券市场经济混沌探测研究——一种基于小波变换的噪声处理.中南财经政法大学学报.2006,2(155)：41-44.
[41]殷光伟,郑丕谔.小波包与混沌集成的股票市场预测新方法.管理工程学报.2006,2：117-119.
[42]邓凯旭,宋宝瑞.小波变换在金融数据分析中的应用.数理统计与管理.2006,3：215-219.
[43]周景宏,郗伟东,禹海兰.小波方法在股市分析中的应用.东北电力大学学报.2006,4：47-51.
[44]刘达,王尔康,牛东晓.小波分析和考虑外生变量的广义自回归条件异方差模型在电价预测中的应用.电网技术.2009,10：99-104.
[45]刘丹红等.LMSV模型波动的长记忆与相关性的小波分析.理论新探(理论版).2007,6：4-7.
[46]高辉清.非线性预测方法：小波网络.预测.1995,6：46-48.
[47]奚振斐,宋国乡.分形小波在外汇投资市场中的应用.华南理工大学学报(自然科学版).2006,11：123-126.
[48]高静,张世英.高频时间序列基于小波分析的预测.理论新探.2006,9(221)：4-5.
[49]章前等.股票价格与成交量关系的小波分析.昆明理工大学学报(理工版).2007,12：95-97.
[50]宋宜美,奚振斐,宋国乡.股票市场分布特性的小波方法研究.西安电子科技大学学报(自然科学版).2002,12：826-829.
[51]戴稳胜,吕奇杰,徐曼文.股指期货信息内含股价变动信息的挖掘——小波框架与支持向量回归的金融建模应用.统计研究.2008,2：78-83.
[52]隋学深,杨忠海.股指时间序列突变点小波检测研究.哈尔滨商业大学学报(自然科学版).2007,4：249-253.
[53]薛超,李星野,雷蕾.沪深港股市相关性的小波分析.数学的实践与认识.2008,8：25-32.
[54]郑中发.基于连续双正交样条小波的股价指数奇异点检测.科技情报开发与经济.2006,20(16)：91-92.
[55]李果,单泪源,陈芝.基于事例推理和小波网络的大规模定制产品快速成本估算方法研究.
[56]奚振斐,王立平,宋国乡.基于小波变换的期权市场中的时间价值序列预测.西北大学学报.2007,12：966-968.
[57]杜修平,那一沙.基于小波变换和SOM聚类的证券板块划分.华南金融电脑.2008,9：84-86.
[58]李晖,郭晨,金洪章.基于小波变换和均生函数周期外推组合模式的非平稳时间序列分 析与长期预测.控制理论与应用.2008,4：283-288.
[59]林志勇,张维强,徐晨.基于小波变换与MOBP的股价预测.计算机工程与应用.2008,44(16)：215-217.
[60]杨稣,史耀媛,宋恒.基于小波变换域的SVM股市时间序列预测算法.科学技术与工程.2008,6：3171-3174.
[61]史建平,张传灵,宋国乡.基于小波的汇率波动序列长记忆性研究.现代电子技术.2007,1(240)：173-175.
[62]孙晋众,林健.基于小波的能源消费弹性系数预测方法.沈阳航空工业学院院报.2007,6：78-81.
[63]许启发,蒋翠侠,张世英.基于小波多分辨分析的协整建模理论与方法的扩展.统计研究.2007,8：92-96.
[64]李迎锋,陈辉,潘林.基于小波多分辨分析方法的高阶矩CAPM研究.理论新探(理论版).2007,3：32-36.
[65]赵晋理.基于小波分析的股票市场分析.太原师范学院学报(自然科学版).2007,6：5-7.
[66]刘晏玲,胡芬,付恩成.基于小波分析的中国人均GDP分析与预测.长江大学学报(自然科学版).2008,3：135-138.
[67]方秀纪,王宁.基于小波分析法的居民收入消费关系实证.商业时代.2007,13：4-5.
[68]刘海波,易东云.基于小波分析与分形理论的股价预测方法.财经论坛.2007,3(233)：110-111.
[69]刘海波,易东云.基于小波分析与分形理论的股价预测方法研究.统计与咨询.2006,5：16-17.
[70]谢品杰等.基于小波分析与广义自回归条件异方差模型的短期电价预测.电网技术.2008,8：96-100.
[71]潘菁,刘辉煌.基于小波支持向量机的经济预测模型.理论新探.2005,11(201)：14-15.
[72]傅强,彭选华,毛一波.金融时间序列变点探测的小波模极大值线方法.2007,8：140-144.
[73]熊正丰.金融时间序列分形维估计的小波方法.系统工程理论与实践.2002,12：48-53,122.
[74]戴稳胜,吕奇杰,David Pitt.金融时间序列预测模型——基于离散小波分解与支持向量回归的研究.理论新探(理论版).2007,7：4-7.
[75]褚万霞.金融数据的二进小波尺度积分析.宁夏师范学院学院学报(自然科学) 2008,6：70-73.
[76]李东,金朝嵩.美式看跌期权定价中的小波方法.经济数学.2003,12：25-30.
[77]闫桂芳,杜雪樵.期权定价的小波方法.大学数学.2007,10：70-74.
[78]聂坚,白永平,孙克.区域经济差异警戒水平的小波分析.经济纵横.2008,11(263)：99-102.
[80]董直庆,王林辉.我国通货膨胀和证券市场周期波动关系——基于小波变换频带分析方法的实证检验.中国工业经济.2008,11：35-44.
[81]董直庆,王林辉.我国证券市场与宏观经济波动关联性：基于小波变换和互谱分析的对比检验.金融研究.2008,8(338)：39-52.
[82]胡博,卫宏儒,廖福成.小波变换在股票分析中的应用.系统管理学报.2007,8：365-369.
[83]史成东,于兰兰,边敦新.小波变换在牛鞭效应消燥处理中的应用研究.计算机工程与应用.2008,44(21)：225-227.
[84]武杨,朱东华.小波变换在期货价格序贯相关分析中的应用.数学的实践与认识.2007,5：65-69.
[85]杜建卫,王超峰.小波分析方法在金融股票数据预测中的应用.数学的实践与认识.2008,4：68-75.
[86]张羽.小波分析在波浪理论中的应用研究.云南财经大学学报.2006,10：32-34.
[87]侯建荣,黄培清,宋国乡.小波分析在股票有偏随机游动中的应用.西北大学学报(自然科学版).2002,12：601-603.
[88]李彩荣,王志平,邵方明.小波分析在经济预测中的应用.辽宁大学学报(自然科学版).2003,1(30)：19-21.
[89]袁修贵,侯木舟.小波分析在证券分析中的应用.中南工业大学学报.2002,2：103-106.
[90]闫苗苗.小波分析在证券市场中的应用.商场现代化.2008,11(中旬刊)：343-344.
[91]张冕,万建平,李楚进.小波理论在VAR计算中的应用.应用数学2002,15(增)：116-119.
[92]钱舒.小波在股市数据分析中的应用.经济数学.2002,12：80-84.
[93]汪惠,王宁.小波在经济数据分析中的应用.山西财经大学学报.2002,6：97-100.
[94]张新红.正交尺度小波网络及在非线性经济系统预测中的应用.运筹与管理.2002,12：99-103.
[95]张新红.中国高新技术产业园区小波网络评价模型.湛江师范学院院报.2006,12：132-135.
[96]贾尚晖,李华.Black-Scholes期权定价方程的自适应小波算法.数学的实践与认识.2010,5：193-200.
[97]柳建芳,陈龙,李辰.改进的小波阀值去噪方法在外汇市场中的应用.2010：金融经济.90-92.
[98]马丽君,孙根年,王洁洁.基于本底趋势线与小波函数的中国旅游成长及多周期分析.旅游科学.2009,12：21-27.
[99]常振海,刘薇,张德生.基于小波的Mallat算法和异方差模型的人民币汇率预测.经济纵横.2010,17(317)：114-116.
[100]吴跃明.基于小波变换的LMSV模型对人民币汇率波动的研究.经济数学.2010,9：59-63.
[101]王春峰,姚宁,房振明.基于小波变换的多尺度跳跃识别与波动性估计研究.管理科学学报.2010,10：63-68.
[102]吴礼斌,崔岩岩.基于小波方差分解的沪深综指序列的特性分析.财经论坛.2010,23(323)：138-140.
[103]金秀,范美玲,刘烨.基于小波分析的多期择时能力模型及实证研究.运筹与管理.2010,2：119-125.
[104]金秀,王佳星,刘烨.基于小波分析的中国A,B股市场相关性研究.东北大学学报(自然科学版).2010,5：750-752,756.
[105]杨天宇,黄淑芬.基于小波降噪方法和季度数据的中国产出缺口估计.经济研究.2010,1：115-126.
[106]褚万霞.金融数据二进小波随机特性分析.科学技术与工程.2010,2：1179-1183.
[107]刘金全,蔡志远,李庆华.我国经济中“托宾效应”的实证检验——基于小波分析的新证据.大连理工大学学报.2010,12：1-5.
[108]张锴,贺丽娟.小波分析在股票趋势线中的应用.黄冈师范学院院报.2010,6：108-114.
[109]肖强.小波在金融时序预测中的应用.甘肃科技.2010,8：115-117.
[110]朱乾龙等.中国A股市场有效性的实证研究——基于小波降噪技术的分析.实证研究24-30.
[111]余宇新,杨大楷.股权分置改革对沪港市场联动性影响的研究——基于小波多分辨率的分析.统计与信息论坛.2009,6：33-37.
[112]曹跃群,周加斌,吴颖.基于小波变换的农民收入增长波动关系预测分析.华东经济 管理.2009,5：44-48.
[115]石柱鲜,黄红梅,朴粉丹.基于小波的我国经济周期波动的分析与预测.吉林大学社会科学学报.2009,5：135-142.
[117]金秀,刘洋.基于小波分析的多期夏普比率及实证研究.管理工程学报.2009,1：154-157.
[118]乔宝明,黄晶,范雯.基于小波分析的基金净值预测模型.统计与信息论坛.2010,11：71-74.
[119]张成虎,赵小虎.基于小波分析的可疑金融交易时间序列研究.现代管理科学.2009,7：102-104.
[120]常振海,张德生,刘薇.基于小波分析和GARCH模型的人民币汇率实证研究.山西大学学报(自然科学版).2009,32(3)：363-366.
[121]谢赤,黄曦,孙柏.基于小波分析与支持向量机的人民币汇率预测.湘潭大学学报(哲学社会科学版).2009,9：82-87.
[122]姜金贵,梁静国.基于小波神经网络的上市公司财务风险预警研究.商业研究.2009,2：97-99.
[123]汤凌冰,盛焕烨,汤凌霄.基于小波支持向量机的金融预测.湘潭大学自然科学学报.2009,3：12-15.
[125]殷光伟.小波与混理论相结合的汇率预测.财经论坛.2009,2(中旬刊)：357-358.
[126]石林梅,刘俊生.用傅里叶和小波对我国产出缺口的分析.青岛科技大学学报(社会科学版).2009,9：74-78.
[127]秦前清,杨宗凯,实用小波分析,西安：西安电子科技大学出版社,1998.
[128]唐卫宁,徐福缘.大批量定制合作伙伴的小波网络综合评价方法.计算机集成制造系统.2007,2：400-404.
[129]徐正国,张世英.高频金融数据“日历效应”的小波神经网络模型分析.数学的实践与认识.2007,8：1-5.
[130]李大营,许伟,陈荣秋.基于粗糙集和小波神经网络模型的房地产价格走势预测研究.经济与金融.2009,8：18-22.
[131]薛永刚.基于小波分解的汇率预测模型实证研究.经济纵横.2010,20：125-126.
[132]赵黎明,吴文清,刘嘉煜.基于小波分析的游客流量神经网络预测研究.系统工程学报.2006,4：221-224.
[133]龚亚琴.基于小波分析和RBF神经网络的铜价预测.陕西理工学院学报.2007,6：87-90.
[134]李婷婷.基于小波和模糊BP神经网络的金融股票市场预测.牡丹江大学学报.2009,6：149-151.
[135]刘丹红,张世英.基于小波神经网络的非线性误差校正模型及其预测.控制与决策.2006,10：1114-1118.
[136]廉新宇,吴立锋.基于小波神经网络的股票价格预测.财经论坛.2006,10(中旬刊)：331-332.
[137]王少平,谭本艳.中国的核心通货膨胀率及其动态调整行为.世界经济,2009年11期.
[138]黄燕,胡海鸥.核心通货膨胀方法的比较研究.上海金融,2006年10期.
[139]石柱鲜,吴泰岳,邓创.我国居民消费价格变动的主要影响因素分析.延边大学学报(社会科学版),2009年4月.
[140]黄燕.核心通货膨胀率的界定与衡量.上海金融,2004年10期.
[141]范跃进,冯维江.核心通货膨胀测量及宏观调控的有效性：对中国1995-2004的实证分析.管理世界,2005年5期.
[142]简泽.中国核心通货膨胀的估计.数量经济技术经济研究,2005年11期.
[143]刘义圣.我国宏观经济调控中利率微调政策的实效性研究.东南学术,2007年6期
[2]Y.Meyer, Ondelettes, et al. Functions splines. Seminaire EDP [J]. Ecole Ploy Technique, Paris, 1986.
[3]I.Daubechies. The wavelets transform,time-frequency localization and signal analysis[J]. IEEE Trans on PAMI,1989,11(7):674-693.
[4]Christiano L.J.,Fitzgerald T.J."The Band Pass Filter"[J].International Economic Review,June 2003,435-465.
[5]Conway Paul and Frame David."Spectral Analysis of New Zealand Output GapsUsing Fourier and Wavelet Techniques"[w].Reserve Bank of New Zealand Discussion Paper,DP2000/06, June 2000.
[6]I.Daubechies, Ten Lectures on Wavelets, CBMS-NSF regional series in applied mathematics. Philadelphia, PA:SIAM,1992.
[7]Wang Jian, Xu Denin and He Yuyao, Multidimensional Wavelet Networks Based on a Tensor Product Structure,控制理论与应用,2002,19(3)：381-386.
[8]Schleicher C."An introduction to Wavelets for Economists"[W].Bank of Canada Working Paper,2002,3.
[9]James B, Marco, Mauro and Willi. Instrumental variables and wavelet decompositions. Economic Modelling,2010,27:1498-1513.
[10]S. Raja Sethu, Saumitra N. Stock prices, inflation and output:Evidence from wavelet analysis. Economic Modelling.2009,26:1089-1092.
[11]Atilla, Sait and Elif. Analysis of sectoral credit default cycle dependency with wavelet networks:Evidence from Turkey. Economic Modelling.2009,26:1382-1388.
[12]Jeyanthi, Cornelis. Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997. International Review of Financial Analysis.2005,14:211-246.
[13]Mansur Mohammed and Omar. Systematic risk and time scales:New evidence from an application of wavelet approach to the emerging Gulf stock markets. International Review of Financial Analysis.2010,19:10-18.
[14]Victor, H.C. and Joseph. Wavelet domain correlation between the futures prices of natural gas and oil. The Quarterly Review of Economic and Finance.2010,50:408-414.
[15]Joseph, Robert, Hakan and His. Tests of long-range dependence in interest rates using wavelets. The Quarterly Review of Economic and Finance.2004,44:180-189.
[16]Larry. Using the Haar wavelet transform in the semiparametric specification of time series. Economic Modelling.2009,26:392-403.
[17]LIU Hongbo, ZHANG Hongwei. Wavelet Analytical Forecasting Method of Water Consumption. Transactions of Tianjin University.2004,9:206-208.
[18]Liu Bin, Dong Qinxi. WAVELET MODELING AND FORECASTING AND ITS APPLICATION IN THE CHINESE MONETARY MULTIPLIER.1999,8:917-923.
[19]L.Zunino,D.G.Pere,M.Garavaglia,O.A.Rosso. Wavelet entropy of stochastic processes. Statistical Mechanics and its Applications.2007,379(2):503-512.
[20]Mark J.Jensen. An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets. Journal of economic Dynamics and Control. 2000,24(3):361-387.
[21]Larry W.Taylor. Using the Haar wavelet transform in the semiparametric specification of time series. Economic Modelling.2009,26(2):392-403.
[22]L.Zunimo,D.G.Perez,M.Garavaglia,Osvaldo A.Rosso. Characterization of laser propagation through turbulent media by quantifiers based on the wavelet transform:Dynamic study. Statistic Mechanics and its Applications.2006,364(15):79-86.
[23]D.G.Perez, L.Zunimo,M.Garavaglia,O. A.Rosso. Wavelet entropy and fractional Brownian motion time series. Statistical Mechanics and its Applications.2006,365(2):282-288.
[24]Ding-wei Huang. Wavelet analysis in a traffic model. Statistical Mechanics and its Applications.2003,329(1-2):298-308.
[25]David Baqaee. Using wavelets to measure core inflation:The case of New Zealand Original Research Article. The North American Journal of Economics and Finance.2010,21(3):241-255.
[26]Emmanuel Haven,Xiaoquan Liu,Chenghu Ma,Liya Shen. Revealing the implied risk-neutral MGF from options:The wavelet method Original Research Article. Journal of Economic Dynamics and Control.2009,33(3):692-709.
[27]James B. Ramsey,Marco Gallegati,Mauro Gallegati,Willi Semmler. Instrumental variables and wavelet decompositions Original Research Article. Economic Modelling,2010,27(6): 1498-1513.
[28]Lui's Aguiar-Conraria,Maria Joana Soares. Business cycle synchronization and the Euro:A wavelet analysis Original Research Article. Journal of Macroeconomics,InPress,Corrected Proof, Available online.2011,21.
[29]Viviana Fernandez. Wavelet and SVM-based forecasts:An analysis of the U.S.metal and materials manufacturing industry Original Research Article. Resources Policy.2007,32(1-2): 80-89.
[30]Gilles Fay,Eric Moulines,Francois Roueff,Murad S.Taqqu. Estimators of long-memory: Fourier versus wavelets Original Research Article. Journal of Econometrics. 2009,151(2):159-177.
[31]Antonio Rua, Luis C. Nunes. International comovement of stock market returns:A wavelet analysis Original Research Article. Journal of Empirical Finance.2009,16(4):632-639.
[32]Luis Aguiar-Conraria,Nuno Azevedo,Maria Joana Soares. Using wavelets to decompose the time-frequency effects of monetary policy Original Research Article. Statistical Mechanics and its Applications.2008,387(12):2863-2878.
[33]Atilla Cifter,Sait Yilmazer,Elif Cifter. Analysis of sectoral credit default cycle dependency with wavelet networks:Evidence from Turkey Original Research Article. Economic Modelling. 2009,26(6):1382-1388.
[34]F.E.A.Leite,Raul Montagne,G.Corso,G.L.Vasconcelos,L.S.Lucena. Optimal wavelet filter for suppression of coherent noise with an application to seismic data Original Research Article. Statistical Mechanics and its Applications.2008,387(7):1439-1445.
[35]Mercedes Esteban-Bravo,Jose M.Vidal-Sanz. Computing continuous-time growth models with boundary conditions via wavelets Original Research Article. Journal of Economic Dynamics and Control.2007,31(11):3614-3643.
[36]Francis In,Sangbae Kim. Multiscale hedge ratio between the Australian stock and futures markets:Evidence from wavelet analysis Original Research Article. Journal of Multinational Financial Management.2006,16(4):411-423.
[37]Fabio Principato,Gaetano Ferrante.1/f Noise decomposition in random telegraph signals using the wavelet transform Original Research Article. Statistical Mechanics and its Applications. 2007,380(1):75-97.
[38]Jeyanthi Karuppiah.Cornelis A.Los. Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997 Original Research Article. International Review of Financial Analysis.2005,14(2):211-246.
[39]Sangbae Kim,Francis In. The relationship between stock returns and inflation:new evidence from wavelet analysis Original Research Article. Journal of Empirical Finance.2005,12(3): 435-444.
[40]Motohiro Yogo. Measuring business cycles:A wavelet analysis of economic time series. Economics Letters.2008,100(2):208-212.
[41]Zbigniew R.Struzik,Arno P.J.M.Siebes. Wavelet transform based multifractal formalism in outlier detection and localisation for financial time series Original Research. Article. Statistical Mechanics and its Applications.2002,15(3-4):388-402.
[42]Mansur Masih,Mohammed Alzahrani,Omar Al-Titi. Systematic risk and time scales:New evidence from an application of wavelet approach to the emerging Gulf stock markets Original Research Article. International Review of Financial Analysis.2010,19(1):10-18.
[43]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Differentiating intraday seasonalities through wavelet multi-scaling Original Research Article. Statistical Mechanics and its Applications.2001,289(3-4):543-556.
[44]Zbigniew R.Struzik. Wavelet methods in (financial) time-series processing Original Research Article. Statistical Mechanics and its Applications.2001,296(1-2):307-319. Atilla Cifter. Value-at-risk estimation with wavelet-based extreme value theory:Evidence from emerging markets. Statistical Mechanics and its Applications.2011,28.
[45]Antonios Antoniou,Constantinos E.Vorlow. Recurrence quantification analysis of wavelet pre-filtered index returns. Statistical Mechanics and its Applications.2004,344(1-2):257-262.
[46]V.Ibarra-Junquera,J.S.Murguia,P.Escalante-Minakata,H.C.Rosu. Application of multifractal wavelet analysis to spontaneous fermentation processes. Statistical Mechanics and its Applications.2008,387(12):2802-2808.
[47]Gongmeng Chen,Yoon K.Choi,Yong Zhou. Detections of changes in return by a wavelet smoother with conditional heteroscedastic volatility. Journal of Econometrics.2008,143(2): 227-262.
[48]S.Raja Sethu Durai,Saumitra N.Bhaduri.Stock prices. Inflation and output:Evidence from wavelet analysis. Economic Modelling.2009,26(5):1089-1092.
[49]E.Serrano,A.Figliola. Wavelet Leaders:A new method to estimate the multifractal singularity spectra. Statistical Mechanics and its Applications.2009,388(14):2793-2805.
[50]P.Manimaran,Prasanta K.Panigrahi,Jitendra C.Parikh. Multiresolution analysis of fluctuations in non-stationary time series through discrete wavelets. Statistical Mechanics and its Applications.2009,388(12):2306-2314.
[51]Sangbae Kim. Francis In On the relationship between changes in stock prices and bond yields in the G7 countries:Wavelet analysis. Journal of International Financial Markets. 2007,17(2):167-179.
[52]O.A.Rosso,M.T.Martin,A.Plastino. Brain electrical activity analysis using wavelet-based informational tools. Statistical Mechanics and its Applications.2002,313(3-4):587-608.
[53]Gabriel J.Power,Calum G.Turvey. Long-range dependence in the volatility of commodity futures prices:Wavelet-based evidence. Statistical Mechanics and its Applications.2010,389(l): 79-90.
[54]Victor Lux Tonn,H.C.Li,Joseph McCarthy. Wavelet domain correlation between the futures prices of natural gas and oil. The Quarterly Review of Economics and Finance. 2010,50(4):408-414.
[55]Rong Jiang,Hong Yan. Studies of spectral properties of short genes using the wavelet subspace Hilbert-Huang transform (WSHHT). Statistical Mechanics and its Applications. 2008,387(16-17):4223-4247.
[56]Silva,Luiz F.L.,Edmundo A. Forecasting oil price trends using wavelets and hidden Markov models. Energy Economics,2010,32(6):1507-1519.
[57]O.A., M.T. Martin, A.Evidence of self-organization in brain electrical activity using wavelet-based informational tools. Statistical Mechanics and its Applications.2005,347(1): 444-464.
[58]L.G.,A.Plastino,M. E. TorresWavelet analysis and nonlinear dynamics in a nonextensive setting. Statistical and Theoretical Physics.1997,246(3-4):487-509.
[59]T.Conlon,M.Crane,H.J.Ruskin. Wavelet multiscale analysis for Hedge Funds:Scaling and strategies. Statistical Mechanics and its Applications.2008,387(21):5197-5204.
[60]V.Ibarra-Junquera,P.Escalante-Minakata,J.S. Murguia,H-C.Rosu. Inferring mixed-culture growth from total biomass data in a wavelet approach. Statistical Mechanics and its Applications. 2006,370(2):777-792.
[61]Dj.Stratimirovi,S.Milo evi,S.Blesi,M.Ljubisavljevi. Wavelet analysis of discharge dynamics of fusimotor neurons. Statistical Mechanics and its Applications.2001,291(2-3):13-23.
[62]I.Antoniou,Vi.V.Ivanov,Va.V.Ivanov,P.V.Zrelov. Wavelet filtering of network traffic measurements. Statistical Mechanics and its Applications.2003,324(3-4):733-753.
[63]Marco Antonio Leonel Caetano,Takashi Yoneyama. Characterizing abrupt changes in the stock prices using a wavelet decomposition method. Statistical Mechanics and its Applications. 2007,383(2):519-526.
[64]Theo Naccache. Oil price cycles and wavelets. Energy Economics.2011,33(2):338-352.
[65]Fatemeh Ebrahimi, Muhammad Sahimi. Multiresolution wavelet coarsening and analysis of transport in heterogeneous media. Statistical Mechanics and its Applications.2002,316(1-4): 160-188.
[66]G.Corso,P.S.Kuhn,L.S.Lucena,Z.D. ThomeSeismic ground roll time-frequency filtering using the gaussian wavelet transform. Statistical Mechanics and its Applications.2003,318(3-4): 551-561.
[67]M.E.Pereyra,P.W.Lamberti,O.A.Rosso. Wavelet Jensen-Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures. Statistical Mechanics and its Applications.2007,379(1):122-132.
[68]Adel Sharkasi,Martin Crane,Heather J. Ruskin,Jose A. Matos. The reaction of stock markets to crashes and events:A comparison study between emerging and mature markets using wavelet transforms. Statistical Mechanics and its Applications.2006,368(2):511-521.
[69]Ingve Simonsen. Measuring anti-correlations in the nordic electricity spot market by wavelets. Statistical Mechanics and its Applications.2003,322:597-606.
[70]Christian Genest,Esterina Masiello,Karine Tribouley. Estimating copula densities through wavelets. Mathematics and Economics.2009,44(2):170-181.
[71]Ashok Razdan. Wavelet correlation coefficient of strongly correlated time series. Statistical and Theoretical Physics.2004,333:335-342.
[72]A.M.Korol,R.J.Rasia,O.A.Rosso.Alterations of thalassemic erythrocytes detected by wavelet entropy. Statistical Mechanics and its Applications.2007,375(1):257-264.
[73]Dj.Stratimirovic,S.Milosevic,S.Blesic,M.Ljubisavljevic. Wavelet transform analysis of time series generated by the stimulated neuronal activity. Statistical Mechanics and its Applications. 2007,374(2):699-706.
[74]A.Arneodo.What can we learn with wavelets about DNA sequences. Statistical Mechanics and its Applications.1998,249(1-4):439-448.
[75]Vera Pancaldi,Peter R.King,Kim Christensen. Wavelet-based upscaling of advection equations. Statistical Mechanics and its Applications.2008,387(19-20):4760-4770.
[76]C Rodrigues Neto,A Zanandrea,F.M Ramos, R.R Rosa,M.J.A Bolzan,L.D.A So. Multiscale analysis from turbulent time series with wavelet transform. Statistical Mechanics and its Applications.2001,295(1-2):215-218.
[77]Jan W.Kantelhardt,Diego Rybski,Stephan A.Zschiegner,Peter Braun,Eva Koscielny-Bunde, Valerie Livina, Shlomo Havlin,Armin Bunde. Multifractality of river runoff and precipitation: comparison of fluctuation analysis and wavelet methods. Statistical Mechanics and its Applications.2003,330(1-2):240-245.
[78]P.A.Ritto,J.J.Alvarado-Gil,J.G.Contreras. Scaling and wavelet-based analyses of the long-term heart rate variability of the Eastern Oyster. Statistical Mechanics and its Applications. 2005,349(1-2):291-301.
[79]O.A.Rosso,M.T.Martin,A.Plastino. Brain electrical activity analysis using wavelet-based informational tools(II):Tsallis non-extensivity and complexity measures. Statistical Mechanics and its Applications.2003,320:497-511.
[80]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Wavelets for Variance-Covariance Estimation. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. 2002:235-271.
[81]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Discrete Wavelet Transforms. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics.2002:96-160.
[82]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Wavelets and Stationary Processes. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics.2002:161-201.
[83]A.Arneodo,B-Audit,E.Bacry,S.Manneville,J.F.Muzy,S.G.Roux. Thermodynamics of fractal signals based on wavelet analysis:application to fully developed turbulence data and DNA sequences. Statistical and Theoretical Physics.1998,254(1-2):24-45.
[84]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Wavelet Denoising. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics.2002:202-234.
[85]A.Arneodo,E.Bacry,J.F.Muzy. The thermodynamics of fractals revisited with wavelets. Statistical and Theoretical Physics.1995,213(1-2):232-275.
[86]Yong Zhou,Alan T.K.Wan,Shangyu Xie,Xiaojing Wang.Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance. Journal of Econometrics.2010,159(1): 183-201.
[87]Jens Giesemann,Martin Greiner,Peter Lipa.Wavelet cascades Statistical and Theoretical Physics.1997,247(1-4):41-58.
[88]Esben P.Non-Parametric Estimation of Diffusion-Paths Using Wavelet Scaling Methods.Modeling and Control of Economic.2001:187-189.
[89]Arnold J.Mandell,Karen A.Selz,Michael F.Shlesinger.Wavelet transformation of protein hydrophobicity sequences suggests their memberships in structural families. Statistical and Theoretical Physics.1997,244(1-4):254-262.
[90]W. Korneta.The orthonormal wavelet representation of complex structures. Statistical Mechanics and its Applications.1992,185(1-4):111-115.
[91]A.Marrone,A.D.Polosa,GScioscia,S.Stramaglia,A.Zenzola.Wavelet analysis of blood pressure waves in vasovagal syncope.Statistical Mechanics and its Applications.1999,271(3-4): 458-469.
[92]Jan W.Kantelhardt,H.Eduardo Roman,Martin Greiner.Discrete wavelet approach to multifractality. Statistical and Theoretical Physics.1995,220(3-4):219-238.
[93]C.F.Caiafa,M.P.Sassano,A.N.Proto. Wavelet and Karhunen Loeve transformations applied to SAR signals and images. Statistical Mechanics and its Applications.2005,356(1):172-177.
[94]V.Ibarra-Junquera.Corrigendum to:"Application of multifractal wavelet analysis to spontaneous fermentation processes".Statistical Mechanics and its Applications.2008,387(18): 4731.
[95]Tests of long-range dependent in interest rates using wavelets.The Quarterly Review of Economics and Finance.2004,44(1):180-189.
[96]Stephan Schlueter.A long-term/short-term model for daily electricity prices with dynamic volatility.Energy Economics.2010,32(5):1074-1081.
[97]Viviana Fernandez. The CAPM and value at risk at different time-scales.International Review of Financial Analysis.2006,15(3):203-219.
[98]Antonio Rua. Measuring comovement in the time-frequency space.Journal of Macroeconomics.2010,32(2):685-691.
[99]Liljana Ferbar. Demand forecasting methods in a supply chain:Smoothing and denoising. International Journal of Production Economics.2009,118(1):49-54.
[100]Viviana Fernandez. The war on terror and its impact on the long-term volatility of financial markets. International Review of Financial Analysis.2008,17(1):1-26.
[101]Viviana Fernandez. The impact of major global events on volatility shifts:Evidence from the Asian crisis and 9/11.Economic Systems.2006,30(1):79-97.
[102]Enrico Capobianco. Empirical volatility analysis:feature detection and signal extraction with function dictionaries.Statistical Mechanics and its Applications.2003,319:495-518.
[103]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Scaling properties of foreign exchange volatility. Statistical Mechanics and its Applications.2001,289(1-2):249-266.
[104]Sangbae Kim,Francis In.A note on the relationship between industry returns and inflation through a multiscaling approach.Finance Research Letters.2006,3(1):73-78.
[105]P.Manimaran,Prasanta K.Panigrahi,Jitendra C.Parikh.Difference in nature of correlation between NASDAQ and BSE indices.Statistical Mechanics and its Applications.2008,387(23): 5810-5817.
[106]D.M.Nachane,Amlendu Kumar Dubey.The vanishing role of money in the macro-economy: An empirical investigation for India.Economic Modelling.2011,28(3):859-869.
[107]Cosmin Sinescu.Quantitative parameters which describe speech sound distortions due to inadequate dental mounting.Statistical Mechanics and its Applications.2008,387(5-6):1205-1217.
[108]P.Manimaran,Prasanta K.Panigrahi. Statistics of event by event fluctuations.Statistical Mechanics and its Applications.2010,389(18):3703-3710.
[109]M.E.Torres,G.Schlotthauer. Slight parameter changes detection in biological models:a multiresolution approach. Statistical Mechanics and its Applications.2003,324(3-4):645-664.
[110]Alan Kirman,Gilles Teyssiere. Testing for bubbles and change-points. Journal of Economic Dynamics and Control.2005,29(4):765-799.
[111]Francis In,Sangbae Kim,Ramazan Gencay. Investment horizon effect on asset allocation between value and growth strategies. Economic Modelling, In Press, Corrected Proof, Available online.2011,3.
[112]Osvaldo A.Rosso,Maria Liliana Mairal. Characterization of time dynamical evolution of electroencephalographic epileptic records. Statistical Mechanics and its Applications. 2002,312(3-4):469-504.
[113]R.Bellotti,F.De Carlo,M.de Tommaso,M.Lucente. Classification of spontaneous EEG signals in migraine. Statistical Mechanics and its Applications.2007,382(2):549-556.
[114]NyoNyo A.Kyaw,Cornelis A.Los,Sijing Zong. Persistence characteristics of Latin American financial markets. Journal of Multinational Financial Management.2006,16(3):269-290.
[115]Ramazan Gencay,Faruk Selcuk,Brandon Whitcher. Multiscale systematic risk Journal of International Money and Finance.2005,24(1):55-70.
[116]Robert DiSario,Hakan Saraoglu,Joseph McCarthy,Hsi Li. Long memory in the volatility of an emerging equity market:The case of Turkey. Journal of International Financial Markets, Institutions and Money.2008,18(4):305-312.
[117]Viviana Fernandez,Brian M.Lucey. Portfolio management under sudden changes in volatility and heterogeneous investment horizons. Statistical Mechanics and its Applications. 2007,375(2):612-624.
[1]石柱鲜、刘俊生、邓创.应用局面转移模型对我国2005年经济景气的分析与预测[J].吉大数量经济研究(2005卷),经济科学出版社,2007年
[2]石柱鲜,黄红梅,刘俊生,王立勇.我国经济周期波动分析与2006年～2007年主要经济指标预[A].经济蓝皮书2007.
[3]刘金全、刘志刚.具有Markov区制转移的向量误差修正模型及其应用[J].管理科学学报,2006,(5)12-20.
[4]石柱鲜、黄红梅、石庆华：关于中国潜在GDP与景气波动、通货膨胀的经验研究[J],《世界经济》,2004年第8期.
[5]孙巍,张屹山.全要素生产率的非参数测度与分解研究[A].21世纪数量经济学(第一卷)[C].北京：2000.256-264.
[6]孙巍.生产资源配置效率生产前沿面理论及其应用[M],北京：社会科学出版社,2000.
[7]崔锦泰著,程正兴译.《小波分析导论》[M]西安交通大学出版社,1995.
[8]徐佩霞,孙功宪,小波分析与应用实例,合肥：中国科学技术大学出版社,1996.
[9]刘贵忠等《小波分析及应用》[M]西安电子科技大学出版社,1995.
[10]姜彬,杨柱元.基于小波神经网络的经济增长预测.云南民族大学学报.2009,1：81-84.
[11]张鸿彦,林辉.基于小波神经网络的期权定价模型.云南大学学报.2007,7：716-720.
[12]汪克亮,杨力.基于小波神经网络的企业技术创新能力模糊综合评价.技术经济.2007,8：34-38.
[13]辛秀.基于小波神经网络的上市公司财务危机预测.计算机系统应用.2010,19(8)：191-194,127.
[14]钟满田,苏云.基于小波网络的非线性逼近股票分析方法.武汉工程大学学报.2007,1：81-83.
[15]刘兰娟,谢美萍.数据挖掘方法研究—对我国石油产量的预测分析.财经研究.2006,3：114-120.
[16]张新红.经济时间序列的连续参数小波网络预测模型.运筹与管理.2007,4：72-77.
[17]向小东.小波神经网络预测方法在石油期货价格预测中的应用研究.技术经济.2006,6：121-124.
[18]张鸿彦,林辉,姜彩楼.用混合小波网络和遗传算法对期权定价的研究.系统工程学报.2010,2：43-49.
[19]李全亮,李怀祖.正交尺度小波网络在我国税收预测中的应用.系统工程理论方法应用.2006,2：54-56,70.
[20]张新红,郑丕谔.正交小波网络及其在经济预测中的应用.系统工程学报.2006,4：196-200.
[21]黄师娟等.国际黄金价格的小波变换FAR预测模型.西安工业大学学报.2009,2：84-88.
[22]姚恩营,周玉国,孙国栋.国民收入的小波—非参数自回归预测模型.计算机工程与设计.2009,30(20)：4774-4776.
[23]余鹏翼,刘娜,施佳庆.国内上市公司“周内效应”的小波多分辨研究.中国证券期货.2009,8：8-9,22.
[24]刘薇,常振海.基于小波Mallat算法的外汇汇率预测模型.河北北方学院学报.2009, 10：69-72.
[25]许启发,蒋翠侠.基于小波变换的金融市场持续性特征研究.统计观察.2006,1(205)：85-87.
[26]徐梅,张世英.基于小波变换的时变长记忆SV模型估计方法研究.系统工程学报.2006,2：12-17,23.
[27]徐梅,张世英.基于小波变换的长记忆随机波动模型估计方法研究.中国管理科学.2006,2：7-14.
[28]陆克从,赵刚,王静.基于小波分析的AR-GREY预测模型及其应用.理论新探.2009,21(297)：20-22.
[29]侯守国,张世英.基于小波分析的股市高频互相关研究.中国管理科学.2006,6：1-6.
[30]刘娜,郑小洋,李为平.基于小波分析的经济数据预测.重庆工学院学报(自然科学),2009,11：169-172.
[31]高雷,任慧玉.基于小波分析的上证综指预测.财经论坛.2006,7(下)：116-117.
[32]侯守国,张世英.基于小波分析的中国股市高频长记忆研究.理论新探.2006,1(下)：9-11.
[33]龚亚琴.基于小波分析的住宅房产均价预测.陕西理工学院院报.2006,9：25-28.
[34]陈童,李积源,赵刚.基于小波分析方法的装备价格指数变化研究.微计算机信息(管控一体化).2006,22(2-3)：70-72.
[35]周潮.基于小波降噪技术的中国居民储蓄波动周期分析.西部金融.2009,11：61-62.
[36]吴礼斌,崔岩岩.基于小波协方差的中国股市波动序列相关性的实证分析.皖西学院学报.2009,10：5-8.
[37]谷政,江惠坤,褚保金.农业保险费率厘定的小波——非参数统计方法极其实证分析.系统工程.2008,8：39-43.
[38]杨凌.上海股票市场的基于小波去噪的混沌性检验.统计与信息论坛.2006,5：86-89,106.
[39]杨凌.深圳证券市场混沌性探测的前提：小波去噪.理论新探.2006,4(下)：21-23.
[40]杨凌,颜日初.我国证券市场经济混沌探测研究——一种基于小波变换的噪声处理.中南财经政法大学学报.2006,2(155)：41-44.
[41]殷光伟,郑丕谔.小波包与混沌集成的股票市场预测新方法.管理工程学报.2006,2：117-119.
[42]邓凯旭,宋宝瑞.小波变换在金融数据分析中的应用.数理统计与管理.2006,3：215-219.
[43]周景宏,郗伟东,禹海兰.小波方法在股市分析中的应用.东北电力大学学报.2006,4：47-51.
[44]刘达,王尔康,牛东晓.小波分析和考虑外生变量的广义自回归条件异方差模型在电价预测中的应用.电网技术.2009,10：99-104.
[45]刘丹红等.LMSV模型波动的长记忆与相关性的小波分析.理论新探(理论版).2007,6：4-7.
[46]高辉清.非线性预测方法：小波网络.预测.1995,6：46-48.
[47]奚振斐,宋国乡.分形小波在外汇投资市场中的应用.华南理工大学学报(自然科学版).2006,11：123-126.
[48]高静,张世英.高频时间序列基于小波分析的预测.理论新探.2006,9(221)：4-5.
[49]章前等.股票价格与成交量关系的小波分析.昆明理工大学学报(理工版).2007,12：95-97.
[50]宋宜美,奚振斐,宋国乡.股票市场分布特性的小波方法研究.西安电子科技大学学报(自然科学版).2002,12：826-829.
[51]戴稳胜,吕奇杰,徐曼文.股指期货信息内含股价变动信息的挖掘——小波框架与支持向量回归的金融建模应用.统计研究.2008,2：78-83.
[52]隋学深,杨忠海.股指时间序列突变点小波检测研究.哈尔滨商业大学学报(自然科学版).2007,4：249-253.
[53]薛超,李星野,雷蕾.沪深港股市相关性的小波分析.数学的实践与认识.2008,8：25-32.
[54]郑中发.基于连续双正交样条小波的股价指数奇异点检测.科技情报开发与经济.2006,20(16)：91-92.
[55]李果,单泪源,陈芝.基于事例推理和小波网络的大规模定制产品快速成本估算方法研究.
[56]奚振斐,王立平,宋国乡.基于小波变换的期权市场中的时间价值序列预测.西北大学学报.2007,12：966-968.
[57]杜修平,那一沙.基于小波变换和SOM聚类的证券板块划分.华南金融电脑.2008,9：84-86.
[58]李晖,郭晨,金洪章.基于小波变换和均生函数周期外推组合模式的非平稳时间序列分 析与长期预测.控制理论与应用.2008,4：283-288.
[59]林志勇,张维强,徐晨.基于小波变换与MOBP的股价预测.计算机工程与应用.2008,44(16)：215-217.
[60]杨稣,史耀媛,宋恒.基于小波变换域的SVM股市时间序列预测算法.科学技术与工程.2008,6：3171-3174.
[61]史建平,张传灵,宋国乡.基于小波的汇率波动序列长记忆性研究.现代电子技术.2007,1(240)：173-175.
[62]孙晋众,林健.基于小波的能源消费弹性系数预测方法.沈阳航空工业学院院报.2007,6：78-81.
[63]许启发,蒋翠侠,张世英.基于小波多分辨分析的协整建模理论与方法的扩展.统计研究.2007,8：92-96.
[64]李迎锋,陈辉,潘林.基于小波多分辨分析方法的高阶矩CAPM研究.理论新探(理论版).2007,3：32-36.
[65]赵晋理.基于小波分析的股票市场分析.太原师范学院学报(自然科学版).2007,6：5-7.
[66]刘晏玲,胡芬,付恩成.基于小波分析的中国人均GDP分析与预测.长江大学学报(自然科学版).2008,3：135-138.
[67]方秀纪,王宁.基于小波分析法的居民收入消费关系实证.商业时代.2007,13：4-5.
[68]刘海波,易东云.基于小波分析与分形理论的股价预测方法.财经论坛.2007,3(233)：110-111.
[69]刘海波,易东云.基于小波分析与分形理论的股价预测方法研究.统计与咨询.2006,5：16-17.
[70]谢品杰等.基于小波分析与广义自回归条件异方差模型的短期电价预测.电网技术.2008,8：96-100.
[71]潘菁,刘辉煌.基于小波支持向量机的经济预测模型.理论新探.2005,11(201)：14-15.
[72]傅强,彭选华,毛一波.金融时间序列变点探测的小波模极大值线方法.2007,8：140-144.
[73]熊正丰.金融时间序列分形维估计的小波方法.系统工程理论与实践.2002,12：48-53,122.
[74]戴稳胜,吕奇杰,David Pitt.金融时间序列预测模型——基于离散小波分解与支持向量回归的研究.理论新探(理论版).2007,7：4-7.
[75]褚万霞.金融数据的二进小波尺度积分析.宁夏师范学院学院学报(自然科学) 2008,6：70-73.
[76]李东,金朝嵩.美式看跌期权定价中的小波方法.经济数学.2003,12：25-30.
[77]闫桂芳,杜雪樵.期权定价的小波方法.大学数学.2007,10：70-74.
[78]聂坚,白永平,孙克.区域经济差异警戒水平的小波分析.经济纵横.2008,11(263)：99-102.
[80]董直庆,王林辉.我国通货膨胀和证券市场周期波动关系——基于小波变换频带分析方法的实证检验.中国工业经济.2008,11：35-44.
[81]董直庆,王林辉.我国证券市场与宏观经济波动关联性：基于小波变换和互谱分析的对比检验.金融研究.2008,8(338)：39-52.
[82]胡博,卫宏儒,廖福成.小波变换在股票分析中的应用.系统管理学报.2007,8：365-369.
[83]史成东,于兰兰,边敦新.小波变换在牛鞭效应消燥处理中的应用研究.计算机工程与应用.2008,44(21)：225-227.
[84]武杨,朱东华.小波变换在期货价格序贯相关分析中的应用.数学的实践与认识.2007,5：65-69.
[85]杜建卫,王超峰.小波分析方法在金融股票数据预测中的应用.数学的实践与认识.2008,4：68-75.
[86]张羽.小波分析在波浪理论中的应用研究.云南财经大学学报.2006,10：32-34.
[87]侯建荣,黄培清,宋国乡.小波分析在股票有偏随机游动中的应用.西北大学学报(自然科学版).2002,12：601-603.
[88]李彩荣,王志平,邵方明.小波分析在经济预测中的应用.辽宁大学学报(自然科学版).2003,1(30)：19-21.
[89]袁修贵,侯木舟.小波分析在证券分析中的应用.中南工业大学学报.2002,2：103-106.
[90]闫苗苗.小波分析在证券市场中的应用.商场现代化.2008,11(中旬刊)：343-344.
[91]张冕,万建平,李楚进.小波理论在VAR计算中的应用.应用数学2002,15(增)：116-119.
[92]钱舒.小波在股市数据分析中的应用.经济数学.2002,12：80-84.
[93]汪惠,王宁.小波在经济数据分析中的应用.山西财经大学学报.2002,6：97-100.
[94]张新红.正交尺度小波网络及在非线性经济系统预测中的应用.运筹与管理.2002,12：99-103.
[95]张新红.中国高新技术产业园区小波网络评价模型.湛江师范学院院报.2006,12：132-135.
[96]贾尚晖,李华.Black-Scholes期权定价方程的自适应小波算法.数学的实践与认识.2010,5：193-200.
[97]柳建芳,陈龙,李辰.改进的小波阀值去噪方法在外汇市场中的应用.2010：金融经济.90-92.
[98]马丽君,孙根年,王洁洁.基于本底趋势线与小波函数的中国旅游成长及多周期分析.旅游科学.2009,12：21-27.
[99]常振海,刘薇,张德生.基于小波的Mallat算法和异方差模型的人民币汇率预测.经济纵横.2010,17(317)：114-116.
[100]吴跃明.基于小波变换的LMSV模型对人民币汇率波动的研究.经济数学.2010,9：59-63.
[101]王春峰,姚宁,房振明.基于小波变换的多尺度跳跃识别与波动性估计研究.管理科学学报.2010,10：63-68.
[102]吴礼斌,崔岩岩.基于小波方差分解的沪深综指序列的特性分析.财经论坛.2010,23(323)：138-140.
[103]金秀,范美玲,刘烨.基于小波分析的多期择时能力模型及实证研究.运筹与管理.2010,2：119-125.
[104]金秀,王佳星,刘烨.基于小波分析的中国A,B股市场相关性研究.东北大学学报(自然科学版).2010,5：750-752,756.
[105]杨天宇,黄淑芬.基于小波降噪方法和季度数据的中国产出缺口估计.经济研究.2010,1：115-126.
[106]褚万霞.金融数据二进小波随机特性分析.科学技术与工程.2010,2：1179-1183.
[107]刘金全,蔡志远,李庆华.我国经济中“托宾效应”的实证检验——基于小波分析的新证据.大连理工大学学报.2010,12：1-5.
[108]张锴,贺丽娟.小波分析在股票趋势线中的应用.黄冈师范学院院报.2010,6：108-114.
[109]肖强.小波在金融时序预测中的应用.甘肃科技.2010,8：115-117.
[110]朱乾龙等.中国A股市场有效性的实证研究——基于小波降噪技术的分析.实证研究24-30.
[111]余宇新,杨大楷.股权分置改革对沪港市场联动性影响的研究——基于小波多分辨率的分析.统计与信息论坛.2009,6：33-37.
[112]曹跃群,周加斌,吴颖.基于小波变换的农民收入增长波动关系预测分析.华东经济 管理.2009,5：44-48.
[115]石柱鲜,黄红梅,朴粉丹.基于小波的我国经济周期波动的分析与预测.吉林大学社会科学学报.2009,5：135-142.
[117]金秀,刘洋.基于小波分析的多期夏普比率及实证研究.管理工程学报.2009,1：154-157.
[118]乔宝明,黄晶,范雯.基于小波分析的基金净值预测模型.统计与信息论坛.2010,11：71-74.
[119]张成虎,赵小虎.基于小波分析的可疑金融交易时间序列研究.现代管理科学.2009,7：102-104.
[120]常振海,张德生,刘薇.基于小波分析和GARCH模型的人民币汇率实证研究.山西大学学报(自然科学版).2009,32(3)：363-366.
[121]谢赤,黄曦,孙柏.基于小波分析与支持向量机的人民币汇率预测.湘潭大学学报(哲学社会科学版).2009,9：82-87.
[122]姜金贵,梁静国.基于小波神经网络的上市公司财务风险预警研究.商业研究.2009,2：97-99.
[123]汤凌冰,盛焕烨,汤凌霄.基于小波支持向量机的金融预测.湘潭大学自然科学学报.2009,3：12-15.
[125]殷光伟.小波与混理论相结合的汇率预测.财经论坛.2009,2(中旬刊)：357-358.
[126]石林梅,刘俊生.用傅里叶和小波对我国产出缺口的分析.青岛科技大学学报(社会科学版).2009,9：74-78.
[127]秦前清,杨宗凯,实用小波分析,西安：西安电子科技大学出版社,1998.
[128]唐卫宁,徐福缘.大批量定制合作伙伴的小波网络综合评价方法.计算机集成制造系统.2007,2：400-404.
[129]徐正国,张世英.高频金融数据“日历效应”的小波神经网络模型分析.数学的实践与认识.2007,8：1-5.
[130]李大营,许伟,陈荣秋.基于粗糙集和小波神经网络模型的房地产价格走势预测研究.经济与金融.2009,8：18-22.
[131]薛永刚.基于小波分解的汇率预测模型实证研究.经济纵横.2010,20：125-126.
[132]赵黎明,吴文清,刘嘉煜.基于小波分析的游客流量神经网络预测研究.系统工程学报.2006,4：221-224.
[133]龚亚琴.基于小波分析和RBF神经网络的铜价预测.陕西理工学院学报.2007,6：87-90.
[134]李婷婷.基于小波和模糊BP神经网络的金融股票市场预测.牡丹江大学学报.2009,6：149-151.
[135]刘丹红,张世英.基于小波神经网络的非线性误差校正模型及其预测.控制与决策.2006,10：1114-1118.
[136]廉新宇,吴立锋.基于小波神经网络的股票价格预测.财经论坛.2006,10(中旬刊)：331-332.
[137]王少平,谭本艳.中国的核心通货膨胀率及其动态调整行为.世界经济,2009年11期.
[138]黄燕,胡海鸥.核心通货膨胀方法的比较研究.上海金融,2006年10期.
[139]石柱鲜,吴泰岳,邓创.我国居民消费价格变动的主要影响因素分析.延边大学学报(社会科学版),2009年4月.
[140]黄燕.核心通货膨胀率的界定与衡量.上海金融,2004年10期.
[141]范跃进,冯维江.核心通货膨胀测量及宏观调控的有效性：对中国1995-2004的实证分析.管理世界,2005年5期.
[142]简泽.中国核心通货膨胀的估计.数量经济技术经济研究,2005年11期.
[143]刘义圣.我国宏观经济调控中利率微调政策的实效性研究.东南学术,2007年6期
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |