<p></p><p>Mirror-Image and Invariant Distributions in Arma Models</p><p>Jonathan D. Cryer; John C. Nankervis; N. E. Savin</p><p>Econometric Theory, Vol. 5, No. 1. (Apr., 1989), pp. 36-52.</p><p>  </p><p>ARMA Memory Index Modeling of Economic Time Series</p><p>Herman J. Bierens</p><p>Econometric Theory, Vol. 4, No. 1. (Apr., 1988), pp. 35-59.</p><p>  </p><p>Noninvertibility and Pseudo-Maximum Likelihood Estimation of Misspecified ARMA Models</p><p>B. M. Pötscher</p><p>Econometric Theory, Vol. 7, No. 4. (Dec., 1991), pp. 435-449.</p><p>  </p><p>On Estimating an ARMA Model with an MA Unit Root</p><p>B. P. M. McCabe; S. J. Leybourne</p><p>Econometric Theory, Vol. 14, No. 3. (Jun., 1998), pp. 326-338.</p><p>  </p><p>A New Method for Obtaining the Autocovariance of an ARMA Model: An Exact Form Solution</p><p>M. Karanasos</p><p>Econometric Theory, Vol. 14, No. 5. (Oct., 1998), pp. 622-640.</p><p>  </p><p>The GLS Transformation Matrix and a Semi-Recursive Estimator for the Linear Regression Model with Arma Errors</p><p>John W. Galbraith; Victoria Zinde-Walsh</p><p>Econometric Theory, Vol. 8, No. 1. (Mar., 1992), pp. 95-111.</p><p>  </p><p>ARIMA Processes with ARIMA Parameters</p><p>Carlo Grillenzoni</p><p>Journal of Business & Economic Statistics, Vol. 11, No. 2. (Apr., 1993), pp. 235-250.</p><p>  </p><p>Prediction Intervals for ARIMA Models</p><p>Ralph D. Snyder; J. Keith Ord; Anne B. Koehler</p><p>Journal of Business & Economic Statistics, Vol. 19, No. 2. (Apr., 2001), pp. 217-225.</p><p>  </p><p>Parameter Estimation for Infinite Variance Fractional ARIMA</p><p>Piotr S. Kokoszka; Murad S. Taqqu</p><p>The Annals of Statistics, Vol. 24, No. 5. (Oct., 1996), pp. 1880-1913.</p><p>  </p><p>On the Order Determination of ARIMA Models</p><p>T. Ozaki</p><p>Applied Statistics, Vol. 26, No. 3. (1977), pp. 290-301.</p><p>  </p><p>Asymptotic Behavior for Partial Autocorrelation Functions of Fractional ARIMA Processes</p><p>Akihiko Inoue</p><p>The Annals of Applied Probability, Vol. 12, No. 4. (Nov., 2002), pp. 1471-1491.</p><p>  </p><p>Temporal Disaggregation of Time Series: An ARIMA-Based Approach</p><p>Victor M. Guerrero</p><p>International Statistical Review / Revue Internationale de Statistique, Vol. 58, No. 1. (Apr., 1990), pp. 29-46.</p><p>  </p><p>Bayesian Comparison of ARIMA and Stationary ARMA Models</p><p>John Marriott; Paul Newbold</p><p>International Statistical Review / Revue Internationale de Statistique, Vol. 66, No. 3. (Dec., 1998), pp. 323-336.</p><p>  </p><p>ARIMA模型全称为自回归移动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)，是由博克思(Box)和詹金斯(Jenkins)于70年代初提出的一著名时间序列预测方法，所以又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA（p，d，q）称为差分自回归移动平均模型，AR是自回归, p为自回归项; MA为移动平均，q为移动平均项数，d为时间序列成为平稳时所做的差分次数。ARIMA（p，d，q）模型是ARMA（p，q）模型的扩展。</p><p>  </p><p>ARIMA模型的基本思想是：将预测对象随时间推移而形成的数据序列视为一个随机序列，用一定的数学模型来近似描述这个序列。这个模型一旦被识别后就可以从时间序列的过去值及现在值来预测未来值。现代统计方法、计量经济模型在某种程度上已经能够帮助企业对未来进行预测。</p><p>  </p><p>  </p><p>【典藏下载系列2】世界顶尖计量经济学著作集锦:</p><p>  </p><p>http://www.pinggu.org/bbs/thread-257614-1-1.html</p>

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