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Estimation By Maximum Likelihood In Autorgressive Moving Average Models In The Time And Frequency Domains
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Book Synopsis Estimation by Maximum Likelihood in Autorgressive Moving Average Models in the Time and Frequency Domains by : Stanford University. Department of Statistics
Download or read book Estimation by Maximum Likelihood in Autorgressive Moving Average Models in the Time and Frequency Domains written by Stanford University. Department of Statistics and published by . This book was released on 1975 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models by : Fereydoon Ahrabi
Download or read book Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models written by Fereydoon Ahrabi and published by . This book was released on 1979 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to derive asymptotically efficient estimates for the autoregressive matrix coefficients and moving average covariance matrices of the vector autoregressive moving average (VARMA) models in both time and frequency domains. To do this we shall apply the Newton-Raphson and scoring methods to the maximum likelihood equations derived from modified likelihood functions under the Gaussian Assumption.
Book Synopsis Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1978 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vector autoregressive moving average model is a multivariate stationary stochastic process where the unobservable multivariate process consists of independently identically distributed random vectors. The coefficient matrices and the covariance matrix are to be estimated from an observed sequence. Under the assumption of normality the method of maximum likelihood is applied to likelihoods suitably modified for techniques in the frequency and time domains. Newton-Raphson and scoring iterative methods are presented.
Book Synopsis Maximum Likelihood Estimation of the Covariances of the Vector Moving Average Models in the Time and Frequency Domains by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Maximum Likelihood Estimation of the Covariances of the Vector Moving Average Models in the Time and Frequency Domains written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1978 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vector moving average process is a stationary stochastic process, where the unobservable process consists of independently identically distributed random variables. The matrix parameters are estimated from the observations. The likelihood function is derived under normality and to solve the maximum likelihood equations the Newton-Raphson and Scoring methods are used. The estimation problem is considered in the time and frequency domains. Asymptotic efficiency of the estimates is established.
Book Synopsis Estimation for Autoregressive Moving Average Models in the Time and Frequency Domains by : T. W. Anderson
Download or read book Estimation for Autoregressive Moving Average Models in the Time and Frequency Domains written by T. W. Anderson and published by . This book was released on 1975 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimators for Arma and ARFIMA Models by : Michael A. Hauser
Download or read book Maximum Likelihood Estimators for Arma and ARFIMA Models written by Michael A. Hauser and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Monte Carlo study is undertaken to analyze the small sample properties of two time domain and two frequency domain estimators for low order autoregressive fractionally integrated moving average Gaussian models, ARFIMA (p,d,q). More specifically (1,0,0), (0,0,1), (0,d,0), various (2,0,0), (0,0,2), and (1,d,0), (0,d,1) models. The time domain estimators are the exact maximum likelihood for demeaned data, EML, the associated modified profile likelihood, MPL. The frequency domain estimators are the Whittle estimator with, WLT, and without tapered data, WL. The length of the series investigated is 100. First the pile-up effect of the estimators is documented. Then the estimators are compared with respect to their mean square error, bias, and empirical confidence level. We conclude that the tapered version of the Whittle likelihood is a reliable estimator for ARMA and ARFIMA models. Despite of small losses in performance in case of "well-behaved" models it performs well for more "difficult" models. The modified profile likelihood has certain advantages over the WLT but is computationally more demanding, especially for long series. In relation to the EML, the MPL is essentially equivalent or slightly more favorable for ARMA models. For fractionally integrated models, in particular, both the WLT and the MPL dominate clearly the EML. The WL cannot be recommended in general. There are serious deficiencies for large ranges of parameters, especially for MA, and AR(2) models with roots "close" to +1 or -1. The EML exhibits a potential large negative bias of the fractional integration parameter, which tends to be enlarged if AR or MA parameters are present in the model. Generally, caution is required for ARMA(1,1) models where the polynomials have almost canceling roots, and with respect to inference for models with a MA root close to +1. This is especially the case for the time domain estimators, EML and MPL.
Book Synopsis Time Series in the Time Domain by : Edward James Hannan
Download or read book Time Series in the Time Domain written by Edward James Hannan and published by North Holland. This book was released on 1985 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardbound. In this volume prominent workers in the field discuss various time series methods in the time domain. The topics included are autoregressive-moving average models, control, estimation, identification, model selection, non-linear time series, non-stationary time series, prediction, robustness, sampling designs, signal attenuation, and speech recognition. This volume complements Handbook of Statistics 3: Time Series in the Frequency Domain.
Book Synopsis Analysis of Economic Time Series by : Marc Nerlove
Download or read book Analysis of Economic Time Series written by Marc Nerlove and published by Academic Press. This book was released on 2014-05-10 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Economic Time Series: A Synthesis integrates several topics in economic time-series analysis, including the formulation and estimation of distributed-lag models of dynamic economic behavior; the application of spectral analysis in the study of the behavior of economic time series; and unobserved-components models for economic time series and the closely related problem of seasonal adjustment. Comprised of 14 chapters, this volume begins with a historical background on the use of unobserved components in the analysis of economic time series, followed by an Introduction to the theory of stationary time series. Subsequent chapters focus on the spectral representation and its estimation; formulation of distributed-lag models; elements of the theory of prediction and extraction; and formulation of unobserved-components models and canonical forms. Seasonal adjustment techniques and multivariate mixed moving-average autoregressive time-series models are also considered. Finally, a time-series model of the U.S. cattle industry is presented. This monograph will be of value to mathematicians, economists, and those interested in economic theory, econometrics, and mathematical economics.
Book Synopsis Maximum Likelihood Estimation of Stochastic Linear Difference Equations with Autoregressive Moving Average Errors by : Greg Reinsel
Download or read book Maximum Likelihood Estimation of Stochastic Linear Difference Equations with Autoregressive Moving Average Errors written by Greg Reinsel and published by . This book was released on 1976 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is proposed for the estimation of a general class of scalar linear time series models. The model takes the form of a stochastic difference equation for the dependent variable with exogenous variable inputs, and the disturbances are autocorrelated through an autoregressive moving average process. In the present paper an asymptotically efficient yet computationally simple estimation procedure (in the time domain) is derived for this model. The resulting estimator is shown to be asymptotically equivalent to the maximum likelihood estimator and to possess a limiting multivariate normal distribution. (Author).
Book Synopsis An Efficient Algorithm for Maximum Likelihood Estimation for Autoregressive Moving Average Model by : Pham Dinh Tuan
Download or read book An Efficient Algorithm for Maximum Likelihood Estimation for Autoregressive Moving Average Model written by Pham Dinh Tuan and published by . This book was released on 1986 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Vector Autoregressive Moving Average Models by : Greg Reinsel
Download or read book Maximum Likelihood Estimation of Vector Autoregressive Moving Average Models written by Greg Reinsel and published by . This book was released on 1976 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is presented for the estimation of the parameters in the vector autoregressive moving average time series model. The estimation procedure is derived from the maximum likelihood approach and is based on Newton-Raphson techniques applied to the likelihood equations. The resulting two-step Newton-Raphson procedure is computationally simple, involving only generalized least squares estimation in the second step. This Newton-Raphson estimator is shown to be asymptotically efficient and to possess a limiting multivariate normal distribution. (Author).
Book Synopsis A Handbook of Time-series Analysis, Signal Processing and Dynamics by : D. S. G. Pollock
Download or read book A Handbook of Time-series Analysis, Signal Processing and Dynamics written by D. S. G. Pollock and published by Academic Press. This book was released on 1999 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: CD-ROM contains: Pascal and C code and programs -- bibliography of the book -- text of book -- tutorials.
Book Synopsis The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models by : Rusdu Saracoglu
Download or read book The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models written by Rusdu Saracoglu and published by . This book was released on 1977 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series by : K. Dzhaparidze
Download or read book Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series written by K. Dzhaparidze and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: . . ) (under the assumption that the spectral density exists). For this reason, a vast amount of periodical and monographic literature is devoted to the nonparametric statistical problem of estimating the function tJ( T) and especially that of leA) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value t;; of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl' . . . , X , usually depends in n a complicated manner on the cyclic frequency). . This fact often presents difficulties in applying the obtained estimate t;; of the function I to the solution of specific problems rela ted to the process X . Theref ore, in practice, the t obtained values of the estimator t;; (or an estimator of the covariance function tJ~( T» are almost always "smoothed," i. e. , are approximated by values of a certain sufficiently simple function 1 = 1
Book Synopsis Existence of Maximum Likelihood Estimators in Autoregressive and Moving Average Models by : STANFORD UNIV CA DEPT OF STATISTICS.
Download or read book Existence of Maximum Likelihood Estimators in Autoregressive and Moving Average Models written by STANFORD UNIV CA DEPT OF STATISTICS. and published by . This book was released on 1980 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is given a sufficient condition on the observations from a scalar autoregressive process such that the maximum likelihood estimate exists and corresponds to a stationary process. A sufficient condition is given for the likelihood function to fail to have a maximum. In a moving average model the maximum likelihood estimates always exist. Some results are obtained for the autoregressive moving average model and vector models. It is shown that the solution to the sample Yule-Walker equations in the autoregressive case yield a stationary process. (Author).
Book Synopsis Maximum Likelihood Estimation of Multivariate Autoregressive-Moving Average Models by : M. S. Phadke
Download or read book Maximum Likelihood Estimation of Multivariate Autoregressive-Moving Average Models written by M. S. Phadke and published by . This book was released on 1977 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms for computing the exact likelihood function of n successive observation vectors from an s-variate autoregressive moving average process of order (p, q) are developed. A quasi-Newton method is used to maximize the likelihood function with respect to the parameters of the process. Monte Carlo simulations are performed to compare the parameter estimates obtained by maximizing the exact likelihood function versus those obtained by maximizing various approximate forms of the likelihood function. (Author).
Book Synopsis The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models by : Rüşdü Saracoglu
Download or read book The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models written by Rüşdü Saracoglu and published by . This book was released on 1977 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: "No abstract available"--Federal Reserve Bank of Minneapolis web site.
