Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Maximum Likelihood Estimation For Vector Autoregressive Moving Average Models
Download Maximum Likelihood Estimation For Vector Autoregressive Moving Average Models full books in PDF, epub, and Kindle. Read online Maximum Likelihood Estimation For Vector Autoregressive Moving Average Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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 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 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 of VARMA Models Using a State-Space EM Algorithm by : Konstantinos Metaxoglou
Download or read book Maximum Likelihood Estimation of VARMA Models Using a State-Space EM Algorithm written by Konstantinos Metaxoglou and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a state-space representation for vector autoregressive moving-average models that enables maximum likelihood estimation using the EM algorithm. We obtain closed-form expressions for both the E- and M-steps; the former requires the Kalman filter and a fixed-interval smoother, and the latter requires least squares-type regression. We show via simulations that our algorithm converges reliably to the maximum, whereas gradient-based methods often fail because of the highly nonlinear nature of the likelihood function. Moreover, our algorithm converges in a smaller number of function evaluations than commonly used direct-search routines. Overall, our approach achieves its largest performance gains when applied to models of high dimension. We illustrate our technique by estimating a high-dimensional vector moving-average model for an efficiency test of California's wholesale electricity market.
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 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 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 The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models by : Rüşdü Saraçoğlu
Download or read book The Maximum Likelihood Estimation of Parameters in Mixed Autoregressive Moving-average Multivariate Models written by Rüşdü Saraçoğlu and published by . This book was released on 1977 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: "No abstract available"--Federal Reserve Bank of Minneapolis web site.
Book Synopsis Time Series and Statistics by : John Eatwell
Download or read book Time Series and Statistics written by John Eatwell and published by Palgrave Macmillan. This book was released on 1990-07-23 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Time Series Analysis by : James D. Hamilton
Download or read book Time Series Analysis written by James D. Hamilton and published by Princeton University Press. This book was released on 2020-09-01 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, self-contained overview of time series analysis for students and researchers The past decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This textbook synthesizes these advances and makes them accessible to first-year graduate students. James Hamilton provides comprehensive treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems—including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter—in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results. This invaluable book starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
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 Hidden Periodic Autoregressive - Moving Average Models in Time Series Data by : Michael R. Grupe
Download or read book Hidden Periodic Autoregressive - Moving Average Models in Time Series Data written by Michael R. Grupe and published by . This book was released on 1979 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Maximum Likelihood Estimation of Parameters of an Autoregressive Process with Moving Average Residuals and Other Covariance Matrices with Linear Structure by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Maximum Likelihood Estimation of Parameters of an Autoregressive Process with Moving Average Residuals and Other Covariance Matrices with Linear Structure written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1973 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Some Estimators in Moving Average Models by : Stanford University. Department of Statistics
Download or read book Asymptotic Properties of Some Estimators in Moving Average Models written by Stanford University. Department of Statistics and published by . This book was released on 1975 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author considers estimation procedures for the moving average model of order q. Walker's method uses k sample autocovariances (k> or = q). Assume that k depends on T in such a way that k nears infinity as T nears infinity. The estimates are consistent, asymptotically normal and asymptotically efficient if k = k (T) dominates log T and is dominated by (T sub 1/2). The approach in proving these theorems involves obtaining an explicit form for the components of the inverse of a symmetric matrix with equal elements along its five central diagonals, and zeroes elsewhere. The asymptotic normality follows from a central limit theorem for normalized sums of random variables that are dependent of order k, where k tends to infinity with T. An alternative form of the estimator facilitates the calculations and the analysis of the role of k, without changing the asymptotic properties.
