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Maximum Likelihood Estimation For A First Order Bifurcating Autoregressive Process With Exponential Errors
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Book Synopsis Maximum Likelihood Estimation for a First-Order Bifurcating Autoregressive Process with Exponential Errors by : Jin Zhou
Download or read book Maximum Likelihood Estimation for a First-Order Bifurcating Autoregressive Process with Exponential Errors written by Jin Zhou and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exact and asymptotic distributions of the maximum likelihood estimator of the autoregressive parameter in a first-order bifurcating autoregressive process with exponential innovations are derived. The limit distributions for the stationary, critical and explosive cases are unified via a single pivot using a random normalization. The pivot is shown to be asymptotically exponential for all values of the autoregressive parameter.
Book Synopsis Likelihood Analysis of a First Order Autoregressive Model With Exponential Innovations by : Bent Nielsen
Download or read book Likelihood Analysis of a First Order Autoregressive Model With Exponential Innovations written by Bent Nielsen and published by . This book was released on 1999 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper derives the exact distribution of the maximum likelihood estimator of a first order linear autoregression with exponential innovations. We show that even if the process is stationary, the estimator is $T$-consistent, where $T$ is the sample size. In the unit root case the estimator is $T^{2}$-consistent, while in the explosive case the estimator is $ rho ^{T}$-consistent. Further, the likelihood ratio test statistic for a simple hypothesis on the autoregressive parameter is asymptotically uniform for all values of the parameter.
Book Synopsis On the Estimation of the First-Order Autoregressive Parameter by : F. Giannella
Download or read book On the Estimation of the First-Order Autoregressive Parameter written by F. Giannella and published by . This book was released on 1984 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents Cramer-Rao (C-R) bounds for parameter estimation of a first-order autoregressive process from a finite record of data. The authors used these bounds to evaluate the performance of Maximum Likelihood Estimation (MLE) and linear prediction approaches. Some estimators use low-rank approximation of an estimated covariance matrix. The latter estimates are based on the method of Tufts and Kumaresan. In this document a zero selection techniques in the last step of the procedure was added. The low-rank, high order, linear prediction estimator performs better than the other methods which were tested, when the pole is close to the unit circle. It is slightly biased and its variance is small and close to the variance given by the C-R bound for unbiased estimators. For a small number of samples (25 to 100) this estimator performs substantially better than the MLE. (Author).
Download or read book Mathematical Reviews written by and published by . This book was released on 2006 with total page 1052 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Misspecified Models by : T. Fomby
Download or read book Maximum Likelihood Estimation of Misspecified Models written by T. Fomby and published by Elsevier. This book was released on 2003-12-12 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Book Synopsis Gaussian Likelihood Estimation for Nearly Nonstationary AR(1) Processes by : Dennis D. Cox
Download or read book Gaussian Likelihood Estimation for Nearly Nonstationary AR(1) Processes written by Dennis D. Cox and published by . This book was released on 1987 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: An asymptotic analysis is presented for estimation in the three parameter first order autoregressive model, where the parameters are the mean, autoregressive coefficient, and variance of the shocks. The nearly nonstationary asymptotic model is considered wherein the autoregressive coefficient tends to 1 as sample size tends to infinity. Three different estimators are considered: the exact gaussian maximum likelihood estimator, the conditional maximum likelihood or least squares estimator, and some naive estimators. It is shown that the estimators converge in distribution to analogous estimators for a continuous time Ornstein-Uhlenbeck process. Simulation results show that the MLE has smaller asymptotic mean squared error than the other two, and that the conditional maximum likelihood estimator gives a very poor estimator of the process mean. Keywords: Likelihood estimation; Autoregressive processes; Nearly nonstationary time series; Ornstein Uhlenbeck process.
Book Synopsis Autoregressive Model Inference in Finite Samples by : Hans Einar Wensink
Download or read book Autoregressive Model Inference in Finite Samples written by Hans Einar Wensink and published by . This book was released on 1996 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Charles M.. Beach Publisher :Kingston, Canada : Department of Economics, Queen's University ISBN 13 : Total Pages :36 pages Book Rating :4.:/5 (851 download)
Book Synopsis Full Maximum Likelihood Estimation of Second-order Autoregressive Error Models by : Charles M.. Beach
Download or read book Full Maximum Likelihood Estimation of Second-order Autoregressive Error Models written by Charles M.. Beach and published by Kingston, Canada : Department of Economics, Queen's University. This book was released on 1977 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimality and Other Asymptotic Properties of the Maximum Likelihood Estimator in the First Order Autoregressive Process by : Michael Monsour
Download or read book Optimality and Other Asymptotic Properties of the Maximum Likelihood Estimator in the First Order Autoregressive Process written by Michael Monsour and published by . This book was released on 1986 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Exact Maximum Likelihood Estimation of an Arma(1, 1) Model with Incomplete Data by : Chunsheng Ma
Download or read book Exact Maximum Likelihood Estimation of an Arma(1, 1) Model with Incomplete Data written by Chunsheng Ma and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a first-order autoregressive and first-order moving average model with nonconsecutively observed or missing data, the closed form of the exact likelihood function is obtained, and the exact maximum likelihood estimation of parameters is derived in the stationary case.
Book Synopsis Maximum Likelihood Estimation for an Autoregressive Process with Missing Observations by : Suan-Boon Tan
Download or read book Maximum Likelihood Estimation for an Autoregressive Process with Missing Observations written by Suan-Boon Tan and published by . This book was released on 1979 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three methods are proposed for estimation of the parameters of an autogressive process of order p with missing observations. These methods are based on the maximum likelihood approach and use the EM algorithm, the Newton-Raphson method and the method of scoring, which are applied to the likelihood equations. Finally, comparison on those methods is also discussed. (Author).
Book Synopsis Asymptotic Distribution of Maximum Likelihood Estimators in Linear Models with Autoregressive Disturbances by : Clifford G. Hildreth
Download or read book Asymptotic Distribution of Maximum Likelihood Estimators in Linear Models with Autoregressive Disturbances written by Clifford G. Hildreth and published by . This book was released on 1966 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hildreth and Lu proposed a method for obtaining maximum likelihood estimates of linear model coefficients whose disturbances are generated by a stationary linear first-order autoregressive process with unknown autoregression coefficient. Until the present study was performed, consistency was the only property that had been shown for these estimates. This memorandum shows that the estimates of coefficients of independent variables and the estimate of the autoregression coefficient have a limiting joint multivariate-normal distribution, with the estimate of autoregression distributed independently of the estimates of coefficients of independent variables. This asymptotic covariance matrix of these latter estimates is the same as that of the best linear unbiased estimates for a model in which the autoregression coefficient is known. (Author).
Book Synopsis Exact Maximum Likelihood Estimation of Observation-driven Econometric Models by : Francis X. Diebold
Download or read book Exact Maximum Likelihood Estimation of Observation-driven Econometric Models written by Francis X. Diebold and published by . This book was released on 1996 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
Book Synopsis Maximum Likelihood Estimation of Higher-Order Integer-Valued Autoregressive Processes by : Ruijun Bu
Download or read book Maximum Likelihood Estimation of Higher-Order Integer-Valued Autoregressive Processes written by Ruijun Bu and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 70-722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) specification with binomial thinning and Poisson innovations, we examine both the asymptotic efficiency and finite sample properties of the ML estimator in relation to the widely used conditional least squares (CLS) and YuleWalker (YW) estimators. We conclude that, if the Poisson assumption can be justified, there are substantial gains to be had from using ML especially when the thinning parameters are large.
Book Synopsis Maximum Likelihood Estimation for Multivariate Autoregressive Model by : D. T. Pham
Download or read book Maximum Likelihood Estimation for Multivariate Autoregressive Model written by D. T. Pham and published by . This book was released on 1990 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Iterative Procedures for Exact Maximum Likelihood Estimation in the First-order Gaussian Moving Average Model by : Stanford University. Department of Statistics
Download or read book Iterative Procedures for Exact Maximum Likelihood Estimation in the First-order Gaussian Moving Average Model written by Stanford University. Department of Statistics and published by . This book was released on 1990 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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).
