Download or read book Parameter Estimation of Nearly Non stationary Autoregressive Processes written by Michiel J.L. de Hoon and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Maximum Likelihood Estimation for Nearly Non Stationary Stable Autoregressive Processes written by Rong-Mao Zhang and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Parameter Estimation of Nearly Nonstationary Autoregressive Processes January Juni 1995 written by M. J. L. de Hoorn and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Parameter estimation for nearly nonstationary AR 1 processes written by and published by . This book was released on 1992 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Studies in the Identification and Forecasting of Non stationary Time Series written by Dean W. Wichern and published by . This book was released on 1969 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Simple and Efficient Estimation of Parameters of Non Gaussian Autoregressive Processes written by Steven M. Kay and published by . This book was released on 1986 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new technique for the estimation of autoregressive filter parameters of a non-Gaussian autoregressive process is proposed. The probability density function of the driving noise is assumed to be known. The new technique is a two-stage procedure motivated by maximum likelihood estimation. It is computationally much simpler than the maximum likelihood estimator and does not suffer from convergence problems. Computer simulations indicate that unlike the least squares or linear prediction estimators, the proposed estimator is nearly efficient, even for moderately sized data records. By a slight modification the proposed estimator can also be used in the case when the parameters of the driving noise probability density function are not known. Keywords: Parameter estimation; Autoregressive processes; Non Gaussian processes; Maximum likelihood estimator; Weighted least squares; Efficiency robustness.

Download or read book Using R for Principles of Econometrics written by Constantin Colonescu and published by Lulu.com. This book was released on 2017-12-28 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a beginner's guide to applied econometrics using the free statistics software R. It provides and explains R solutions to most of the examples in 'Principles of Econometrics' by Hill, Griffiths, and Lim, fourth edition. 'Using R for Principles of Econometrics' requires no previous knowledge in econometrics or R programming, but elementary notions of statistics are helpful.

Download or read book Estimation in Threshold Autoregressive Models with Nonstationarity written by Jiti Gao and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a class of new nonlinear threshold autoregressive models with both stationary and nonstationary regimes. Existing literature basically focuses on testing for a unit-root structure in a threshold autoregressive model. Under the null hypothesis, the model reduces to a simple random walk. Parameter estimation then becomes standard under the null hypothesis. How to estimate parameters involved in an alternative nonstationary model, when the null hypothesis is not true, becomes a nonstandard estimation problem. This is mainly because models under such an alternative are normally null recurrent Markov chains. This paper thus proposes to establish a parameter estimation method for such nonlinear threshold autoregressive models with null recurrent structure. Under certain assumptions, we show that the ordinary least squares (OLS) estimates of the parameters involved are asymptotically consistent. Furthermore, it can be shown that the OLS estimator of the coefficient parameter involved in the stationary regime can still be asymptotically normal while the OLS estimator of the coefficient parameter involved in the nonstationary regime has a nonstandard asymptotic distribution. In the limit, the rate of convergence in the stationary regime is n-1 = 4, whereas it is n-1 in the nonstationary regime. The proposed theory and estimation method is illustrated by both simulated and real data examples.

Download or read book Asymptotic Inference for Nearly Non Stationary Time Series written by Isabel Llatas and published by . This book was released on 1987 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Econometric Theory and Practice written by P. C. B. Phillips and published by Cambridge University Press. This book was released on 2006-01-09 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essays in this book explore important theoretical and applied advances in econometrics.

Download or read book Selected Proceedings of the Symposium on Inference for Stochastic Processes written by Ishwar V. Basawa and published by IMS. This book was released on 2001 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation of the Parameters in Stationary Autoregressive Processes After Hard Limiting written by Benjamin Kedem and published by . This book was released on 1977 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: The parameters of a stationary AR(p) process are estimated after clipping. This estimation is based in part on the number of certain runs in the binary series. Very little precision is lost due to this quantization but the expected number of arithmetical operations which are saved is at least (p+2)n where counting a run is considered as an operation and n is the series size. (Author).

Download or read book Non Gaussian Autoregressive Type Time Series written by N. Balakrishna and published by Springer Nature. This book was released on 2022-01-27 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.

Download or read book Parameter Estimation for Noncausal and Heavy Tailed Autoregressive Processes written by Matthew Calder and published by . This book was released on 1998 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book On Some Problems of Estimation and Prediction for Non stationary Time Series written by James Thomas McClave and published by . This book was released on 1971 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many techniques are available for estimating the parameters of linear stationary time series. The effect of some of these estimators on the least squares predictor for some future value of the series is examined. We have obtained approximations for the increase in prediction error due to parameter estimation in several cases for which no exact expression could be found. An efficient estimator for the parameters in a first order autoregressive-moving average model is developed by making use of a linear function of the autocorrelations. For more general models we conclude that efficient estimation is so difficult to attain that first consideration in many estimation prediction problems should be given to ease of calculation. Numerous unsolved estimation and prediction problems remain for non-stationary time series. We consider the logistic growth process, which is used extensively as an economic and population growth model, in detail. Current estimation procedures for the logistic process' parameters make no reference to an error structure. We propose a probability structure consistent with the realistic properties of the series. We then use this structure to obtain estimators from three different observational standpoints: (1) that of observation at equidistant time points, (2) that of continuous observation, and (3) that of arrival time observation. For observation types (1) and (2) we have used a modification of maximum likelihood procedures to obtain estimators having most of the usual properties associated with maximum likelihood estimators. A computer program was written for type (1) to solve the intractable estimation equations, using the Newton-Rhapson procedure. Observation of arrival times is shown not to lead to any useful estimation procedures. We also examine the effect of the estimators calculated from the observations taken at equal time intervals (type (1) above) on the error of prediction. The procedure developed for observation type (1) is then compared by means of example to an estimation procedure developed by Rhodes. The logistic model is also fitted by each method to the population of conterminous United States. The estimates are then used to obtain predictions of the population of conterminous United States in 1970. We conclude from the results that the effort required to calculate the maximum likelihood estimates is worthwhile. We further conjecture that the methods developed may be applicable to other growth processes.

Download or read book Consistent Estimation and Order Selection for Nonstationary Autoregressive Processes with Stable Innovations written by Peter Burridge and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A possibly nonstationary autoregressive process, of unknown finite order, with possibly infinite-variance innovations is studied. The ordinary least squares autoregressive parameter estimates are shown to be consistent, and their rate of convergence, which depends on the index of stability, is established. We also establish consistency of lag-order selection criteria in the nonstationary case. A small experiment illustrates the relative performance of different lag-length selection criteria in finite samples.

Download or read book Parameter Estimation in Non linear Time Series written by Lianfen Qian and published by . This book was released on 1996 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: