Download or read book Estimation Procedures for Linear Models with Autoregressive and Moving Average Errors written by Askar H. Choudhury and published by . This book was released on 1990 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book ARMA Model Identification written by ByoungSeon Choi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last two decades, considerable progress has been made in statistical time series analysis. The aim of this book is to present a survey of one of the most active areas in this field: the identification of autoregressive moving-average models, i.e., determining their orders. Readers are assumed to have already taken one course on time series analysis as might be offered in a graduate course, but otherwise this account is self-contained. The main topics covered include: Box-Jenkins' method, inverse autocorrelation functions, penalty function identification such as AIC, BIC techniques and Hannan and Quinn's method, instrumental regression, and a range of pattern identification methods. Rather than cover all the methods in detail, the emphasis is on exploring the fundamental ideas underlying them. Extensive references are given to the research literature and as a result, all those engaged in research in this subject will find this an invaluable aid to their work.

Download or read book Statistical Inference In Time Series Regression Models written by S. Durga Prasad and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book attempts to develope some new inferential procedures for time series regression models.An inferential method for a time series linear regression model with auto correlated disturbances using quarterly data, has been developed by proposing a test based on internally studentized residuals.Two modified estimation procedures have been proposed for time series regression models involving MA (1) and MA (q) process errors.Autoregressive moving averages and autoregressive conditionally heteroscadastic (ARCH) processesses have been specified systematically with their characteristics. The generalized ARCH model is specified and the effect of error structure on ARCH model has been explained. Two modified tests for detecting the problem of ARCH errors have been developed by using Box-pierce-lying test statistics based on internally studentized residuals. A new estimation procedure has been developed for ARCH model by using an interactive technique

Download or read book Lasso for Autoregressive and Moving Average Coeffi ci ents Via Residuals of Unobservable Time Series written by Hanh Nguyen and published by . This book was released on 2018 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains four topics in time series data analysis. First, we propose the oracle model selection for autoregressive time series when the observations are contaminated with trend. An adaptive least absolute shrinkage and selection operator (LASSO) type model selection method is used after the trend is estimated by non-parametric B-splines method. The first step is to estimate the trend by B-splines method and then calculate the detrended residuals. The second step is to use the residuals as if they were observations to optimize an adaptive LASSO type objective function. The oracle properties of such an Adaptive Lasso model selection procedure are established; that is, the proposed method can identify the true model with probability approaching one as the sample size increases, and the asymptotic properties of estimators are not affected by the replacement of observations with detrended residuals. The extensive simulation studies of several constrained and unconstrained autoregressive models also confirm the theoretical results. The method is illustrated by two time series data sets, the annual U.S. tobacco production and annual tree ring width measurements. Second, we generalize our first topic to a more general class of time series using the autoregressive and moving-average (ARMA) model. The ARMA model class is the building block for stationary time series analysis. We adopt the two-step method non-parametric trend estimation with B-spline and model selection and model estimation with the adaptive LASSO. We prove that such model selection and model estimation procedure possesses the oracle properties. Another important objective of this topic is forecasting time series with trend. We approach the forecasting problem by two methods: the empirical method by using the one-step ahead prediction in time series and the bagging method. Our simulation studies show that both methods are efficient with the decreased mean square error when the sample size increases. Simulation studies are adopted to illustrate the asymptotic result of our proposed method for model selection and model estimation with twelve ARMA(p, q) models, in which p an q are in the range from 1 to 15. The method is also illustrated by two time series data sets from the New York State Energy Research and Development Authority (NYSERDA), a public benefit corporation which offers data and analysis to help New Yorkers increase energy efficiency. Third, we propose a new model class, which is motivated by lag effects of covariates on the dependent variable. Our paper aims at providing more accurate statistical analysis for the relationship, for example, between the outcome of an event that occurs once every several years and the covariates that have observations every year. Lag effects have received a great deal of attention since Almon (1965) proposed linear distributed lag models to model the dependence of time series on several regressors from a correlated sequence. Motivated by the linear distributed lag model, we propose distributed generalized linear models as well as the estimation procedure for the model coefficients. The estimators from our proposed procedure are shown to be oracle or asymptotically efficient. Simulation studies confirm the asymptotic properties of the estimators and present consequences of model misspecification as well as better model prediction accuracy. The application is illustrated by analysis of the presidential election data in 2016. Fourth, we aim to provide an easy-to-use data analysis procedure for linear regression with non-independent errors. In practice, errors in regression model may be non-independent. In such situation, it is usually suitable to assume that the error terms for the model follow a time series structure. In fact, this type of model structure (reffered as RegARMA) has received great interests from researchers. Pierce (1971) discussed a nonlinear least squares estimation of RegARMA; Greenhouse et al. (1987) studied biological rhythm data by using the RegARMA model. Recently, Wu and Wang (2012) used the shrinkage estimation procedure to analyze data using RegARMA. However, in the literature the trend factor of the time series has not been considered. We will use the same idea of the two step-procedure as in the first project and the second project for our approach. We first estimate the trend of the time series by using a non-parametric method such as B-spline or linear Kernel. We then use the adaptive LASSO method for model selection and model estimation of the linear part and the time series error part. Simulation results show that our approach works quite well. However, it would be very interesting and challenging to improve the estimations and extend the estimation method to more complicated models, which will be the focus of the future research.

Download or read book Applied Linear Statistical Models written by Michael H. Kutner and published by McGraw-Hill/Irwin. This book was released on 2005 with total page 1396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

Download or read book Convenient Methods for Estimation of Linear Regression Models with MA 1 Errors written by Glenn M. MacDonald and published by Kingston, Ont. : Institute for Economic Research, Queen's University. This book was released on 1983 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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).

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:

Download or read book Introduction to Time Series Analysis written by Mark Pickup and published by SAGE Publications. This book was released on 2014-10-15 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing time series methods and their application in social science research, this practical guide to time series models is the first in the field written for a non-econometrics audience. Giving readers the tools they need to apply models to their own research, Introduction to Time Series Analysis, by Mark Pickup, demonstrates the use of—and the assumptions underlying—common models of time series data including finite distributed lag; autoregressive distributed lag; moving average; differenced data; and GARCH, ARMA, ARIMA, and error correction models. “This volume does an excellent job of introducing modern time series analysis to social scientists who are already familiar with basic statistics and the general linear model.” —William G. Jacoby, Michigan State University

Download or read book Parameter Estimation for Mixed Linear Models with a First Order Autoregressive Covariance Structure written by Kevin Michael Wade and published by . This book was released on 1990 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Time Series Analysis Forecasting Control 3 E written by and published by Pearson Education India. This book was released on 1994-09 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a complete revision of a classic, seminal, and authoritative text that has been the model for most books on the topic written since 1970. It explores the building of stochastic (statistical) models for time series and their use in important areas of application -forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.

Download or read book Forecasting principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Download or read book Introduction to Statistical Time Series written by Wayne A. Fuller and published by John Wiley & Sons. This book was released on 1995-12-29 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter. Major topics include: * Moving average and autoregressive processes * Introduction to Fourier analysis * Spectral theory and filtering * Large sample theory * Estimation of the mean and autocorrelations * Estimation of the spectrum * Parameter estimation * Regression, trend, and seasonality * Unit root and explosive time series To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.

Download or read book Time Series Analysis written by George E. P. Box and published by . This book was released on 1976 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction and summary; Stochastic models and their forecasting; The autocorrelation function and spectrum; Linear stationary models; Linear nonstationary models; Forecasting; Stochastic model building; Model identification; Model estimation; Model diagnostic checking; Seasonal models; Transfer function models; Identification fitting, and checking of transfer function models.

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:

Download or read book Time Series Models written by Andrew C. Harvey and published by . This book was released on 1981 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stationary stochastic process and their properties in the time domain; The frequency domain; State space models and the kalman filter; Estimation of autoregressive moving average models; Model building and prediction; Selected topics in time series regression.

Download or read book Recent Advances in Regression Methods written by Hrishikesh D. Vinod and published by . This book was released on 1981 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression model; Criteria for good regression estimators: MSE, consistency, stability, robustness, minimaxity and Bayesian 'MELO' ness; Restricted least squares and bayesian regression; Autoregressive moving average (ARMA) regression errors and heteroscedasticity; Multicollinearity and stability of regression coefficients; Stein-rule shrinkage estimator; Ridge regression; Further ridge theory and solutions; Estimation of polynomial distributed lag models; Multiple sets of regression squations; Simultaneous equations models; Canonical correlations, and discriminant analysis with ridge-type modification; Improved estimators under nonnormal errors and robust regression.