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Rank Based Estimation For Autoregressive Moving Average Time Series Models
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Book Synopsis Rank-Based Estimation for Autoregressive Moving Average Time Series Models by : Beth Andrews
Download or read book Rank-Based Estimation for Autoregressive Moving Average Time Series Models written by Beth Andrews and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449-1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.
Book Synopsis Time Series Models by : Andrew C. Harvey
Download or read book Time Series Models written by Andrew C. Harvey and published by . This book was released on 1993 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: A companion volume to "The Econometric Analysis of Time" series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.
Book Synopsis Estimation of Parameters in Integrated Autoregressive-Moving Average Time Series Models. Part 1. Autoregressive Models by : G. E. P. Box
Download or read book Estimation of Parameters in Integrated Autoregressive-Moving Average Time Series Models. Part 1. Autoregressive Models written by G. E. P. Box and published by . This book was released on 1972 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: A very useful class of stochastic models for the representation of time series such as occur in economics, business, and engineering are the integrated auto-regressive moving average processes. The paper provides a discussion of estimation of the auto-regressive parameters from the Bayesian viewpoint. (Author).
Book Synopsis Estimation of Parameters in Integrated Autoregressive-moving Average Time Series Models by : George E. P. Box
Download or read book Estimation of Parameters in Integrated Autoregressive-moving Average Time Series Models written by George E. P. Box and published by . This book was released on 1972 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation of Parameters in Integrated Autoregressive-Moving Average Time Series Models. Part 2. Moving Average and Mixed Models by : G. E. P. Box
Download or read book Estimation of Parameters in Integrated Autoregressive-Moving Average Time Series Models. Part 2. Moving Average and Mixed Models written by G. E. P. Box and published by . This book was released on 1972 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: A very useful class of stochastic models for the representation of time series such as occur in economics, business, and engineering are the integrated autoregressive moving average processes. The paper discusses the estimation of parameters in moving average or mixed models from the Bayesian point of view. (Author).
Book Synopsis Estimation of Parameters in Integrated Autoregressive-moving Average Time Series Models by : George E. P. Box
Download or read book Estimation of Parameters in Integrated Autoregressive-moving Average Time Series Models written by George E. P. Box and published by . This book was released on 1972 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Unified Approach to ARMA (Autoregressive-Moving Average) Model Identification and Preliminary Estimation by : G. T. Wilson
Download or read book A Unified Approach to ARMA (Autoregressive-Moving Average) Model Identification and Preliminary Estimation written by G. T. Wilson and published by . This book was released on 1983 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reviews several different methods for identifying the orders of autoregressive-moving average models for time series data. The case is made that these have a common basis, and that a unified approach may be found in the analysis of a matrix G, defined to be the covariance matrix of forecast values. The estimation of this matrix is considered, emphasis being placed on the use of high order autoregression to approximate the predictor coefficients. Statistical procedures are proposed for analyzing G, and identifying the model orders. A simulation example and three sets of real data are used to illustrate the procedure, which appears to be very useful as a tool for order identification and preliminary model estimation. (Author).
Book Synopsis Generalized Method of Moments Estimation by : Laszlo Matyas
Download or read book Generalized Method of Moments Estimation written by Laszlo Matyas and published by Cambridge University Press. This book was released on 1999-04-13 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. Contributors to the volume include well-known authorities in the field based in North America, the UK/Europe, and Australia. The work is likely to become a standard reference for graduate students and professionals in economics, statistics, financial modeling, and applied mathematics.
Book Synopsis Time Series Econometrics by : Klaus Neusser
Download or read book Time Series Econometrics written by Klaus Neusser and published by Springer. This book was released on 2018-05-30 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.
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 Robust Statistics for Signal Processing by : Abdelhak M. Zoubir
Download or read book Robust Statistics for Signal Processing written by Abdelhak M. Zoubir and published by Cambridge University Press. This book was released on 2018-11-08 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the benefits of robust statistics for signal processing using this unique and authoritative text.
Book Synopsis Time Series Models for Short-Term Forecasting Performance Indicators by : Arno Palmrich
Download or read book Time Series Models for Short-Term Forecasting Performance Indicators written by Arno Palmrich and published by GRIN Verlag. This book was released on 2009-11-30 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diploma Thesis from the year 2007 in the subject Business economics - Business Management, Corporate Governance, grade: highest grade (ausgezeichnet), University of Applied Sciences Kufstein Tirol, course: Economics Statistics, language: English, abstract: Managers use forecasting in budgeting time and resources. In this thesis, various advanced time series models are constructed, computed and tested for adequacy. This thesis serves as a practical guide to regression and time series analysis. It seeks to demonstrate how to approach problems according to scientific standards to students who are familiar with SPSS(R) but beginners in regression and time series analysis. Bibliographic notes of classical works and more recent academic advances in time series analysis are provided throughout the text. The research question that this thesis seeks to answer can be formulated in its shortest version as: "How can the management of Dalian Chemson Chemical Products Co; Ltd. use existing company data to make short-term predictions about net sales, Cost of Goods Sold (COGS), and net contribution?" More specifically, this thesis seeks to provide different tools (models) for forecasting the P&L entries net sales, COGS, and net contribution a few months ahead. This author's approach is based on various versions of two models: One model will forecast net sales and the other model will predict COGS. The expected net contribution is simply defined as the difference between the predictions of these two models. In chapter 4.3 an ordinary least squares regression version of the two models has been computed. In chapter 4.6 a weighted least squares regression has been applied to the models. Autoregressions have been computed in chapter 4.7.1 and two Autoregressive Integrated Moving Average (ARIMA) versions have been constructed in chapter 4.7.6. The various versions of the models have then been compared against each other. The version that fits the data best will be used in forecasting.
Book Synopsis Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets by : Gustavo Fruet Dias
Download or read book Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets written by Gustavo Fruet Dias and published by . This book was released on 2017 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models are feasible alternatives when forecasting with many predictors. We show that VARMA models outperform the AR(1), ARMA(1,1), Bayesian VAR, and factor models, considering different model dimensions.Supplement is available at: 'https://ssrn.com/abstract=2830838' https://ssrn.com/abstract=2830838.
Book Synopsis Development of Traditional and Rank-based Algorithms for Linear Models with Autoregressive Errors and Multivariate Logistic Regression with Spatial Random Effects by : Shaofeng Zhang
Download or read book Development of Traditional and Rank-based Algorithms for Linear Models with Autoregressive Errors and Multivariate Logistic Regression with Spatial Random Effects written by Shaofeng Zhang and published by . This book was released on 2017 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear models are the most commonly used statistical methods in many disciplines. One of the model assumptions is that the error terms (residuals) are independent and identically distributed. This assumption is often violated and autoregressive error terms are often encountered by researchers. The most popular technique to deal with linear models with autoregressive errors is perhaps the autoregressive integrated moving average (ARIMA). Another common approach is generalized least squares, such as Cochrane-Orcutt estimation and Prais-Winsten estimation. However, these usually have poor behaviors when fitting small samples. To address this problem, a double bootstrap method was proposed by McKnight et al. (2000). One purpose of this study is to transfer their algorithm from Fortran to the R computing environment and, ultimately develop an R software package, which, as R, is freeware and runs on all platforms. Furthermore, this study fixes some flaws of the original method and develops a rank-based alternative, which is robust in terms of resistance to outliers. An R package is created and the usage is demonstrated via examples. Monte Carlo studies for different sample sizes (20, 30, 50, and 100) show that both the original and robust algorithm have the expected properties, even for small sample sizes. In addition to the original algorithm, we also develop a robust rank-based alternative algorithm. By adopting the rank-based estimator, this new algorithm is resistant to outliers. This is the most important feature of the rank-based estimator. In the same time, this estimator does not loss much efficiency compared to the ordinary least square (OLS) estimator, when the random errors are normally distributed. Comparison of this new algorithm and the original one is made by simulation studies under different settings. This research also includes an application of the variational approximation in fitting multivariate logistic regression with spatial effects in the Bayesian framework. Variational approximation is much faster than Markov Chain Monte Carlo (MCMC), with- out losing accuracy. Hence this technique becomes an important alternative to MCMC. Spatial models, such as Conditional Autoregressive (CAR) Models, are extremely popular in characterizing spatial dependencies when datasets are collected over aggregated spatial regions, such as, counties, census tracts, zip codes, etc. Modeling spatially correlated multiple health outcomes requires specification of cross-correlations. Statisticians developed several forms of multivariate conditional autoregressive models (MCAR) for joint modeling of multiple diseases. More specifically, this research investigates the generalized multivariate logistic regression with the spatial random effects modeled via MCAR. For the Bayesian inference of the parameters, both variational approximation and MCMC are developed. They are then compared in terms of the parameter point estimation, confidence interval (CI) and deviance information criterion (DIC). The simulation results exhibit the speedup and accuracy of the estimation and inference of the parameters.
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 Aggregation and Estimation for Autoregressive-moving Average Models by : Aldo V. Vecchia
Download or read book Aggregation and Estimation for Autoregressive-moving Average Models written by Aldo V. Vecchia and published by . This book was released on 1983 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Least Absolute Deviation Estimation for General Autoregressive Moving Average Time-Series Models by : Rongning Wu
Download or read book Least Absolute Deviation Estimation for General Autoregressive Moving Average Time-Series Models written by Rongning Wu and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study least absolute deviation (LAD) estimation for general autoregressive moving average time-series models that may be noncausal, noninvertible or both. For ARMA models with Gaussian noise, causality and invertibility are assumed for the parameterization to be identifiable. The assumptions, however, are not required for models with non-Gaussian noise, and hence are removed in our study. We derive a functional limit theorem for random processes based on an LAD objective function, and establish the consistency and asymptotic normality of the LAD estimator. The performance of the estimator is evaluated via simulation and compared with the asymptotic theory. Application to real data is also provided.
