Download or read book Poisson QMLE of Count Time Series Models written by Ali Ahmad and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularity conditions are given for the consistency of the Poisson quasi-maximum likelihood estimator of the conditional mean parameter of a count time series model. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary. Tests for the significance of the parameters and for constant conditional mean are deduced. Applications to specific integer-valued autoregressive (INAR) and integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models are considered. Numerical illustrations, Monte Carlo simulations and real data series are provided.

Download or read book Regression Analysis of Count Data written by A. Colin Cameron and published by Cambridge University Press. This book was released on 2013-05-27 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

Download or read book Negative Binomial Quasi Likelihood Inference for General Integer Valued Time Series Models written by Abdelhakim Aknouche and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two negative binomial quasi-maximum likelihood estimates (NB-QMLEs) for a general class of count time series models are proposed. The first one is the profile NB-QMLE calculated while arbitrarily fixing the dispersion parameter of the negative binomial likelihood. The second one, termed two-stage NB-QMLE, consists of four stages estimating both conditional mean and dispersion parameters. It is shown that the two estimates are consistent and asymptotically Gaussian under mild conditions. Moreover, the two-stage NB-QMLE enjoys a certain asymptotic efficiency property provided that a negative binomial link function relating the conditional mean and conditional variance is specified. The proposed NB-QMLEs are compared with the Poisson QMLE asymptotically and in finite samples for various well-known particular classes of count time series models such as the Poisson and negative binomial integer-valued GARCH model and the INAR(1) model. Application to a real dataset is given.

Download or read book Count Time Series written by Konstantinos Fokianos and published by CRC Press. This book was released on 2020-06-30 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modelling Time Series Count Data written by Andréas Heinen and published by . This book was released on 2008 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper introduces and evaluates new models for time series count data. The Autoregressive Conditional Poisson model (ACP) makes it possible to deal with issues of discreteness, overdispersion (variance greater than the mean) and serial correlation. A fully parametric approach is taken and a marginal distribution for the counts is specified, where conditional on past observations the mean is autoregressive. This enables to attain improved inference on coeffcients of exogenous regressors relative to static Poisson regression, which is the main concern of the existing literature, while modelling the serial correlation in a flexible way. A variety of models, based on the double Poisson distribution of Efron (1986) is introduced, which in a first step introduce an additional dispersion parameter and in a second step make this dispersion parameter time-varying. All models are estimated using maximum likelihood which makes the usual tests available. In this framework autocorrelation can be tested with a straightforward likelihood ratio test, whose simplicity is in sharp contrast with test procedures in the latent variable time series count model of Zeger (1988). The models are applied to the time series of monthly polio cases in the U.S between 1970 and 1983 as well as to the daily number of price change durations of .75$ on the IBM stock. A .75$ price change duration is defined as the time it takes the stock price to move by at least .75$. The variable of interest is the daily number of such durations, which is a measure of intradaily volatility, since the more volatile the stock price is within a day, the larger the counts will be. The ACP models provide good density forecasts of this measure of volatility.

Download or read book Handbook of Discrete Valued Time Series written by Richard A. Davis and published by CRC Press. This book was released on 2016-01-06 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca

Download or read book Research Papers in Statistical Inference for Time Series and Related Models written by Yan Liu and published by Springer Nature. This book was released on 2023-05-31 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.

Download or read book Statistical Inference for Poisson Time Series Models written by Abdullah Maedh Almarashi and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many nonlinear econometric models which are useful in analysis of financial time series. In this thesis, we consider two kinds of nonlinear autoregressive models for nonnegative integer-valued time series: threshold autoregressive models and Markov switching models, in which the conditional distribution given historical information is the Poisson distribution. The link between the conditional variance (i.e. the conditional mean for the Poisson distribution) and its past values as well as the observed values of the Poisson process may be different according to the threshold variable in threshold autoregressive models, and to an unobservable state variable in Markov switching models in different regimes. We give a condition on parameters under which the Poisson generalized threshold autoregressive heteroscedastic (PTGARCH) process can be approximated by a geometrically ergodic process. Under this condition, we discuss statistical inference (estimation and tests) for PTGARCH models, and give the asymptotic theory on the inference. The complete structure of the threshold autoregressive model is not exactly specific in economic theory for the most financial applications of the model. In particular, the number of regimes, the value of threshold and the delay parameter are often unknown and cannot be assumed known. Therefore, in this research, the performance of various information criteria for choosing the number of regimes, the threshold value and the delay parameters for different sample sizes is investigated. Tests for threshold nonlinearity are applied. The characteristics of Markovian switching Poisson generalized autoregressive hetero-scedastic (MS-PGARCH) models are given, and the maximum likelihood estimation of parameters is discussed. Simulation studies and applications to modelling financial counting time series are presented to support our methodology for both the PTGARCH model and the MS-PGARCH model.

Download or read book An Introduction to Discrete Valued Time Series written by Christian H. Weiss and published by John Wiley & Sons. This book was released on 2018-02-05 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series Time series analysis is an essential tool in a wide array of fields, including business, economics, computer science, epidemiology, finance, manufacturing and meteorology, to name just a few. Despite growing interest in discrete-valued time series—especially those arising from counting specific objects or events at specified times—most books on time series give short shrift to that increasingly important subject area. This book seeks to rectify that state of affairs by providing a much needed introduction to discrete-valued time series, with particular focus on count-data time series. The main focus of this book is on modeling. Throughout numerous examples are provided illustrating models currently used in discrete-valued time series applications. Statistical process control, including various control charts (such as cumulative sum control charts), and performance evaluation are treated at length. Classic approaches like ARMA models and the Box-Jenkins program are also featured with the basics of these approaches summarized in an Appendix. In addition, data examples, with all relevant R code, are available on a companion website. Provides a balanced presentation of theory and practice, exploring both categorical and integer-valued series Covers common models for time series of counts as well as for categorical time series, and works out their most important stochastic properties Addresses statistical approaches for analyzing discrete-valued time series and illustrates their implementation with numerous data examples Covers classical approaches such as ARMA models, Box-Jenkins program and how to generate functions Includes dataset examples with all necessary R code provided on a companion website An Introduction to Discrete-Valued Time Series is a valuable working resource for researchers and practitioners in a broad range of fields, including statistics, data science, machine learning, and engineering. It will also be of interest to postgraduate students in statistics, mathematics and economics.

Download or read book Regression Analysis of Count Data written by Adrian Colin Cameron and published by Cambridge University Press. This book was released on 2013-05-27 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.

Download or read book Modelling Time Series Counts Data in Financial Microstructure written by Andreas Heinen and published by . This book was released on 2004 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Linear and Log linear Models for Count Time Series Analysis written by Nicholas Michael Bosowski and published by . This book was released on 2016 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling count data is a topic of interest in many applications. Traditional time series assume continuous data with a normal distribution, which is not appropriate for count data. In this thesis we focus on linear and log-linear count models with Poisson and NB2 distributions with or without zero-inflation. These models provide a parsimonious manner to account for serial correlation in count data through the conditional mean and distribution. Current research on these models provides theoretical results for model analysis, estimation, and use. This thesis provides a unified framework of these models based on current literature . We also provide several new results. First, we develop a simple heuristic evaluation of the Poisson model. This approximate marginal distribution helps visualize the range of values the Poisson model achieves. It can also be used as a horizon forecast when the present has little influence on the forecast. We exploit similarities between these and ARMA models to find bounds on stationarity of the NB2 linear model, ensuring that estimation techniques are bounded. We also extend estimation methods for these models via conditional maximum likelihood estimation. This estimation method has been studied for the Poisson models by [1, 2]. We use this technique to develop estimators of the NB2 models as well as zero-inflated Poisson and NB2 models. We evaluate the estimators for consistency and asymptotic performance and find they perform well. We compare the estimator for the NB2 model to the technique of quasi maximum likelihood estimation [3] and find they perform comparably. In addition, we develop approximations for the limiting information matrix for two cases of the Poisson linear model. We evaluate performance of these approximations and use them to develop a better understanding of how true parameter values affect estimation. Finally, we study the use of linear and log-linear models for forecasting. We focus predominantly on probabilistic forecasts discussing theoretical framework as well as practical use. We then apply these methods to a real world data set to demonstrate how the models handle the real world data.

Download or read book Stochastic Models Statistics and Their Applications written by Ansgar Steland and published by Springer Nature. This book was released on 2019-10-15 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.

Download or read book Multivariate Modelling of Time Series Count Data written by Andréas Heinen and published by . This book was released on 2003 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Science in Applications written by Gintautas Dzemyda and published by Springer Nature. This book was released on 2023-03-09 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of a wide range of relevant applications and reveals how to solve them. Many of the latest applications in finance, technology, education, medicine and other important and relevant fields are data-driven. The volumes of data are enormous. Specific methods need to be developed or adapted to solve a particular problem. It illustrates data science in applications. These applications have in common the discovery of knowledge in data and the use of this knowledge to make real decisions. The set of examples presented serves as a recipe book for their direct application to similar problems or as a guide for the development of new, more sophisticated approaches. The intended readership is data scientists looking for appropriate solutions to their problems. In addition, the examples provided serves as material for lectures at universities.

Download or read book Continuous Parameter Time Series written by Peter J. Brockwell and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-07-22 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained account of continuous-parameter time series, starting with second-order models. Integration with respect to orthogonal increment processes, spectral theory and linear prediction are treated in detail. Lévy-driven models are incorporated, extending coverage to allow for infinite variance, a variety of marginal distributions and sample paths having jumps. The necessary theory of Lévy processes and integration of deterministic functions with respect to these processes is developed at length. Special emphasis is given to the analysis of continuous-time ARMA processes.

Download or read book Time Series Analysis Methods and Applications written by and published by Elsevier. This book was released on 2012-05-18 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respective areas