State-Space Methods for Time Series Analysis, Casals Jose Manuel Carro
Автор: Durbin, James; Koopman, Siem Jan Название: Time Series Analysis by State Space Methods ISBN: 019964117X ISBN-13(EAN): 9780199641178 Издательство: Oxford Academ Рейтинг: Цена: 121440.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
Автор: Hamilton, James Название: Time Series Analysis ISBN: 0691042896 ISBN-13(EAN): 9780691042893 Издательство: Wiley Рейтинг: Цена: 73920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.
Автор: Becketti Название: Introduction to Time Series Using Stata ISBN: 1597181323 ISBN-13(EAN): 9781597181327 Издательство: Taylor&Francis Рейтинг: Цена: 75530.00 T Наличие на складе: Невозможна поставка. Описание: Recent decades have witnessed explosive growth in new and powerful tools for timeseries analysis. These innovations have overturned older approaches to forecasting, macroeconomic policy analysis, the study of productivity and long-run economic growth, and the trading of financial assets. Familiarity with these new tools on time series is an essential skill for statisticians, econometricians, and applied researchers. Introduction to Time Series Using Stata provides a step-by-step guide to essential timeseries techniques—from the incredibly simple to the quite complex—and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. Over the last two decades, Becketti has led proprietary research teams at several leading financial firms, responsible for the models underlying the valuation, hedging, and relative value analysis of some of the largest fixed-income portfolios in the world.
Автор: Arnold Zellner (Editor) Название: The Structural Econometric Time Series Analysis Approach ISBN: 0521187435 ISBN-13(EAN): 9780521187435 Издательство: Cambridge Academ Рейтинг: Цена: 49620.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book assembles previously published texts in the theory and application of the Structural Econometric Time Series Analysis (SEMTSA) approach. It provides a discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making.
Автор: Kohler Название: Data Analysis Using Stata, Third Edition ISBN: 1597181102 ISBN-13(EAN): 9781597181105 Издательство: Taylor&Francis Рейтинг: Цена: 74510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.
The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.
Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.
Автор: Boris Ryabko; Jaakko Astola; Mikhail Malyutov Название: Compression-Based Methods of Statistical Analysis and Prediction of Time Series ISBN: 3319322516 ISBN-13(EAN): 9783319322513 Издательство: Springer Рейтинг: Цена: 71730.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area.The first chapter introduces and describes the application of universal codes to prediction and the statistical analysis of time series;
Автор: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kul Название: Introduction to Time Series Analysis and Forecasting ISBN: 1118745116 ISBN-13(EAN): 9781118745113 Издательство: Wiley Рейтинг: Цена: 121390.00 T Наличие на складе: Поставка под заказ. Описание: Praise for the First Edition " [t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics.
Автор: Commandeur, Jacques J.F.; Koopman, Siem Jan Название: An Introduction to State Space Time Series Analysis ISBN: 0199228876 ISBN-13(EAN): 9780199228874 Издательство: Oxford Academ Рейтинг: Цена: 51210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics.
Автор: Box-Steffensmeier Название: Time Series Analysis for the Social Sciences ISBN: 0521691559 ISBN-13(EAN): 9780521691550 Издательство: Cambridge Academ Рейтинг: Цена: 30610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.
Автор: V?ctor G?mez Название: Multivariate Time Series With Linear State Space Structure ISBN: 331928598X ISBN-13(EAN): 9783319285986 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents a comprehensive study of multivariate time serieswith linear state space structure. The strength of the book also lies in the numerous algorithms includedfor state space models that take advantage of the recursive nature of themodels.
Автор: Masanao Aoki Название: State Space Modeling of Time Series ISBN: 3540528709 ISBN-13(EAN): 9783540528708 Издательство: Springer Рейтинг: Цена: 74490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series.
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