Time Series And Dynamic Models
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Time Series And Dynamic Models
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Author : Christian Gourieroux
language : en
Publisher: Cambridge University Press
Release Date : 1997
Time Series And Dynamic Models written by Christian Gourieroux and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Business & Economics categories.
In this book Christian Gourieroux and Alain Monfort provide an up-to-date and comprehensive analysis of modern time series econometrics. They have succeeded in synthesising in an organised and integrated way a broad and diverse literature. While the book does not assume a deep knowledge of economics, one of its most attractive features is the close attention it pays to economic models and phenomena throughout. The coverage represents a major reference tool for graduate students, researchers and applied economists. The book is divided into four sections. Section one gives a detailed treatment of classical seasonal adjustment or smoothing methods. Section two provides a thorough coverage of various mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to time series econometric problems.
Time Series Models
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Author : Manfred Deistler
language : en
Publisher: Springer Nature
Release Date : 2022-10-21
Time Series Models written by Manfred Deistler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-21 with Mathematics categories.
This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.
Social Dynamics Models And Methods
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Author : Nancy Brandon Tuma
language : en
Publisher: Elsevier
Release Date : 1984-08-28
Social Dynamics Models And Methods written by Nancy Brandon Tuma and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984-08-28 with Technology & Engineering categories.
Social Dynamics: Models and Methods focuses on sociological methodology and on the practice of sociological research. This book is organized into three parts encompassing 16 chapters that deal with the basic principles of social dynamics. The first part of this book considers the development of models and methods for causal analysis of the actual time paths of change in attributes of individual and social systems. This part also discusses the applications in which the use of dynamic models and methods seems to have enhanced the capacity to formulate and test sociological arguments. These models and methods are useful for answering questions about the detailed structure of social change processes. The second part explores the formulation of the continuous-time models of change in both quantitative and qualitative outcomes and the development of suitable methods for estimating these models from the kinds of data commonly available to sociologists. The third part describes a stochastic framework for analyzing both qualitative and quantitative outcome of social changes. This part also discusses the sociologists' perspective on the empirical study of social change processes. This text will be of great value to sociologists and sociological researchers.
A Practical Guide To Static And Dynamic Econometric Modelling
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Author : Sarit Maitra
language : en
Publisher: Springer Nature
Release Date : 2025-07-04
A Practical Guide To Static And Dynamic Econometric Modelling written by Sarit Maitra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-04 with Business & Economics categories.
This book provides a comprehensive guide to econometric modeling, combining theory with practical implementation using Python. It covers key econometric concepts, from data collection and model specification to estimation, inference, and prediction. Readers will explore linear regression, data transformations, and hypothesis testing, along with advanced topics like the Capital Asset Pricing Model and dynamic modeling techniques. With Python code examples, this book bridges theory and practice, making it an essential resource for students, finance professionals, economists, and data scientists seeking to apply econometrics in real-world scenarios.
State Space Modeling Of Time Series
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Author : Masanao Aoki
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
State Space Modeling Of Time Series written by Masanao Aoki and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Business & Economics categories.
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. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.
Bayesian Forecasting And Dynamic Models
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Author : Mike West
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Bayesian Forecasting And Dynamic Models written by Mike West and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
New Introduction To Multiple Time Series Analysis
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Author : Helmut Lütkepohl
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-26
New Introduction To Multiple Time Series Analysis written by Helmut Lütkepohl and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-26 with Business & Economics categories.
This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.
Introduction To Time Series Analysis
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Author : Mark Pickup
language : en
Publisher: SAGE Publications
Release Date : 2014-10-15
Introduction To Time Series Analysis written by Mark Pickup and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-15 with Social Science categories.
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
Time Series Modeling Of Neuroscience Data
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Author : Tohru Ozaki
language : en
Publisher: CRC Press
Release Date : 2012-01-26
Time Series Modeling Of Neuroscience Data written by Tohru Ozaki and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-26 with Mathematics categories.
Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to
The Sage Handbook Of Quantitative Methods In Psychology
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Author : Roger E Millsap
language : en
Publisher: SAGE
Release Date : 2009-07-23
The Sage Handbook Of Quantitative Methods In Psychology written by Roger E Millsap and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-23 with Psychology categories.
`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology′ - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.