Time Series Analysis And Adjustment

In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.

In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.

This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies. Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature. Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action. This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.

Analysis of Economic Time Series: A Synthesis integrates several topics in economic time-series analysis, including the formulation and estimation of distributed-lag models of dynamic economic behavior; the application of spectral analysis in the study of the behavior of economic time series; and unobserved-components models for economic time series and the closely related problem of seasonal adjustment. Comprised of 14 chapters, this volume begins with a historical background on the use of unobserved components in the analysis of economic time series, followed by an Introduction to the theory of stationary time series. Subsequent chapters focus on the spectral representation and its estimation; formulation of distributed-lag models; elements of the theory of prediction and extraction; and formulation of unobserved-components models and canonical forms. Seasonal adjustment techniques and multivariate mixed moving-average autoregressive time-series models are also considered. Finally, a time-series model of the U.S. cattle industry is presented. This monograph will be of value to mathematicians, economists, and those interested in economic theory, econometrics, and mathematical economics.

Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time series modeling and seasonal adjustment, as is reflected both in the contents of the chapters and in their authorship, with contributors coming from academia and government statistical agencies. For easier perusal and absorption, the contents have been grouped into seven topical sections: Section I deals with periodic modeling of time series, introducing, applying, and comparing various seasonally periodic models Section II examines the estimation of time series components when models for series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation Section III examines the quantification of error in X-11 seasonal adjustments, with comparisons to error in model-based seasonal adjustments Section IV discusses some practical problems that arise in seasonal adjustment: developing asymmetric trend-cycle filters, dealing with both temporal and contemporaneous benchmark constraints, detecting trading-day effects in monthly and quarterly time series, and using diagnostics in conjunction with model-based seasonal adjustment Section V explores outlier detection and the modeling of time series containing extreme values, developing new procedures and extending previous work Section VI examines some alternative models and inference procedures for analysis of seasonal economic time series Section VII deals with aspects of modeling, estimation, and forecasting for nonseasonal economic time series By presenting new methodological developments as well as pertinent empirical analyses and reviews of established methods, the book provides much that is stimulating and practically useful for the serious researcher and analyst of economic time series.

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.

The application of time series techniques in economics has become increasingly important, both for forecasting purposes and in the empirical analysis of time series in general. In this book, Terence Mills not only brings together recent research at the frontiers of the subject, but also analyses the areas of most importance to applied economics. It is an up-to-date text which extends the basic techniques of analysis to cover the development of methods that can be used to analyse a wide range of economic problems. The book analyses three basic areas of time series analysis: univariate models, multivariate models, and non-linear models. In each case the basic theory is outlined and then extended to cover recent developments. Particular emphasis is placed on applications of the theory to important areas of applied economics and on the computer software and programs needed to implement the techniques. This book clearly distinguishes itself from its competitors by emphasising the techniques of time series modelling rather than technical aspects such as estimation, and by the breadth of the models considered. It features many detailed real-world examples using a wide range of actual time series. It will be useful to econometricians and specialists in forecasting and finance and accessible to most practitioners in economics and the allied professions.