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Author: Terence C. Mills Publisher: Springer Nature ISBN: 3030763595 Category : Business & Economics Languages : en Pages : 219
Book Description
Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In this second edition, Terence Mills expands on the research in the area of trends and cycles over the last (almost) two decades, to highlight to students and researchers the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.
Author: Terence C. Mills Publisher: Springer Nature ISBN: 3030763595 Category : Business & Economics Languages : en Pages : 219
Book Description
Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In this second edition, Terence Mills expands on the research in the area of trends and cycles over the last (almost) two decades, to highlight to students and researchers the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.
Author: T. Mills Publisher: Springer ISBN: 0230595529 Category : Business & Economics Languages : en Pages : 184
Book Description
Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. Terence Mills introduces these various approaches to allow students and researchers to appreciate the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.
Author: Philip Hans Franses Publisher: Oxford University Press, USA ISBN: Category : Business & Economics Languages : en Pages : 256
Book Description
This book provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. Two key concepts are periodic integration and periodic cointegration. Periodic integration implies that a seasonally varying differencing filter is required to remove a stochastic trend. Periodic cointegration amounts to allowing cointegration paort-term adjustment parameters to vary with the season. The emphasis is on useful econrameters and shometric models that explicitly describe seasonal variation and can reasonably be interpreted in terms of economic behaviour. The analysis considers econometric theory, Monte Carlo simulation, and forecasting, and it is illustrated with numerous empirical time series. A key feature of the proposed models is that changing seasonal fluctuations depend on the trend and business cycle fluctuations. In the case of such dependence, it is shown that seasonal adjustment leads to inappropriate results.
Author: William R. Bell Publisher: CRC Press ISBN: 1439846588 Category : Mathematics Languages : en Pages : 544
Book Description
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 s
Author: Haim Y. Bleikh Publisher: CRC Press ISBN: 1317010183 Category : Business & Economics Languages : en Pages : 149
Book Description
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.
Author: Terence C. Mills Publisher: Edward Elgar Publishing ISBN: 9781840647860 Category : Business cycles Languages : en Pages : 0
Book Description
This two-volume reference for students, researchers, and lecturers in economics presents a selection of the most important articles in the field published between 1923 and 1999. The 31 articles in Volume I cover topics related to business cycles including first attempts at measuring, traditional theories, sceptic views and early empirical methods, measurement without theory debate, interactions between trends and cycles, modern theories, and nonlinear and duration models. The 23 contributions in Volume II discuss dating business cycle turning points, modelling trends and cycles in economic time series, detrending economic time series, and historical examinations of trends and cycles. There is no subject index. Annotation copyrighted by Book News, Inc., Portland, OR
Author: Regina Kaiser Publisher: Springer Science & Business Media ISBN: 1461301297 Category : Business & Economics Languages : en Pages : 198
Book Description
This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components. The main innovation of the alternative strategy involves augmenting the series forecasts and back-casts obtained from an ARIMA model, and then applying the HP filter to the augmented series. Comparisons presented using artificial and actual data demonstrate the superiority of the alternative strategy.
Author: Philip Hans Franses Publisher: Cambridge University Press ISBN: 1139952129 Category : Business & Economics Languages : en Pages : 421
Book Description
With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.
Author: Gebhard Kirchgässner Publisher: Springer Science & Business Media ISBN: 3642334350 Category : Business & Economics Languages : en Pages : 326
Book Description
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.