Time Series Models for Business and Economic Forecasting

ISBN-10: 0521520916
ISBN-13: 9780521520911
Edition: 2nd 2014 (Revised)
List price: $54.99 Buy it from $35.61
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Description: Time Series Models for Business and Economic Forecasting is the most up-to-date and accessible guide to one of the fastest growing areas in business and economic analysis. The author is regarded as one of the most accomplished econometricians in  More...

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Book details

List price: $54.99
Edition: 2nd
Copyright year: 2014
Publisher: Cambridge University Press
Publication date: 4/24/2014
Binding: Paperback
Pages: 311
Size: 7.00" wide x 11.00" long x 0.75" tall
Weight: 1.496
Language: English

Time Series Models for Business and Economic Forecasting is the most up-to-date and accessible guide to one of the fastest growing areas in business and economic analysis. The author is regarded as one of the most accomplished econometricians in Europe and this book is based on his highly successful lecture program for multidisciplinary, graduate and upper level undergraduate students. Early chapters of the book focus on the typical features of time series data in business and economics. Later chapters are concerned with the discussion of some important concepts in time series analysis, the techniques that can be readily applied in practice, different modeling methods and model structures, multivariate time, and the common aspects across time series.

Philip Hans Franses is Professor of Applied Econometrics and Professor of Marketing Research at the Erasmus School of Economics.

Dick van Dijk is Professor of Financial Econometrics at the Erasmus School of Economics.

Anne Opschoor has recently completed a PhD at the Erasmus School of Economics and is an Assistant Professor at the Free University.

Introduction
Key Features of Economic Time Series
Trends
Seasonality
Aberrant observations
Conditional heteroskedasticity
Nonlinearity
Common features
Useful Concepts in Univariate Time Series Analysis
Autoregressive moving average models
Autocorrelation and identification
Estimation and diagnostic measures
Model selection
Forecasting
Trends
Modeling trends
Testing for unit roots
Testing for stationarity
Forecasting
Seasonality
Typical features of seasonal time series
Seasonal unit roots
Periodic models
Miscellaneous topics
Aberrant Observations
Modeling aberrant observations
Testing for aberrant observations
Irregular data and unit roots
Conditional Heteroskedasticity
Models for heteroskedasticity
Specification and forecasting
Various extensions
Nonlinearity
Some models and their properties
Empirical specification strategy
Multivariate Time Series
Representations
Empirical model building
Use of VAR models
Common Features
Some preliminaries for a bivariate time series
Common trends and co-integration
Common seasonality and other features
Data appendix

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