Time Series Models for Business and Economic Forecasting

by Philip Hans Franses , Dick van Dijk , Anne Opschoor

• ISBN13: 9780521817707
• ISBN10: 0521817706
• Edition: 2nd
• Format: Hardcover
• Copyright: 2014-04-24
• Publisher: Cambridge University Press

Summary

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.

Table of Contents

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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