Cover image for Time series models for business and economic forecasting
Title:
Time series models for business and economic forecasting
Personal Author:
Publication Information:
New York : Cambridge University Press, 1998
Physical Description:
x, 280 p. : ill. ; 24 cm.
ISBN:
9780521586412

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30000010229645 HA30.3 F72 1998 Open Access Book Book
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Summary

Summary

The econometric analysis of economic and business time series is a major field of research and application. The last few decades have witnessed an increasing interest in both theoretical and empirical developments in constructing time series models and in their important application in forecasting. In Time Series Models for Business and Economic Forecasting, Philip Franses examines recent developments in time series analysis. The early parts of the book focus on the typical features of time series data in business and economics. Part III is concerned with the discussion of some important concepts in time series analysis, the discussion focuses on the techniques which can be readily applied in practice. Parts IV-VIII suggest different modeling methods and model structures. Part IX extends the concepts in chapter three to multivariate time series. Part X examines common aspects across time series.


Table of Contents

Part I Introduction
Part II Key Features of Economic Time Series
1 Trends
2 Seasonality
3 Aberrant observations
4 Conditional heteroskedasticity
5 Nonlinearity
6 Common features
Part III Useful Concepts in Univariate Time Series Analysis
7 Autoregressive moving average models
8 Autocorrelation and identification
9 Estimation and diagnostic measures
10 Model selection
11 Forecasting
Part IV Trends
12 Modeling trends
13 Testing for unit roots
14 Testing for stationarity
15 Forecasting
Part V Seasonality
16 Typical features of seasonal time series
17 Seasonal unit roots
18 Periodic models
19 Miscellaneous topics
Part VI Aberrant Observations
20 Modeling aberrant observations
21 Testing for aberrant observations
22 Irregular data and unit roots
Part VII Conditional Heteroskedasticity
23 Models for heteroskedasticity
24 Specification and forecasting
25 Various extensions
Part VIII Nonlinearity
26 Some models and their properties
27 Empirical specification strategy
Part IX Multivariate Time Series
28 Representations
29 Empirical model building
30 Use of VAR models
Part X Common Features
31 Some preliminaries for a bivariate time series
32 Common trends and co-integration
33 Common seasonality and other features
Data appendix