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Designed for the analysis of linear time series and the practical modelling and prediction of data collected sequentially in time. It provides the reader with a practical understanding of the six programs contained in the ITSM software (PEST, SPEC, SMOOTH, TRANS, ARVEC, and ARAR). This IBM compatible software is included in the back of the book on two 5 1/4'' diskettes and on one 3 1/2 '' diskette. - Easy to use menu system - Accessible to those with little or no previous compu- tational experience - Valuable to students in statistics, mathematics, busi- ness, engineering, and the natural and social sciences. This package is intended as a supplement to the text by the same authors, "Time Series: Theory and Methods." It can also be used in conjunction with most undergraduate and graduate texts on time series analysis.
List of contents
1 Introduction.- 1.1 The Programs.- 1.2 System Requirements.- 1.3 Creating Data Files.- 2 PEST.- 2.1 Getting Started.- 2.2 Preparing Your Data for Modelling.- 2.3 Finding a Model for Your Data.- 2.4 Testing Your Model.- 2.5 Prediction.- 2.6 Model Properties.- 2.7 Nonparametric Spectral Estimation.- 3 SMOOTH.- 3.1 Introduction.- 3.2 Moving Average Smoothing.- 3.3 Exponential Smoothing.- 4 SPEC.- 4.1 Introduction.- 4.2 Bivariate Spectral Analysis.- 5 TRANS.- 5.1 Introduction.- 5.2 Computing Cross Correlations.- 5.3 An Overview of Transfer Function Modelling.- 5.4 Fitting a Preliminary Transfer Function Model.- 5.5 Calculating Residuals from a Transfer Function Model.- 5.6 LS Estimation and Prediction with Transfer Function Models.- 6 ARVEC.- 6.1 Introduction.- 6.2 Model Selection with the AICC Criterion.- 6.3 Forecasting with the Fitted Model.- 7 ARAR.- 7.1 Introduction.- 7.2 Running the Program.- A Word6: A Screen Editor.- A.1 Basic Editing.- A.2 Alternate Keys.- A.3 Printing a File.- A.4 Merging Two or More Files.- A.5 Margins Left and Centre Justification.- A.6 Tab Settings.- A.7 Block Commands.- A.8 Searching.- A.9 Special Characters.- A.10 Function Keys.- A.11 Editing Information.- B Data Sets.
