Alan Pankratz – författare
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4 produkter
4 produkter
E-bok
PDF, Engelska, 20093 908 kr
Läs direkt efter köp
Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another.
Del 36 - Wiley Series in Probability and Statistics
Forecasting with Univariate Box - Jenkins Models
Concepts and Cases
Inbunden, Engelska, 1983
3 434 kr
Skickas inom 5-8 vardagar
Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another.
Del 236 - Wiley Series in Probability and Statistics
Forecasting with Dynamic Regression Models
Inbunden, Engelska, 1991
3 126 kr
Skickas inom 5-8 vardagar
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
E-bok
PDF, Engelska, 20123 526 kr
Läs direkt efter köp
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author''s earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.