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.
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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.
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6 of 6 people found the following review helpful:
5.0 out of 5 stars
Time Series Analysis for beginner,
By "goi_k" (Stillwater,OK USA) - See all my reviews
This review is from: Forecasting with Dynamic Regression Models (Hardcover)
It is an excellent book for understanding time series analysis, well written and clearly explained. Readers should have some knowledge of univariate ARIMA model but no expertise is required. The book focuses on how, why, and when to combine the effect of other variables and the univariate ARIMA model. The suggested methods are demonstrated by a well-explained example. The author describes each step in the analysis in detail, this greatly helps in understanding the logic. Therefore, I highly recommend this book for students or persons who have a preliminary knowledge of the time series analysis technique, and who want to develop a more in-depth understanding of the subject. In addition, it is a must-have reference book for all time-series analyst.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
tharit,
By
This review is from: Forecasting with Dynamic Regression Models (Hardcover)
I was assigned to develop a stochastic model for air pollution (well, back to 3 weeks before, I had no idea what time series analysis was !!!). After spend some time searching around for an easy-to-understand textbook dealing with time series data, I found this book is the best. It is comprehensive and clearly explain with many good excercises. What you need is some basic knowledge in ARIMA (which could be find from general econometrics textbook or SPSS guideline) and simple linear regression and then you will love this book as I am now.
5.0 out of 5 stars
excellent,
By rita luk (Canada) - See all my reviews
This review is from: Forecasting with Dynamic Regression Models (Hardcover)
Being a noive to Box Jenkin method, the clear, and well organized exposition of this book took me step by step to a firm understanding of the method within a short time span, and keys to practice it. After finishing reading a copy from the library, I find it worths to purchase one for reference.
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Inside This Book
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First Sentence:
Comedian George Carlin has noticed that "If there were no such thing as time, everything would happen all at once." Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
rational form transfer function, government receipts data, sample impulse response function, rational distributed lag models, saving rate data, backshift form, business inventories data, random shock series, inadequate transfer function, leading indicator data, trading day variables, intervention transfer function, pulse intervention, nonseasonal part, disturbance series, ordinary algebraic variable, transfer function part, backshift notation, constant overall mean, perfect colinearity, leading indicator model, distributed lag response, redundant coefficients, infinitely high order, metric forecasts Key Phrases - Capitalized Phrases (CAPs): (learn more)
Data Appendix, Residuals Figure, Lydia Pinkham, Parameter Estimate Standard Error, United States, Correlation Matrix of Parameter Estimates, Commerce Department, Coefficient Correlation Matrix New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Comedian George Carlin has noticed that "If there were no such thing as time, everything would happen all at once." Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
rational form transfer function, government receipts data, sample impulse response function, rational distributed lag models, saving rate data, backshift form, business inventories data, random shock series, inadequate transfer function, leading indicator data, trading day variables, intervention transfer function, pulse intervention, nonseasonal part, disturbance series, ordinary algebraic variable, transfer function part, backshift notation, constant overall mean, perfect colinearity, leading indicator model, distributed lag response, redundant coefficients, infinitely high order, metric forecasts Key Phrases - Capitalized Phrases (CAPs): (learn more)
Data Appendix, Residuals Figure, Lydia Pinkham, Parameter Estimate Standard Error, United States, Correlation Matrix of Parameter Estimates, Commerce Department, Coefficient Correlation Matrix New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
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100
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cite this book:
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- Forecasting in Business and Economics, Second Edition (Economic Theory, Econometrics, and Mathematical Economics) (Economic Theory, Econometrics, and Mathematical Economics) by C. W. J. Granger in Back Matter (1), and Back Matter (2)
- Applied Linear Regression (Wiley Series in Probability and Statistics) by Sanford Weisberg in Back Matter (1), and Back Matter (2)
- Linear Models: Least Squares and Alternatives (Springer Series in Statistics) by C.R. Rao in Front Matter, and Back Matter
- Statistical Methods for Forecasting (Wiley Series in Probability and Statistics) by Bovas Abraham in Front Matter, and Back Matter
- Stochastic Processes by Sheldon M. Ross in Front Matter, and Back Matter
See all 100 books this book cites
- Models for Investors in Real World Markets (Wiley Series in Probability and Statistics) by James R. Thompson in Back Matter
- Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics) by Charles E. McCulloch in Back Matter
- The Statistical Analysis of Failure Time Data (Wiley Series in Probability and Statistics) by John D. Kalbfleisch in Back Matter
- Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley Series in Probability and Statistics) by David W. Scott in Back Matter
- Case Studies in Biometry by Nicholas Lange in Back Matter
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