- 269 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
About this book
Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced.
The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting methods, followed by a detailed discussion of the actual methods in Parts I, II, and III. Part I discusses a combination of basic time series models such as percentage average, simple moving average, double moving average, exponential moving average, double as well as triple, simple trend line, time-series with cyclical variation, and time-series regression, with single and multiple independent variables. Part II discusses some of the more advanced, but frequently used time series models, such as ARIMA, regular as well as seasonal, Vector Autoregression (VAR), and Vector Error Correction (VEC). Part III provides an overview of three of the more recent advances in time series models, namely ensemble forecasting, state-space forecasting, and neural network. The book concludes with a brief discussion of some practical issues in budget forecasting.
The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting methods, followed by a detailed discussion of the actual methods in Parts I, II, and III. Part I discusses a combination of basic time series models such as percentage average, simple moving average, double moving average, exponential moving average, double as well as triple, simple trend line, time-series with cyclical variation, and time-series regression, with single and multiple independent variables. Part II discusses some of the more advanced, but frequently used time series models, such as ARIMA, regular as well as seasonal, Vector Autoregression (VAR), and Vector Error Correction (VEC). Part III provides an overview of three of the more recent advances in time series models, namely ensemble forecasting, state-space forecasting, and neural network. The book concludes with a brief discussion of some practical issues in budget forecasting.
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Yes, you can access Forecasting Government Budgets by Aman Khan,Kenneth A. Kriz in PDF and/or ePUB format, as well as other popular books in Economics & Political Economy. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Acknowledgments
- Introduction
- Part I: Basic Time Series Models
- Chapter 1: Basic Time Series Models I
- Chapter 2: Basic Time Series Models II
- Chapter 3: Time Series Regression
- Part II: Advanced Time Series Models
- Chapter 4: Box-Jenkins and ARIMA Models
- Chapter 5: Seasonal ARIMA Models
- Chapter 6: Advanced Time Series Models
- Part III: Recent Advances in Forecasting Models
- Chapter 7: Some Recent Advances in Forecasting Models
- Conclusion
- Appendix
- Bibliography
- Index
- About the Authors