Forecasting Government Budgets : Methods and Applications

Kenneth A. Kriz

ISBN 10: 1793613125 / ISBN 13: 9781793613127

Published by Lexington Books, 2025

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Written in a simple and easy to understand manner, Forecasting Government Budgets presents some of the frequently used methods, simple as well as advanced, in budget forecasting. Although written primarily for graduate students in public administration and management, students from other disciplines will also find it useful.

Seller Inventory # 9781793613127

Bibliographic Details

• Title: Forecasting Government Budgets : Methods and Applications
• Publisher: Lexington Books
• Publication Date: 2025
• Language: English
• ISBN 10: 1793613125
• ISBN 13: 9781793613127
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 16D millimeters 229 millimeters width by 152 millimeters height by 16 millimeters depth
• Weight: 443 grams

About this title

Synopsis

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.

About the Authors

Aman Khan is Professor of Political Science and Public Administration at Texas Tech University.

Kenneth A. Kriz is University Distinguished Professor of Public Administration at the University of Illinois at Springfield.