Forecasting Government Budgets

Aman Khan; Kenneth A. Kriz

doi:10.5771/9781793613110

Summary

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.

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Bibliographic data

Copyright year: 2022
ISBN-Print: 978-1-7936-1310-3
ISBN-Online: 978-1-7936-1311-0
Publisher: Lexington, Lanham
Language: English
Pages: 254
Product type: Book Titles

1. Dedication No access
2. Contents No access
3. Preface No access
4. Acknowledgments No access
1. 1. Contrast with Projections No access
2. The Role of Forecasting in Public Budgeting No access
3. Forecasting Process No access
4. 1. 1. Forecast Accuracy and Bias No access
5. An Overview of Methods Used No access
6. Summary No access
7. Notes No access
1. 1. Naïve Model No access
  2. Percentage Average Method No access
  3. Simple Moving Average No access
  4. Double Moving Average No access
  5. Weighted Moving Average No access
  6. Exponential Moving Average No access
  7. 1. Basic Structure No access
    2. The Model Parameters No access
    3. Tests of Significance of the Estimated Parameters and the Model No access
    4. Model Application No access
    5. 1. Nonparametric Tests No access
      2. Parametric Tests No access
    6. Forecasts No access
    7. Confidence Intervals No access
    8. Conditions Affecting Trend No access
  8. Decomposing a Time Series No access
  9. 1. Mean-Squared Error No access
    2. Mean Absolute Deviation No access
    3. Mean Percentage Error No access
    4. Mean Absolute Percentage Error No access
    5. Other Measures No access
  10. Summary No access
  11. Notes No access
2. 1. Brown’s Double Exponential Smoothing No access
  2. Holt’s Two-Parameter Exponential Smoothing No access
  3. Brown’s Triple Exponential Smoothing No access
  4. Winters’ Trend and Seasonal Exponential Smoothing No access
  5. Time Series with Cyclical Variations No access
  6. Forecasting Irregular Time Series No access
  7. Summary No access
  8. Note No access
3. 1. Background Discussion No access
  2. Basic Structure No access
  3. 1. Conditions and Assumptions No access
    2. Assumption SLR/MLR1: Population Model is Linear in Parameters No access
    3. Assumption SLR/MLR2: Random Sampling No access
    4. Assumption SLR/MLR3: Zero Conditional Mean of the Error Term No access
    5. Assumptions SLR4: Sample Variations in the Predictor Variable No access
    6. Assumption MLR5: No Perfect Collinearity No access
    7. Assumptions SLR/MLR 6: Homoscedasticity No access
  4. Regression Forecasts with Single Independent Variable No access
  5. 1. 1. Step 1: Examine Descriptive Statistics No access
      2. Step 2: Initial Regression Analysis No access
      3. Step 3: Examine Error Measures No access
      4. Step 4: Examine Regression Residuals No access
      5. Step 5: Modify and Reestimate the Regression Model No access
      6. Step 6: Forecast the Data Using the Final Regression Model No access
  6. Summary No access
  7. Notes No access
1. 1. 1. Alternative Formulation No access
    2. Stationarity No access
  2. 1. 1. Unit Root Tests No access
    2. Model Estimation No access
    3. Diagnostic Checks No access
    4. Forecasts No access
  3. 1. Model Identification No access
    2. Model Estimation No access
    3. Diagnostic Checks No access
    4. Forecasts No access
  4. Summary No access
  5. Notes No access
2. 1. 1. Basic Structure No access
    2. Seasonal Differencing No access
    3. Seasonal ARIMA Process No access
  2. 1. Model Identification No access
    2. Model Estimation No access
    3. Diagnostic Checks No access
    4. Forecasts No access
  3. 1. Model Identification No access
    2. Model Estimation No access
    3. Diagnostic Checks No access
    4. Forecasts No access
  4. Summary No access
  5. Notes No access
3. 1. 1. Basic Structure No access
    2. Types of VAR No access
    3. Steps in VAR No access
  2. 1. 1. Granger-Causality Test No access
    2. Checking for Stationarity No access
    3. Determining the Order of the Model No access
    4. Model Estimation and Diagnostic Checks No access
    5. Forecasts No access
    6. 1. Cointegration Test No access
    7. A Note on Impulse Response Function No access
  3. 1. Basic Structure No access
    2. Stationarity No access
    3. 1. Johansen Test No access
    4. Lag Order, Model Specification, and Estimation No access
    5. Forecasts No access
  4. Summary No access
  5. Notes No access
1. 1. 1. Model Framework No access
    2. Steps in Creating Ensemble Forecasts No access
    3. Calculating Individual Forecasts No access
    4. Using Bates-Granger and Simple Average Weights to Calculate Forecasts No access
    5. Calculating Granger-Ramanathan Weights Using Regression Analysis No access
    6. Using Granger-Ramanathan Weights to Calculate Forecasts No access
  2. 1. Forms of State-Spaced Models No access
    2. Steps in Dynamic Factor Modeling No access
    3. Testing for Stationarity and Differencing, if Necessary No access
    4. Estimating Unobserved Factors Using DFM-MLE No access
    5. Extract the Latent Factor and Check for Consistency with Prevailing Facts No access
  3. 1. Model Structure No access
    2. Neural Networks and Time Series Data No access
    3. Extended Example No access
  4. Summary No access
  5. Notes No access
2. 1. 1. Diffusion Index No access
    2. Composite Index No access
  2. Challenges to Time Series Forecasting No access
  3. 1. Evaluating Forecast Errors No access
    2. Dealing with Forecasting Bias No access
  4. Forecast Monitoring No access
  5. The End Product: Constructing a Multiyear Forecast No access
  6. Do’s and Don’ts of Forecasting No access
  7. Summary No access
  8. Notes No access
1. Selected Data Tables No access
Bibliography No access Pages 239 - 246
Index No access Pages 247 - 252
About the Authors No access Pages 253 - 254