Course Outline

1. Introduction

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   1. Welcome

      1. Welcome to the course

2. What is Tax Revenue Forecasting?

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   1. Overview of Recent Trends in Kenyan Tax Revenues

      1. Revenue Data
      2. Evaluation

   2. The Institutional Framework

      1. What Are the Key Features of an Effective Institutional Framework for Revenue Forecasting?
      2. Reference Materials on Fiscal Institutions
      3. Evaluation

   3. The Objectives of Tax Revenue Forecasting

      1. Performance and Accuracy
      2. Evaluation
      3. Interpretability
      4. Evaluation
      5. Reporting and Transparency
      6. Evaluation

3. Practical Guide: How to Use the Forecasting Tool

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   1. Getting Ready

      1. Download the Excel Tool
      2. About this Section

   2. Description and Philosophy of the Excel Forecasting Tool

      1. Quick Tour of the Tool: Inputs
      2. Quick Tour of the Tool: Macroeconomic Data
      3. Quick Tour of the Tool: Forecasts
      4. Evaluation
      5. Overview of the Main Forecasting Methods Used in the Tool

   3. Data Requirements to Produce Forecasts

      1. Tutorial: How to Update Macroeconomic Data - Getting the Data
      2. Tutorial: How to Update Macroeconomic Data - Updating the Tool
      3. From Calendar Series to Fiscal Year Series

   4. Tax Specific Tutorials

      1. The PAYE Tax
      2. Excise Taxes
      3. Smaller Taxes (Using AR/MA)

   5. Taking into Account New Policy Measures

      1. Understanding Discretionary Measures (and their Use in Tax Elasticity)
      2. Quantifying Discretionary Policy Measures

4. Reporting and Analysis of the Forecasts

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   1. Producing Quality Graphical Output to Describe the Forecast

      1. Using Excel Graphics to Produce Clear Outputs of the Forecast
      2. How to Compute the Contribution of Each Tax to Total Revenue Growth

   2. Explaining and Checking the Output of the Forecast

      1. Elasticity of Tax Revenue to GDP: a Macro Safeguard to Tax-by-Tax Forecast
      2. Evaluation

5. Best Practices in Tax Revenue Forecasting

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   1. Tax Forecasting as a Data Workflow

      1. Determining the Required Information for the Forecast and Setting the Schedule Based on their Availability

   2. Preserving and Improving Knowledge, Experience and Tools

      1. Preserving Knowledge by Keeping a Clear Documentation and Preparing Adequate Training Tools
      2. Control Estimated Elasticities by Re-Estimating
      3. Evaluation

   3. Setting up a Quality Monitoring Policy for the Forecast

      1. Quality Monitoring
      2. Evaluation

6. Introduction to R for Economic Forecasting

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   1. Installing and Getting Started with R and RStudio

      1. Welcome
      2. Installation Guide
      3. Getting Started

   2. Data Visualization

      1. How to Use R to Visualize Revenue Data
      2. Graphical Tools for Building a Model
      3. Evaluation: Data Visualization

   3. Data Import and Manipulation

      1. Data Import and Manipulation

7. Tax Forecasting Methods

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   1. About this Section

      1. Overview of Forecasting Methods

   2. Time Series Basics

      1. Computing Useful Statistics on Time Series
      2. Stationarity: What Is It and Why Is It Important in Forecasting?
      3. Evaluation: Time Series Basics

   3. Simple (but Useful) Tools

      1. Simple (but Useful) Tools: Averaging, Naive Method, Drift Method, Exponential Smoothing
      2. Evaluation

   4. ARIMA Models

      1. Differencing: What Is It and When Do We Need It?
      2. Autoregressive Models
      3. Moving Average Models
      4. Choosing and Estimating an ARIMA model
      5. Evaluation

   5. Regression Models: Forecasting with Explicit Economic Assumptions Regarding the Tax Base

      1. How to Estimate the Elasticity between a Tax and a Predictor of the Tax Base?
      2. Forecasting with a Regression Model
      3. Evaluation

   6. Expert Judgment in Forecast

      1. How to Incorporate Expert Judgments?

8. Evaluating Forecasts and Selecting a Model

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   1. About this Section

      1. About this Section

   2. The Importance of Generalization

      1. The Importance of Generalization

   3. Cross Validation for Time Series

      1. Cross Validation for Time Series

   4. Wrap-Up and Example

      1. Wrap-Up Example