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Equipping analysts, practitioners, and graduate students with a statistical framework to make effective decisions based on the application of simple economic and statistical methods, Economic and Business Forecasting offers a comprehensive and practical approach to quantifying and accurate forecasting of key variables. Using simple econometric techniques, author John E. Silvia focuses on a select set of major economic and financial variables, revealing how to optimally use statistical software as a template to apply to your own variables of interest.
- Presents the economic and financial variables that offer unique insights into economic performance
- Highlights the econometric techniques that can be used to characterize variables
- Explores the application of SAS software, complete with simple explanations of SAS-code and output
- Identifies key econometric issues with practical solutions to those problems
Presenting the "ten commandments" for economic and business forecasting, this book provides you with a practical forecasting framework you can use for important everyday business applications.
Table of Contents
Chapter 1: Creating Harmony Out of Noisy Data
Effective Decision Making: Characterize the Data
Chapter 2: First, Understand the Data
Growth: How is the Economy Doing Overall?
Gross Private Domestic Investment
Net Exports of Goods and Services
Real Final Sales and Gross Domestic Purchases
The Labor Market: Always a Core Issue
Data Revision: A Special Consideration
The Household Survey
Marrying the Labor Market Indicators Together
Consumer Price Index: A Society’s Inflation Benchmark
Producer Price Index
Personal Consumption Expenditure Deflator: The Inflation Benchmark for Monetary Policy
Interest Rates: Price of Credit
The Dollar and Exchange Rates: The United States in a Global Economy
Chapter 3: Financial Ratios
Chapter 4: Characterizing a Time Series
Why Characterize a Time Series?
How to Characterize a Time Series
Application: Judging Economic Volatility
Chapter 5: Characterizing a Relationship between Time Series
Important Test Statistics in Identifying Statistically Significant Relationships
Simple Econometric Techniques to Determine a Statistical Relationship
Advanced Econometric Techniques to Determine a Statistical Relationship
Chapter 6: Characterizing a Time Series Using SAS Software
Tips for SAS Users
The DATA Step
The PROC Step
Chapter 7: Testing for a Unit Root and Structural Break Using SAS Software
Testing a Unit Root in a Time Series: A Case Study of the U.S. CPI
Identifying a Structural Change in a Time Series
The Application of the H-P Filter
Application: Benchmarking the Housing Bust, Bear Stearns and Lehman Brothers
Appendix 7A: The State-Space Approach to Testing for a Structural Break
Chapter 8: Characterizing a Relationship Using SAS
Useful Tips for an Applied Time Series Analysis
Converting a Dataset from One Frequency to Another
Application: Did the Great Recession Alter Credit Benchmarks?
Chapter 9: The Ten Commandments of Applied Time Series Forecasting for Business and Economics
Objective of the Forecast: What Are You Forecasting?
What is the Purpose of Forecasting?
Cost of Forecast Error: The Loss Function
Forecast Horizon: How Far Out to Forecast
The Choice of Variables: Quality vs. Quantity
Forecasting Modeling: How Do You Choose the Forecast Methods?
How Do You Present the Results?
Evaluating the Forecast Results
Recursive Methods: The Controlled-Forecasting Experiment
There is No-Silver Bullet: Forecasting Models Evolve Over Time
Chapter 10: A Single-Equation Approach to Model-Based Forecasting
The Unconditional (A-Theoretical) Approach
The Conditional (Theoretical) Approach
Recession Forecast Using a Probit Model
Chapter 11: A Multiple-Equations Approach to Model-Based Forecasting
The Importance of the Real-Time Short-term Forecasting
The Individual Forecast vs. Consensus Forecast: Is There an Advantage?
The Econometrics of Real-Time Short-term Forecasting: The BVAR Approach
Forecasting in Real-Time: Issues Related to the Data and the Model Selection
Case Study: WFC vs. Bloomberg
Appendix 11A: List of Variables
Chapter 12: A Multiple-Equations Approach to Long-Term Forecasting
The Unconditional Long-term Forecasting: The BVAR Model
The BVAR Model with Housing Starts
The Model without Oil Price Shock
The Model with Oil Price Shock
Chapter 13: The Risks of Model-Based Forecasting: Modeling, Assessing and Remodeling
Risks to the short-term Forecasting: There is no Magic Bullet
13.2 Risks of Long-term Forecasting: Black Swan vs. a Group of Black Swans
13.3 Model-Based Forecasting and the Great Recession/Financial Crisis: Worst-case Scenario vs. Panic
Chapter 14: Putting the Analysis to Work in the 21st Century Economy
Benchmarking Economic Growth
Industrial Production: Another Case of Stationary Behavior
Employment: Jobs in the 21st Century
Imbalances Between Bond Yields and Equity Earnings
A Note of Caution on Patterns of Interest Rates
Business Credit: Patterns Reminiscent of Cyclical Recovery
Financial Market Volatility: Assessing Risk
Economic Policy—Impact of Fiscal Policy and the Evolution of the U.S. Economy
The Long-Term Deficit Bias and Its Economic Implications
Appendix: Useful References for SAS Users
About the Authors