For junior/senior undergraduates in a variety of fields such as economics, business administration, applied mathematics and statistics, and for graduate students in quantitative masters programs such as MBA and MA/MS in economics.
A student-friendly approach to understanding forecasting.
Knowledge of forecasting methods is among the most demanded qualifications for professional economists, and business people working in either the private or public sectors of the economy. The general aim of this textbook is to carefully develop sophisticated professionals, who are able to critically analyze time series data and forecasting reports because they have experienced the merits and shortcomings of forecasting practice.
1. Introduction and Context
2. A Review of Basic Statistics Concepts and the Linear Regression Model
3. Statistics and Time Series
4. Tools of the Forecaster
5. Understanding Linear Dependence: A Link to Economic Models
6. Forecasting with Moving Average (MA) Processes
7. Forecasting with AutoRegressive (AR) Processes
8. Forecasting Practice: Modeling San Diego House Price Index
9. Assessment of Forecasts and Combination of Forecasts
10. Forecasting the Long Run: Deterministic and Stochastic Trends
11. Forecasting with a System of Equations: Vector AutoRegression
12. Forecasting the Long Run and the Short Run Jointly: Cointegration and Error Correction Models
13. Forecasting Volatility I
14. Forecasting Volatility II
15. Financial Applications of Time-varying Volatility
16. Forecasting with Nonlinear Models