Applied Time Series Analysis for the Social Sciences

Chapter 1: Introduction

• • 1.1 Why Time Series and Why This Book?
• • 1.2 Time Series: Preliminaries
• • 1.3 Time Series Approaches: Some History, and Outline of the Book
• • 1.4 Summary

Chapter 2: Foundations

• • 2.1 Multiple Interpretations: Some Intuition
• • 2.2 The Lag Operator, the Difference Operator, and Lag Operator Algebra
• • 2.3 Lag Operator Division and Infinite Series
• • 2.4 Lag Operator Algebra: An Example
• • 2.5 An Aside on Linear Difference Equations

Chapter 3: Properties of Time Series: Mean and Variance Stationarity

• • 3.1 Stationarity: Formal Definitions
• • 3.2 Mean Non-stationarity: Stochastic Trend Versus Deterministic Trend
• • 3.3 Dickey–Fuller (D–F) Tests
• • 3.4 Unit Root Testing Strategies: Elder–Kennedy’s Simplified Approach
• • 3.5 Transforming Unit Root Series to Achieve Stationarity: Differencing
• • 3.6 Extensions of the Dickey–Fuller Test
• • 3.7 Seasonal Non-stationarity
• • 3.8 Variance Non-stationarity
• • 3.9 Summary

Chapter 4: Properties of Time Series: Autocorrelation

• • 4.1 Rethinking Autocorrelation
• • 4.2 Modeling Autocorrelation: Wold’s Theorem
• • 4.3 Moving Average Processes
• • 4.4 The Autocorrelation Function (ACF) and Sample Autocorrelation Function (SACF)
• • 4.5 ACFs for MA Processes
• • 4.6 Autoregressive Processes
• • 4.7 The Partial Autocorrelation Function
• • 4.8 Seasonality
• • 4.9 ARMA (mixed) Processes
• • 4.10 Summary

Chapter 5: Autocorrelation: Univariate ARIMA Estimation and Forecasting

• • 5.1 ARIMA (p,d,q)(P,D,Q) s Notation
• • 5.2 The ARIMA Model-Building Process
• • 5.3 Example: Directory Assistance (411)
• • 5.4 Forecasting
• • 5.5 Summary
• • 5.6 Appendix to Chapter 5: Non-linear Models and Numerical Estimation Methods

Chapter 6: ARIMA Intervention Models

• • 6.1 Transfer Functions: General Form
• • 6.2 Simplest Form: Zero-Order Transfer Functions
• • 6.3 Gradual Changes: First-Order Transfer Functions
• • 6.4 Example: The Directory Assistance (411) Series

Chapter 7: ARIMA with Continuous Explanatory Variables

• • 7.1 The Cross-Correlation Function (CCF)
• • 7.2 Prewhitening
• • 7.3 Example: Lydia Pinkham Advertising
• • 7.4 Conclusion

Chapter 8: OLS and the Gauss–Markov Assumptions

• • 8.1 Autocorrelation and its Consequences: A Review
• • 8.2 Detecting Autocorrelation
• • 8.3 Generalized Least Squares
• • 8.4 Limitations of GLS Approaches
• • 8.5 An Aside: Newey–West Standard Errors
• • 8.6 Summary

Chapter 9: Dynamic Specification: Distributed Lag Models

• • 9.1 Distributed Lag and Autoregressive Distributed Lag Models
• • 9.2 The Koyck Model and Dynamic Specification
• • 9.3 General-to-Specific Modeling and the ADL(1,1) Model
• • 9.4 Estimating Models with Lagged Dependent Variables
• • 9.5 Testing Constraints

Chapter 10: Regression with Non-stationary Series: Cointegration and Error Correction Models

• • 10.1 Non-stationary Series and Spurious Regression
• • 10.2 Cointegration and Cointegration Tests
• • 10.3 Testing for Cointegration: Two Examples
• • 10.4 Error Correction Models for Non-stationary Series
• • 10.5 Summary

Chapter 11: The LSE Approach: Encompassing, General-to-Specific Modeling, and Forecasting Success

• • 11.1 Competing Models of the Consumption Function
• • 11.2 Additional Criteria: Forecast Success and Parameter Constancy
• • 11.3 A Sketch of the DHSY Process
• • 11.4 Summary

Chapter 12: A Brief Introduction to Vector Autoregression

• • 12.1 VAR: Logic and Motivation
• • 12.2 Estimating VAR Models
• • 12.3 Summary