Applied Time Series Analysis for the Social Sciences - Baker, Regina M.

. .

Résumé :
EXPLORE THIS INDISPENSABLE AND COMPREHENSIVE GUIDE TO TIME SERIES ANALYSIS FOR STUDENTS AND PRACTITIONERS IN A WIDE VARIETY OF DISCIPLINES

Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference delivers an accessible guide to time series analysis that includes both theory and practice. The coverage spans developments from ARIMA intervention models and generalized least squares to the London School of Economics (LSE) approach and vector autoregression. Designed to break difficult concepts into manageable pieces while offering plenty of examples and exercises, the author demonstrates the use of lag operator algebra throughout to provide a better understanding of dynamic specification and the connections between model specifications that appear to be more different than they are.

The book is ideal for those with minimal mathematical experience, intended to follow a course in multiple regression, and includes exercises designed to build general skills such as mathematical expectation calculations to derive means and variances. Readers will also benefit from the inclusion of:

• A focus on social science applications and a mix of theory and detailed examples provided throughout
• An accompanying website with data sets and examples in Stata, SAS and R
• A simplified unit root testing strategy based on recent developments
• An examination of various uses and interpretations of lagged dependent variables and the common pitfalls students and researchers face in this area
• An introduction to LSE methodology such as the COMFAC critique, general-to-specific modeling, and the use of forecasting to evaluate and test models

Perfect for students and professional researchers in the political sciences, public policy, sociology, and economics, Applied Time Series Analysis for the Social Sciences: Specification, Estimation, and Inference will also earn a place in the libraries of post graduate students and researchers in public health, public administration and policy, and education....

Biographie:
REGINA M. BAKER has extensive experience teaching a time series course for the Inter-University Consortium for Political and Social Research summer program, the University of Oregon and the University of Notre Dame....

Sommaire:

Acknowledgments xi
About the Companion Website xiii

1 Introduction 1
1.1 Why Time Series and Why This Book? 1
1.2 Time Series: Preliminaries 4
1.3 Time Series Approaches: Some History, and Outline of the Book 10
1.4 Summary 16

2 Foundations 19
2.1 Multiple Interpretations: Some Intuition 19
2.2 The Lag Operator, the Difference Operator, and Lag Operator Algebra 22
2.3 Lag Operator Division and Infinite Series 24
2.4 Lag Operator Algebra: An Example 26
2.5 An Aside on Linear Difference Equations 27

3 Properties of Time Series: Mean and Variance Stationarity 29
3.1 Stationarity: Formal Definitions 32
3.2 Mean Non- stationarity: Stochastic Trend Versus Deterministic Trend 33
3.3 Dickey-Fuller (D-F) Tests 42
3.4 Unit Root Testing Strategies: Elder-Kennedy's Simplified Approach 47
3.5 Transforming Unit Root Series to Achieve Stationarity: Differencing 56
3.6 Extensions of the Dickey-Fuller Test 58
3.7 Seasonal Non- stationarity 61
3.8 Variance Non- stationarity 62
3.9 Summary 64

4 Properties of Time Series: Autocorrelation 67
4.1 Rethinking Autocorrelation 67
4.2 Modeling Autocorrelation: Wold's Theorem 69
4.3 Moving Average Processes 70
4.4 The Autocorrelation Function (ACF) and Sample Autocorrelation Function (SACF) 72
4.5 ACFs for MA Processes 76
4.6 Autoregressive Processes 80
4.7 The Partial Autocorrelation Function 88
4.8 Seasonality 90
4.9 ARMA (mixed) Processes 96
4.10 Summary 96

5 Autocorrelation: Univariate ARIMA Estimation and Forecasting 99
5.1 ARIMA (p,d,q)(P,D,Q) s Notation 100
5.2 The ARIMA Model-Building Process 102
5.3 Example: Directory Assistance (411) 106
5.4 Forecasting 113
5.5 Summary 118
5.6 Appendix to Chapter 5: Non-linear Models and Numerical Estimation Methods 118

6 ARIMA Intervention Models 121
6.1 Transfer Functions: General Form 123
6.2 Simplest Form: Zero-Order Transfer Functions 125
6.3 Gradual Changes: First-Order Transfer Functions 126
6.4 Example: The Directory Assistance (411) Series 130

7 ARIMA with Continuous Explanatory Variables 135
7.1 The Cross-Correlation Function (CCF) 136
7.2 Prewhitening 142
7.3 Example: Lydia Pinkham Advertising 143
7.4 Conclusion 147

8 OLS and the Gauss-Markov Assumptions 149
8.1 Autocorrelation and its Consequences: A Review 150
8.2 Detecting Autocorrelation 152
8.3 Generalized Least Squares 158
8.4 Limitations of GLS Approaches 162
8.5 An Aside: Newey-West Standard Errors 163
8.6 Summary 164

9 Dynamic Specification: Distributed Lag Models 167
9.1 Distributed Lag and Autoregressive Distributed Lag Models 168
9.2 The Koyck Model and Dynamic Specification 171
9.3 General- to- Specific Modeling and the ADL(1,1) Model 175
9.4 Estimating Models with Lagged Dependent Variables 178
9.5 Testing Constraints 180

10 Regression with Non-stationary Series: Cointegration and Error Correction Models 183
10.1 Non-stationary Series and Spurious Regression 185
10.2 Cointegration and Cointegration Tests 187
10.3 Testing for Cointegration: Two Examples 190
10.4 Error Correction Models for Non-stationary Series 193
10.5 Summary 198

11 The LSE Approach: Encompassing, General-to-Specific Modeling, and Forecasting Success 201
11.1 Competing Models of the Consumption Function 202
11.2 Additional Criteria: Forecast Success and Parameter Constancy 204
11.3 A Sketch of the DHSY Process 207
11.4 Summary 210

12 A Brief Introduction to Vector Autoregression 211
12.1 VA...