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Informationen zum Autor 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. Klappentext 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. Zusammenfassung Filling the need for a comprehensive guide on the subject, Applied Time Series Analysis for the Social Sciences presents time series analysis in an accessible format designed to appeal to students and professional researchers with little mathematical and statistical background. Inhaltsverzeichnis 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 Fun...
