Applied Time Series Analysis for the Social Sciences

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 Inferencedeliversan accessible guide to time series analysis that includes both theory and practice. ThecoveragespansdevelopmentsfromARIMA intervention models and generalized least squaresto the London School of Economics(LSE)approach andvectorautoregression. Designed to break difficult concepts into manageable pieces while offering plenty ofexamplesandexercises, the author demonstrates the use oflag operator algebra throughout to provide a better understanding ofdynamic specification andthe connections betweenmodelspecificationsthat appear to be more different than they are. The book is ideal for those with minimal mathematicalexperience, intended to follow a course in multiple regression, and includes exercises designed to build general skills such asmathematical expectation calculations to derive means and variances.Readers will also benefit from the inclusion of: A focus on socialscience applications and a mix of theory and detailedexamplesprovidedthroughout An accompanying website with data sets and examples in Stata, SASand R A simplified unit root testing strategy based on recentdevelopments An examination of various uses and interpretations of lagged dependent variables and the common pitfalls students and researchers face in thisarea Anintroduction to LSE methodology such as the COMFAC critique, general-to-specific modeling, and the use of forecasting to evaluate and testmodels 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 Inferencewill also earn a place in the libraries of post graduate students and researchers in public health, public administration and policy, and education.