Introduction to Time Series Analysis (Quantitative Applications in the Social Sciences, 174, Band 174) - Softcover

Buch 145 von 194: Quantitative Applications in the Social Sciences

Pickup, Mark

 
9781452282015: Introduction to Time Series Analysis (Quantitative Applications in the Social Sciences, 174, Band 174)

Inhaltsangabe

Introducing time series methods and their application in social science research, this practical guide to time series models is the first in the field written for a non-econometrics audience. Giving readers the tools they need to apply models to their own research, Introduction to Time Series Analysis, by Mark Pickup, demonstrates the use of—and the assumptions underlying—common models of time series data including finite distributed lag; autoregressive distributed lag; moving average; differenced data; and GARCH, ARMA, ARIMA, and error correction models.

 

This volume does an excellent job of introducing modern time series analysis to social scientists who are already familiar with basic statistics and the general linear model.”  —William G. Jacoby, Michigan State University

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Über die Autorin bzw. den Autor

Mark Pickup is an associate professor in the Department of Political Science at Simon Fraser University. Mark is a specialist in political behavior and political methodology. Substantively, his research primarily falls into four areas: political identities and vote choice; the economy and democratic accountability; conditions of democratic responsiveness; and polls and electoral outcomes. His research focuses on political information, public opinion, political identities, and election campaigns within North American and European countries. His methodological interests concern the analysis of longitudinal data (time series, panel, network, etc.), with secondary interests in Bayesian analysis and survey/lab experiment design.

Von der hinteren Coverseite

Approaching the topic from the perspective of the social scientist interested in hypothesis-testing, this volume is an introduction to time-series methods and their application in social science research.

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