Introduction to Time Series Analysis

Introduction to Time Series Analysis pdf epub mobi txt 电子书 下载 2025

Mark Pickup is an Assistant Professor in the Department of Political Science at Simon Fraser University. He has taught time series analysis at the Inter-University Consortium for Political and Social Research Summer Training Program since 2010.

Mark is a specialist in Comparative Politics and Political Methodology. Substantively, his research primarily falls into three areas: the economy and democratic accountability; polls and electoral outcomes; and conditions of democratic responsiveness. His research focuses on political information, public opinion, the media, election campaigns and electoral institutions within North American and European countries. His methodological interests concern the analysis of longitudinal data (time series, panel, network, etc.) with a secondary interest in Bayesian analysis. He has published in a variety of leading journals.

Mark holds degrees in Chemical Physics (B.Sc.) and Political Science (B.A., M.A. and Ph.D.). He received his doctoral degree at the University of British Columbia. In addition to his current position at Simon Fraser University, he has been a Lecturer at the University of Nottingham and a Postdoctoral Research Fellow at the University of Oxford.

出版者:SAGE Publications, Inc
作者:Mark Pickup
出品人:
页数:232
译者:
出版时间:
价格:USD 22
装帧:Paperback
isbn号码:9781452282015
丛书系列:Quantitative Applications in the Social Sciences
图书标签:
  • Quantitative 
  • Analysis 
  •  
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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.

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