A Practical Guide to Time Series Analysis by Suzanna Linn, Ma... - 9781108407519
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A Practical Guide to Time Series AnalysisAuthor(s): Suzanna Linn, Matthew J. Lebo, Clayton Webb\nFormat: Paperback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9781108407519, 978-1108407519\nSynopsis\nUnderstanding change over time is a critical component of social science. However, data measured over time time series requires their own set of statistical and inferential tools. Inthis book,Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications usingexamples. Theguideoutlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly explain their statist.
A Practical Guide to Time Series Analysis by Suzanna Linn, Ma... - 9781108407519
A Practical Guide to Time Series AnalysisAuthor(s): Suzanna Linn, Matthew J. Lebo, Clayton Webb\nFormat: Paperback\nPublisher: Cambridge University Press, United Kingdom\nImprint: Cambridge University Press\nISBN-13: 9781108407519, 978-1108407519\nSynopsis\nUnderstanding change over time is a critical component of social science. However, data measured over time time series requires their own set of statistical and inferential tools. Inthis book,Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications usingexamples. Theguideoutlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly explain their statist.
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