Open AccessTime-series analysis in political sciences: opportunities and limitations
Author(s) -
Андрей Семенов Andrey Semenov
Publication year - 2021
Publication title -
političeskaâ nauka
Language(s) - English
Resource type - Journals
ISSN - 1998-1775
DOI - 10.31249/poln/2021.01.03
Subject(s) - autoregressive integrated moving average , autoregressive model , politics , distributed lag , computer science , dynamics (music) , operations research , time series , econometrics , series (stratigraphy) , management science , industrial engineering , economics , political science , sociology , mathematics , engineering , law , machine learning , paleontology , pedagogy , biology
In this paper, I consider opportunities and limitations of modelling the political dynamics with the time-series instruments. Using the examples of the president Putin’s approval rating and readiness to join the collective actions with economic demands I demonstrate the analytical potential of autoregressive integrated moving average model (ARIMA), autoregressive distributed lag model (ADL), and error correction model (ECM). Modelling the political dynamics faces a string of analytical dilemmas. This paper aims at identifying the basic choices in application of the statistical instruments to dynamic processes and helping the other researchers to navigate through them. While it is hard to account in a single paper for all the developments in the discipline, which has been substantially advanced substantially and technically i the last three decades, this text also aims at stimulating the discussion on the opportunities and limitations when applied to Russian politics.