politscienceПолитическая наукаPolitical science1998-1775ИНИОН РАН10.31249/poln/2021.01.03politscience-834Research ArticleСОСТОЯНИЕ ДИСЦИПЛИНЫАнализ временных рядов в политической науке: возможности и ограниченияTime-series analysis in political sciences: opportunities and limitationsСеменовА. В.SemenovA. V.

Семенов Андрей Владимирович, кандидат политических наук, научный сотрудник

Пермь

Perm

andreysemenov@comparativestudies.ruПермский федеральный исследовательский центр Уральского отделения Российской академии наукРоссияPerm Federal Research Center of the Ural Branch of the Russian Academy of SciencesRussian Federation20211805202301Математические методы в политической науке7697Copyright © Семенов А.В., 20232023Семенов А.В.Semenov A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.politnauka.ru/jour/article/view/834

В данной статье рассматриваются возможности и ограничения статистического моделирования динамики политических процессов. На примере моделирования рейтинга одобрения президента В. Путина и готовности россиян присоединиться к коллективным действиям с экономическими требованиями демонстрируется аналитический потенциал базовых моделей авторегрессии со скользящим средним и интеграцией (ARIMA), авторегрессии с распределенным лагом (ADL) и модели коррекции регрессионных остатков (ECM). В ходе статистического моделирования временных рядов в политике исследователи сталкиваются с целым рядом аналитических проблем. Данная статья призвана обозначить основные «развилки» в исследовательском процессе и основания для выбора того или иного варианта исследовательского дизайна. Не претендуя на полноту охвата всей дискусии, данная статья призвана стимулировать использование данного метода применительно к российскому материалу.

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 ave- rage 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.

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The authors declare that there are no conflicts of interest present.