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Прогнозирование инфляции: практика использования синтетических процедур // Inflation Forecasting: The Practice of Using Synthetic Procedures

Author

Listed:
  • E. Balatskiy V.

    (Financial University)

  • M. Yurevich A.

    (Financial University)

  • Е. Балацкий В.

    (Финансовый университет)

  • М. Юревич А.

    (Финансовый университет)

Abstract

The article contains a review of inflation forecasting models, including the most popular class of models as one-factor models: random walk, direct autoregression, recursive autoregression, stochastic volatility with an unobserved component and of the integrated model of autoregression with moving average. Also, we discussed the possibilities of various modifications of models based on the Phillips curve (including the “triangle model”), vector autoregressive models (including the factor-extended model of B. Bernanke’s vector autoregression), dynamic general equilibrium models and neural networks. Further, we considered the comparative advantages of these classes of models. In particular, we revealed a new trend in inflation forecasting, which consists of the introduction of synthetic procedures for private forecasts accounting obtained by different models. An important conclusion of the study is the superiority of expert assessments in comparison with all available models. We have shown that in the conditions of a large number of alternative methods of inflation modelling, the choice of the adequate approach in specific conditions (for example, for the Russian economy of the current period) is a non-trivial procedure. Based on this conclusion, the authors substantiate the thesis that large prognostic possibilities are inherent in the mixed strategies of using different methodological approaches, when implementing different modelling tools at different stages of modelling, in particular, the multifactorial econometric model and the artificial neural network. В статье представлена общая типология моделей прогнозирования инфляции. Подробно рассмотрены однофакторные модели, включая модели случайного блуждания, прямой авторегрессии, рекурсивной авторегрессии, стохастической волатильности с ненаблюдаемой составляющей и интегрированные модели авторегрессии со скользящей средней. Помимо этого, обсуждаются возможности различных модификаций моделей на основе кривой Филлипса (включая «треугольную модель»), векторных авторегрессионных моделей (включая факторно-расширенную модель векторной авторегрессии Б. Бернанке), динамических моделей общего равновесия и нейронных сетей. Рассмотрены сравнительные преимущества указанных классов моделей, выявлен новый тренд в прогнозировании инфляции, состоящий во внедрении синтетических процедур учета частных прогнозов, полученных на основе разных типов моделей. Сделан важный вывод о превосходстве экспертных оценок по сравнению со всеми имеющимися моделями. Важным аспектом сравнения разных классов моделей является зависимость успешности их применения от таких факторов, как величина лагов для объясняющих регрессоров, величина горизонта планирования, тип экономики моделируемой страны и т. д. Авторами показано, что в условиях большого числа альтернативных способов моделирования инфляции выбор наиболее адекватного подхода в конкретных условиях (например, для российской экономики нынешнего периода времени) представляет собой нетривиальную процедуру. Опираясь на данный вывод, авторы обосновывают тезис, согласно которому большие прогностические возможности заложены в смешанных стратегиях использования разных методических подходов, когда на разных стадиях моделирования применяется разный модельный инструментарий, в частности многофакторная эконометрическая модель и искусственная нейронная сеть.

Suggested Citation

  • E. Balatskiy V. & M. Yurevich A. & Е. Балацкий В. & М. Юревич А., 2018. "Прогнозирование инфляции: практика использования синтетических процедур // Inflation Forecasting: The Practice of Using Synthetic Procedures," Мир новой экономики // The world of new economy, Финансовый университет при Правительстве Российской Федерации // Financial University under The Governtment оf The Russian Federation, vol. 12(4), pages 20-31.
  • Handle: RePEc:scn:wnewec:y:2018:i:4:p:20-31
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    References listed on IDEAS

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