Abstract
The pricing policy of airlines is developed based on a retrospective analysis of the price dynamics of air transportation and forecasting the market situation of supply and demand. The price dynamics of passenger air transportation has a certain structure and patterns, the identification of which helps to develop a competitive price offer for consumers.The objective of the work is to determine the structure of price dynamics and identify patterns of price fluctuations in passenger air transportation from 2008 to 2022, which is important to consider when developing the pricing policy of airlines and the range of tariffs. Studies of the price dynamics of airline tickets by econometric methods allowed to identify the structure of the time series of prices and develop several models.The study of the price dynamics structure, first, identified and analysed the seasonal component of the dynamics of airline ticket prices. Its calculation was carried out using additive and multiplicative models. The range of seasonal changes was -8,5% to +12,5%. The autocorrelation function of the dynamics of average monthly prices showed that the time series of airline ticket prices contained a trend. In addition to trend and seasonal components, cyclical fluctuations were identified in the price dynamics, the modelling of which was carried out based on regression analysis. Cyclical changes in the dynamics of air ticket prices, identified from 2008 to the present, are not sustainable.Analysed dynamics revealed several medium-term cycles with a duration of 4–6 years. The cyclical dynamics of air transportation prices largely coincides with the general economic medium-term cycles, but there are time lags or lagging growth and decline rates.Thus, the change in prices for civil air transportation has a natural trend-cyclical character shaped under the influence of fundamental macroeconomic factors and new determinants, the effect of which may result in a stronger change but with shorter impact or lag effect. Additive and multiplicative models will help predict average annual prices.
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