USING ONLINE PRICE DATA FOR CONSTRUCTION LEADING MACROECONOMIC INDICATORS

Today big data and online data provide macroeconomists with the advantage to create unique
data sets that are conducted to specific research needs, and more than that, it enables some specific
capabilities for both measurement and forecasting with the usage of new forms of data. The importance
of this work is based on that the online price data has the ability to demonstrate the upcoming dynamics
of various economic processes, including inflation, in short term period. The relevance of this work
is determined by the ability of high-frequency indicators, including price indices based on online &
scanner data, to evaluate the leading dynamics from microlevel and big data. This research is devoted
to the issues of assessing and constructing high-frequency indicators, which acquires special
significance for macroeconomic forecasting. The main goal of the study is to identify the capabilities
of high-frequency indicators and price indices to show leading dynamics in macroeconomics and its
capabilities for short-term forecasting (subject of the study). To achieve the stated goal, it is based on
reviewing of academic and empirical literature (as the main source of information) to systematize and
classify the types of high-frequency leading indicators and their construction methods. Also, it is
conducted with the areas of measuring various macroeconomic processes and determined the degree
of consistency of the results, which are obtained from low-frequency data comparatively to the results
on daily and online data basis (research objectives). In this research were used methods such as
descriptive, statistical and graphical analysis, the big data analysis, including the systematic approach
and comparative analysis with regression modelling. Based on the empirical results of a study it is
shown that the construction and evaluation of high-frequency indicators as leading indicators of
macroeconomic processes, can be quite interesting and combat. Finally, the high-frequency indicators
show better statistics dynamics for current changes in the economy and demonstrate the greater
significance than low-frequency indicators in VAR- & SVAR- models (scientific novelty of the work).
The prospects for further research are to show the capabilities of high-frequency indicators in
forecasting inflation in the short-term period of time. In the conclusion, the results of the work can be
used in the interests of statistical services and macroeconomic departments of the Russian Federation
to monitor current price statistics and to obtain advanced information for short-term planning purposes
from microlevel data.