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Inventories estimation by vector autoregression model

https://doi.org/10.34023/2313-6383-2016-0-8-39-45

Abstract

The lack and inconsistency of information on the availability and movement of inventories is forcing national accountants to look for new approaches for their reliable calculation, in addition to the commonly used commodity flow method (balance sheet method), due to inaccuracies, due to the many incoming payments. To refine the results the author proposes to use mathematic models, in particular, macroeconometric models of vector autoregressive time series given in the form of a system of simultaneous econometric equations relying on several time series with the slowdowns, lags. Taking first differences of variables in these models is suitable to achieve stationarity for initially non-stationary time series and for estimation of increases, in particular, on changes in inventories. The latter is a component of gross domestic product reflected in the capital account of the nation and behaves very unstable, due to expectations of economic agents, market conditions and possible seasonal influence. Initial stationarity of inventories time series, given a sufficiently lengthy observations, makes possible the calculation of their levels, in particular, for the balance sheet of the nation. One of the key moments in the calculation of inventories is their deflation in comparable prices to highlight the «imaginary» part of their value due to price changes during the time spent in their inventory. The article proposes to impute duration (turnover) as the length of the vector autoregression lag, implying thus the cycle time of the use of inventories. The change in the value due to price changes is proposed to rewrite in a kind of moving average model expressed via the functions of «impulse response».

About the Author

Victor M. Yankov
Federal State Statistics Service (Rosstat)
Russian Federation


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Review

For citations:


Yankov V.M. Inventories estimation by vector autoregression model. Voprosy statistiki. 2016;(8):39-45. (In Russ.) https://doi.org/10.34023/2313-6383-2016-0-8-39-45

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ISSN 2313-6383 (Print)
ISSN 2658-5499 (Online)