Forecasting of Agroindustrial Complex Efficiency in the Region: Adaptive and Rational Expectations


  • Natalya Yurievna Timofeeva
  • . .





model of adaptive expectations, model of rational expectations, regression analysis, growth curves, level of profitability, indicators of technological efficiency of agricultural production.


The main task of the modern Russia´s economic activity is to simulate a new type of organizational and economic structure of the agroindustrial complex (hereinafter – the AIC) in the region, since the structure of the regional AIC, which developed in the post-Soviet years, has proved to be ineffective in market economic conditions. The need to anticipate the probabilistic outcome of events in the future has never been more urgent than today. This is due to the high degree of uncertainty in emerging events in society, the complexity of production control systems, and the increasing volume of information. A clear understanding of the possible state of the AIC in the future is only possible with precise forecasting methods. However, the forecasting methods are little used by managers of agricultural enterprises. As a rule, the decisions are made intuitively; thus, there is an inadequate assessment of the existing situation, based on the subjective assessment of an expert, but not on an assessment of realistic data from the mathematical apparatus. Purpose of the research is to study forecasting methods based on the hypothesis of adaptive and rational expectation and, based on the data on the operational efficiency of the Lipetsk region's AIC, to show the mechanism for their application, as well as draw conclusions about the expediency of their application to assess the region's AIC performance. Methods. The article examines the methods of regression analysis of time series forecasting, based on hypotheses about adaptive and rational expectations. As an economic series of dynamics, statistical data on the performance of the Lipetsk region (profitability level, indicators of technological efficiency of production output) are used. Results. The mechanism for building models of adaptive and rational expectations has been studied. Based on the data on the operational efficiency of the Lipetsk region's AIC, the mechanism of their widespread use has been shown. Their advantages and disadvantages have been revealed.



[1] Davnis VV, Tinyakova VI (2006), Adaptivnyie modeli: analiz i prognoz v ekonomicheskih sistemah [Adaptive models: analysis and forecast in economic systems]. Voronezh, Izd-vo Voronezh. gos. un-ta.

[2] Lukashin YuP, Rahlina LI (2012), Sovremennyie napravleniya statisticheskogo analiza vzaimosvyazey i zavisimostey [Modern trends of statistical analysis of relationships and dependencies]. Moscow, IMEMO RAN.

[3] Shaw GK (1984), Rational Expectations: At Elementary Exploration. New York: St.-Martin’s Press.

[4] Box G, Jenkins GM, Reinsel G (2000), Time Series Analysis: Forecasting & Control. New York, Wiley-Interscience.

[5] Brown RG (1963), Smoothing, Forecasting and Prediction of Discrete Time series. New Jersy: Prentice-Hall.

[6] Holt CC (1957), Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. ONR Memorandum, 52. Pitsburg, Carnegie Inst. Of Technology.

[7] Lucas RE (1976), Econometric Policy Evaluation: A Critique. In Carnegie-Rochester Conference Series on Public Policy, 1(1). The Phillips Curve. Amsterdam, North-Holland, pp: 19–46.

[8] Muth JF (1961), Rational expectations and the theory of price movements. Econometrica, 29, pp: 313-335.

[9] Chow GC (1989), Rational Versus Adaptive Expectations in Present Value Models. The Review of Economics and Statistics, 71(3),
pp: 376-384.

[10] Cesarno F (1983), The Rational Expectations Hypothesis in Retrospect. The American Economic Review, 73(1), pp: 198-203.

[11] Armstrong JS (1989), Combining Forecast: The End of the Beginning or the Beginning of the End? International Journal of Forecasting, 5(4), pp: 585-592.

[12] Hansen LP, Sargent TJ (1991), Rational Expectations Econometrics. Boulder, San Francisc, Oxford, Westview Press.

[13] Newey W, West K (1994), Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61, pp: 631–653.

[14] Dickey D, Fuller W (1979), Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, pp: 427–431.

[15] Boshoff W (2012), Advances in Price-Time-Series Tests for Market Definition. Stellenbosh Economic Working Papers, 01/11.

[16] Pesaran MH, Shin Y, Smith RJ (2001), Bounds Testing Approaches to theAnalysis of Level Relationships. Journal of Applied Econometrics, 16, pp: 289-326

[17] Katsoulacos Y, Konstantakopoulou I, Metsiou E et al. (2014), Quantitative Price Tests in Antitrust Market Definition with an Application to the Savory Snacks Markets. Journal of Agricultural & Food Industrial Organization, 12(1), pp. 1-33,

[18] Administratsiya Lipetskoy oblasti: ofitsialnyiy sayt (2017) [The administration of the Lipetsk region: the official website, available online:, last visit: 12.04.2017.

[19] Federalnaya sluzhba gosudarstvennoy statistiki: ofitsialnyiy sayt (2017) [Federal state statistics service: official website], available online:, last visit: 18.08.2017.

View Full Article:

How to Cite

Yurievna Timofeeva, N., & ., . (2018). Forecasting of Agroindustrial Complex Efficiency in the Region: Adaptive and Rational Expectations. International Journal of Engineering & Technology, 7(4.38), 556–563.
Received 2018-12-22
Accepted 2018-12-22
Published 2018-12-03