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With the increasing role of economic uncertainty, improving the efficiency of forecasts is ever so important. This book makes suggestions on how to evaluate the key economic indicators under uncertainty. It presents the interval method to study economic indicators, which will allow us to understand the possibilities of forecasting and the irregular nature of the economy. It is shown that with the accumulation of negative phenomena in a seemingly stable situation the effect of a compressed spring may snap into action. The book outlines the uncertainty relations in the economy, the minimal uncertainty interval, the effect of an expanding uncertainty band, sensitivity thresholds, as well as the principles of systematization and forecasting of economic indicators. The book presents ways to facilitate economic development, assess the quality of a forecast, and increase the efficiency of forecasts and decision-making in conditions of uncertainty.

This book explores and formulates the principles necessary for forecasting the economic processes and decision-making under uncertainty. It presents the minimal uncertainty interval to solve the issue of inflexible programs.

Purpose: This article introduces an innovative interval method to evaluate Armenia's export dynamics in uncertain economic contexts.   Theoretical Framework: Focusing on export forecasts and interval forecasts, this research addresses the dynamic nature of economic processes, enhancing accuracy in assessing economic indicators during uncertainty.   Methodology: We apply the interval approach to assess Armenia's exports, particularly in the EAEU market versus the global market. We analyze the effectiveness of export-oriented scenarios amid uncertainty.   Findings: Our analysis shows that the EAEU market offers more favorable export growth compared to the global market during uncertainty. The interval method significantly improves forecast accuracy. We emphasize the importance of prioritizing trade relations with specific countries and the economic significance of finished product exports.   Research, Practical & Social Implications: This research provides insights into Armenia's export-oriented growth prospects and offers a practical guide for policymakers. It underscores the significance of modernization, collaboration, and economic competitiveness, particularly in smaller economies.   Originality/Value: This article introduces the interval method for export dynamics in Armenia. It adds value by advocating targeted trade strategies and emphasizing economic policy focused on finished product exports during uncertain times.

Purpose: This article introduces an innovative interval method to evaluate Armenia's export dynamics in uncertain economic contexts. Theoretical Framework: Focusing on export forecasts and interval forecasts, this research addresses the dynamic nature of economic processes, enhancing accuracy in assessing economic indicators during uncertainty. Methodology: We apply the interval approach to assess Armenia's exports, particularly in the EAEU market versus the global market. We analyze the effectiveness of export-oriented scenarios amid uncertainty. Findings: Our analysis shows that the EAEU market offers more favorable export growth compared to the global market during uncertainty. The interval method significantly improves forecast accuracy. We emphasize the importance of prioritizing trade relations with specific countries and the economic significance of finished product exports. Research, Practical & Social Implications: This research provides insights into Armenia's export-oriented growth prospects and offers a practical guide for policymakers. It underscores the significance of modernization, collaboration, and economic competitiveness, particularly in smaller economies. Originality/Value: This article introduces the interval method for export dynamics in Armenia. It adds value by advocating targeted trade strategies and emphasizing economic policy focused on finished product exports during uncertain times.

The Systematization of Economic IndicatorsConditions for stable economic developmentTarget indicatorsSystematization of key indicators with the detection of their uncertainty intervals is a necessary condition for an efficient economic policy. Key indicators constitute nodes that link economic processes into one system. Links of key indicators accumulate other connections of economic processes and present them in a summarized form. The U.S. government estimates around 45,000 economic variables, and nongovernmental sources keep track of at least four million data. In this case, many of the formulated variables will be of minor importance.Thus, it is necessary to present the nodes, which summarize other variables to obtain tolerable results in the study of economic causations. This system approach should be carried out using a combination of economic objectives, normative constraints and the basic principles of regulation in the economy.The system approach and a precise mating of key economic indicators are exceptionally important for economic development. Unfortunately, there are numerous examples of inconsistencies. For example, adherence to the principle of price stability often turns into a tough, inflexible monetary policy. Not enough attention is often paid to its feedback, that is, the explicit impact on GDP, structure of GDP, exports and employment.In the most concentrated form, the key indicators are given and legislated in the so-called Magic square. In 1967, Germany adopted the Act to Promote Economic Stability and Growth, which formulated the basic principles of economic policy to avoid a subjective approach. Those principles for the key indicators, which are required as the basis for the preparation of budgets and financial planning, are as follows: steady economic growth, balanced foreign trade, a high level of employment and price stability. The “Magic square” represents the key indicators of an economy in a concentrated form. They must be considered in a single system. An attempt to single out a sole indicator with no regard to its causations to other key indicators will not yield a quality result, especially in the long run.Goodhart's law may snap into action if the monetary policy is represented by a single indicator. Once a government or a central bank starts using a sole indicator, its significance may not match the economic reality.

The Minimal Uncertainty Intervals of Economic IndicatorsTo have an unattainable exact forecast or an achievable forecast in the minimal interval?The interval uncertainty of key economic indicators represents the infeasibility to pinpoint an economic indicator within the minimal uncertainty interval. The probability of the minimal interval may change, and the interval will have to be adjusted. The minimal uncertainty interval should not be equated to the confidence interval; the uncertainty interval is not the mean value of an indicator with a statistical estimate of the probability of its deviation.The presented uncertainty interval should not be regarded as a purely statistical tool with which it is possible, as in other intervals, to estimate the probability of a particular parameter of the population. Economic indicators are always changing, and their parameters and distributions also change over time. Not to mention the substantial amount of noise generated by the markets. Considering the factor of heterogeneity of economic indicators and in most cases small samples, the exact calculation of probabilities or p-values of one or another indicator is meaningless.All outcomes are possible in the uncertainty interval; their probability is indefinable; a point estimate and the mean are an artifice in the interval. The mean or the average often gives the illusion of stability. The concept of the average is inefficient; the key here is the minimal uncertainty interval.The average value is usually uninformative. The information can be obtained with the uncertainty interval and, of course, with an estimate of the likelihood of serious repercussions when exiting a said interval. Even if some value is declared as an average, the likelihood of its fulfillment does not become higher or more preferable than other possible values within the interval. Indeed, this expression is appropriate here: do not jump off ledges in the dark if their average is a meter or three feet.When inflation is expected to be between 2 and 5 percent, this does not mean that the likelihood of the average, which is 3.5 percent, will exceed 3 percent. For illustrative purposes, let's assume that the weather will change and the air temperature will drop by 4–6 degrees; this does not mean that the likelihood of 5 degrees is greater than 5.5 degrees. Moreover, the conse-quences of exiting the interval are quite different here.

The Interval Method and SystemologyProtection from the misrepresentation of economic indicatorsA real process may not be subject to measuring, nor be exactly measured, for the economic situation is continually subject to critical alteration, with periodical considerable perturbations.It is to be recognized that there are no methods for precise measuring of the economic processes. Thus, when quantitatively formulating the precisely indeterminate processes, it must be identified whether its evaluation is distorted. The interval-based method is particularly useful for processes that cannot be precisely measured or predicted due to subjective or even objective reasons, for it will enable to describe the process while discovering its essence with no distortions.A point estimate of an economic indicator often results in the loss of several of its attributes; hence, the book presents the interval method enabling quantifying the process with minimal losses in determining the indicator attributes. It is the indicative estimation of the process attributes manifested in the reality that will give a systemic picture of the quantitative and qualitative features of said process.The interval method will enable a most complete description to be made of the essence and the manifestation of the real processes; hence, fully satisfying is the principle of Occam's razor which can be regarded as a sufficient condition for the application of this method. Given the impossibility of accurate measurement in the economy, the interval method allows us to overcome the complexity of describing economic processes.The interval determination of indicators is a method of research for precisely indeterminate indicators under the state of uncertainty natural for the economy. The interval representation of the indicator will enable a description to be made of its most likely values. The interval method of economic indicators will open a logical path from the predicament of explaining its quantitatively precise indefinability. It will help to deploy a complete picture of causal links in the economy.The capabilities of the existing methods of evaluation and forecasting the economic indicators cannot be seen as an absolute cure-all. This is no less hazardous than their complete disregard. Meanwhile, for an efficient quantitative analysis of economic processes, it is necessary to determine the content of economic causal links and the dependencies of the indicators.

The Effect of Compressed SpringThe rules of the economic game are continually changing or the unreliability of the past-based forecastsEquilibrium can be dangerous. With the accumulation of negative phenomena and with the transition of their quantity to a new quality, the “spring” of accumulated problems may be suddenly released. An unexpected issue in the market causes the spring to rebound. Incorrectly implemented policies that mainly focus on one economic indicator wind the spring up. Paradoxically, small doses of deviations, in contrast to rigid stability, reduce the risk of reaching the threshold of critical fluctuation. Moreover, slight stress allows to identify the weak links in the system. It is highly advisable to denote that a seemingly stable economic situation is by no means equivalent to a low-risk situation of negative consequences, especially in unforeseen circumstances.Econometric or other forecasting methods, based on data of even a long-term stable development or even a minor crisis, cannot, by definition, predict a future crisis caused by newly emerging factors, especially with high accuracy. In those cases, the examined variable is presented as endogenous, which means that the variable is determined by the model. The forecast is then car-ried out based on those values of selected economic variables, defined as exogenous, which are determined outside the model. The result is based on the analysis of observations of the past behavior and works only for the past, that is, for the already available data. However, the situation in the economy is volatile, sometimes subject to significant changes. The relations between variables based on already known data can always be formulated, although the forecast based on the past values of endogenous variables is rather uncertain.In mathematical methods, the possibility of a significant economic change and the factor of uncertainty are often underestimated. At the same time, artificial patterns are revealed, and cause-and-effect relations are presented in an explicit form where they may not exist. Accepting an outcome after a certain procedure does not mean it has resulted from that same procedure, since the cause-and-effect relations are by no means chronological.Econometrics can produce veritable results for the future only where the dynamic processes are not subject to significant changes.

The main objective of this work is to study the intervals of key economic indicators facilitating economic growth under conditions of uncertain economic processes. The methods herein applied for both the evaluation of key causalities of indicators and the principles of economic forecasts have been stipulated by the factor of uncertainty and by the need for systemic analysis of economic interdependencies. It should be noted that the complexity of the model, as will be demonstrated in this book, has no decisive role in uncovering the main principles of key economic causalities. Based upon the synthesis of economic research and the analysis of statistical data, this book presents a relevant and quite illustrative approach for indicator systematization and interval research. The economy, as any low-validity system, is inherently volatile and hard to predict, making it difficult to unambiguously point out the causalities of an economic system. In this context, determining the interval of indicators is most productive within the bounds in which their values have the highest likelihood.The book cites a complex analysis of key economic indicators under conditions of uncertainty, which is the natural state of the economy. It clarifies the essence of economic indicators and substantiates the need for certain devices based on their systemic analysis. Any proposal should not logically contradict the causalities of the indicator system. The systematization of economic indicators is a synthesis between the analysis of economic causalities of indicators and the solutions of specific issues in the economy.The real situation requires a study to be made not only of the state of equilibrium under growth but also mainly of its malfunction, with due regards to the fact that in the economy the unforeseen events may have substantial, even negative aftermaths. Only complex research of the system of economic indicators under uncertainty will help to formulate the principles to be achieved, which will yield adequate solutions.There exists a widespread, somewhat vulgar notion on the connection between economic research and economic practice that any economic research has to deliver an inventory of specific instructive practical recommendations, and vice versa, any efficient action in economic regulation has to be substantiated with scientific conclusions.

The precise definition of economic indicators is impossible in any model, even if the model represents the economy in an extremely simplified form. It is only possible to formulate their ratio in general terms.Illustrative examples of models are used herein since the complexity of the model is not critical for describing generalized functional interdependencies between indicators.Below is an example of a simplified quantitative economic model:where gj is the share of income in the price of the product j, ?ij is the expenses of the i-th resource in the unit production of the j-th product, Ri is the volume of the i-th resource and qj is the volume of product j.The dual problem of the considered model is formulated below:where ui is the optimal valuation of the i-th resource.From the duality principle the optimal values satisfy the following conditions:In the primal problem, the volumes of resources Ri, the share of income gj, the objective function of the direct problem and technological rates of resource consumption are known. Resources ui and volume of output qj are unknown and to be determined. It can be observed from the obtained ratio that if ui and qj are known, then the values of Ri and gj can be determined. Therefore, for any given set of variables, the optimal values of others can be obtained.Thus, if a model contains price variables gj and ui while it is necessary to determine quantitative variables qj and Ri, then separately for both quantitative and price variables m + n linear equations with m + n unknowns can be obtained. Provided that the coefficients of the unknowns are positive, the rank of the matrix composed of the coefficients of the unknowns will be equal to m + n; in so doing, the quantitative variables will be found. Under the same condition, if quantitative variables are given, then price variables will be found.Likewise, in any optimization model, point estimates of one group of quantitative and price variables can be found if only the other group is assigned a value. To determine some variables, the values of other variables must be taken for granted in advance.