The  main  objective of this study  is to identify  the characteristics  of methods  for regulating inflation  processes in an  open  economy.  In this regard, the article discusses in detail such issues as, features of inflationary  processes in an open economy, the specifics of the impact of external  factors on the level of inflation.  On  the example  of the economy of the Republic of Tajikistan, the peculiarities of the influence of the dynamics  of the exchange  rate and  foreign trade factor on the inflation rate are considered,  the main factors influencing the dynamics  of the national  currency exchange  rate are identified,  the problem of regulating inflation tendencies in conditions of high import dependence  is studied.

The  theoretical  basis  for  this  study  was  the  work  of  foreign  and domestic scientists on the problems of inflation  in an open economy, the impact  of the exchange  rate on inflationary  processes, and  the anti-inflationary monetary  policy  under  conditions  of  high  import dependence.  Statistical  data of the National  Bank  of the Republic of Tajikistan website - www.nbt.tj and the Statistical Agency under the President of the Republic of Tajikistan - www.stat.tj for 2000-2019 were used as an information  database.

Results.  The  main  factors  of  inflation  in  an  open  economy  are identified.  The mechanism  of direct and indirect influence  of foreign economic factors, in particular the exchange rate on inflationary processes, is investigated. The main factors of inflation and the mechanism  of their influence  on the example  of the Republic of Tajikistan are investigated.  The significant influence of external factors (imports,  cash flow,  exchange  rate,  dynamics  of the Russian  ruble) on inflationary  processes and economic development  of the Republic of Tajikistan has been  revealed.  The  study  shows  that  import is a factor  that  holds  back  inflationary  trends  in  the  country,  external cash  flows  form  domestic  demand, influence  the  dynamics  of  the Somoni rate, and thereby affect the inflation rate. The significance of the Somoni  exchange  rate in regulating inflation  processes has been determined.  Significant influence of external factors on the formation of the Somoni  exchange  rate also was  revealed.  Based  on the correlation  and  regression analysis,  a close relationship  is established between  the dynamics  of the Tajik  Somoni  and  the Russian  ruble. The  article studies  the peculiarity  of the use of monetary  policy in order to stimulate  domestic production and export.

The  results of the study show that in the conditions of the Republic of Tajikistan, foreign economic  factors,  in particular  the  exchange rate, largely determine inflationary trends. On this basis, an objective need arises to maintain  the stability of the national currency in order to curb inflation  and  ensure the stability of economic  development. The study showed that the dynamics  of the exchange rate of Somoni largely determined  by external than internal factors. For this reason, the presence of a significant effect of the exchange rate on inflationary trends in the long term may lead to undesirable consequences that are associated with  the problem of regulating the exchange  rate.

Purpose of research.  The relevance of the research topic is associated with  the presence of a number  of controversial issues arising in the statistical study of monetary incomes of the population of the country and regions. These issues include: comparability  of monetary income in dynamics,  assessment  of differentiation  of population  by income and  consumption,  comprehensive  assessment  of regional differences in the income  level.  The  aim of this work  is to study  the monetary income  of the population  of the Russian  Federation  as a universal characteristic  of the living standard.

Materials and methods.  The  information  base of the study was the official  statistical  materials  of the  Federal  State  Statistics  Service, including  the  materials  published  by ROSSTAT in the  “Statistical review” in early 2019. Along with traditional statistical methods,  the study  also used  the  method  of assessing structural  differences,  the method  of index  analysis  of the differentiation  of the population  by monetary  income at both the Federal and regional levels. Structural shifts  in  consumer  spending  between  groups with  different  income levels were identified.

Results.  In  Russia,  from 2014  to 2017,  the annual  decline  in real disposable income ranged from one to 3%,  with  an average annual growth rate of nominal income of 5%. In 2010–2018, more than 47% of income was concentrated  in 20% of the population of the Russian Federation with the highest incomes. The 20% of the population with the lowest income accounted  for less than 5.4%  of the total. Inequality  in the distribution of monetary income leads to the formation of consumer structures that differ in social groups with different levels of income.  In  the  study,  an  integral  coefficient  of structural shifts of Gatev was used to estimate these differences.  As a result of its calculation,  significant structural  differences  between  the groups of the population of Russia with the highest and lowest incomes were established.

A study was conducted on the level of monetary income of the population  of 14  regions of the  Volga  Federal  District  in  2018. For all  regions of the  Federal  District,  three  local  indices  were calculated on three indicators: average per capita monetary income of the population,  the average monthly  nominal  accrued wages of employees  of organizations  and  the  share of the  population  with incomes  below  the  subsistence  minimum. Their  arithmetic  mean value representing the index of monetary incomes of the population was  determined.

Conclusion.  The  results of the calculations  allowed  identifying  three groups of entities  in the  district,  which  differ significantly  in terms of cash  income  of the  population.  The  problems  of differentiation of monetary  income  in the Volga Federal  District,  identified  in the study,  are typical for many  regions of Russia.  In  order to solve the problems,  concerted  actions  of  the  regional  government  and  the state,  aimed  at  improving  the  living  standard  of the  population  of the country are necessary.

Purpose  of the  study.  The  purpose of this research  is modeling  of the current trends for changing the cost of the residential real estate. The price on the housing market is one of the most difficult indicators for the analysis,  as it reflects trends in the commodity  and financial markets,   labor  market,   reacts  to  political,  social  and  economic changes. On the housing market,  it is possible to determine  the level of income  of the population  and  their ability  to accumulate  by the price if we  consider the regional level,  then  it is possible to define degree of attractiveness  of this or that  region, etc. Development  of the housing market promotes improvement of a demographic situation and development  of social stability of the society.

Materials and  methods.  In  the  analysis  of dynamics  of changing the cost of the residential real estate, the methodological base is important.  In  this research,  the methods  of descriptive statistics for the description  of the existing trends to change  the average cost of housing  and  development  of mortgage lending  were  presented  and assessment of structure of price differentiation in regions is carried out. With  the methods of identification  and the analysis of the top trend, the adequate  model, describing a trend functionally  was constructed. For the evident representation of results of the research, tabular and graphic methods  of visualization  of data were used. For the purpose of the solution of objectives the package of the IBM  SPSS Statistics application programs was used.

Results. Results of the research allowed  to define the main trends of changing the cost of the residential  real estate in the Russian  Federation on primary and secondary markets  and to analyze  dynamics of  granting  mortgage  loans.  The  reasons  for  significant  regional price differentiation  of housing  are  considered.  Creation  of model for changing the average cost of housing on primary and  secondary markets  is carried out and forecast estimates to change the price for 2019 are received.

Сonclusion.  The  conducted  research showed  that  there is a steady trend of growth of cost for housing both on primary  and  secondary markets.  It should be noted that in the nearest future the increase in rates of gain of the price in connection  with  the legislation change since July 2019 is possible, first, it would concern primary market,  but in a consequence,  it will be reflected also on the secondary  market.

The aim  of the research  is to develop a methodology  and  assess the cross-border flows of HQs from Russia (permanent  and temporary  labor migrants),  taking  into account  the peculiarities of the legislative conditions  established  by the host country  (the criterion  of  the  entry  channel),  on  the  example  of  the  United States  and  South  Korea.

Materials and  methods.  The  focus  is on  the  analysis  of  legal regimes  of  entry  in  terms  of  the  identification   of  the  category of HQs  and  the  calculation  of their number  using a situational approach. The study used general theoretical methods and statistical methods:  analysis  of the dynamics  of absolute  and  relative values.  The  information  and  statistical  base  of  the  study  was made  up of data from the Russian  State  Statistics,  data from the statistical  Agency  – Eurostat,  data  from  the  US  State  Agency for Citizenship  and Immigration, data from the Korean Statistics Agency KOSIS, data  from the Headhunter survey.

Results:

– Three  criteria have  been developed  on the basis of which  the evaluation  of the  number  of migrants-HQs from  Russia  can  be made.  These  are the  following: the  criterion  of the  entry  channel;  the  criterion  of income  and  the  educational  criterion.  The features of each criterion and  the possibility of its application  to estimate  the number  of HQs  who  left the country  are specified.

– Identified  the boundaries  of the category of persons included in the HQs  in accordance  with  the visa regimes of the USA  and South  Korea. This  identification  has distinctive  features in each case and is determined  by the level of development  of the country for  which  the  calculation  of  the  number  of  HQs,  who  left  the country,  in this case – for Russia.

– The  estimation  of the  dynamics  of the  number  of HQs,  who left Russia  to the United  States  and  South  Korea,  based  on the criterion  of  the  channel  of  entry,  that  is,  in  accordance  with the  legal conditions  of entry  of this category of persons in each country  separately.

Conclusion.  The  article deals with  the methodological  problems of accounting  for the  migration  of Highly  Qualified  Specialists from Russia,  analyzes  the volume and dynamics  of their flows to individual  countries: the United  States  and  South  Korea.

Estimates  of the number  of HQs  who  have  emigrated  from Russia to the United States and South  Korea,  have been calculated  based on the identification  of this category of persons in accordance  with legislative modes of entry for different specialists, established in these countries,  and  taking  into account  the peculiarities of determining the HQs contingent for Russia. If the US law provides for a separate entry  procedure  for HQs,  and  the  calculation  of their  number  is not difficult,  the legislation of South  Korea does not have  special types of visas for HQs,  so their identification  and calculation of the number  is carried  out by allocating  a range of visas,  which  were used for the persons entering the country,  representing the category of  HQs  for  Russia.  The  peculiarity  of  the  approach  used  in  the work  is also that the number of HQs,  both in the US and in South Korea,  includes  persons who  have  entered  for reasons of transfers within  companies.  Calculations  similar  to those  presented  in  this paper should be made for a number of other countries – migration partners of Russia,  which  will make  it possible to obtain an overall estimate  of the number  of HQs  who  left Russia  and  to assess the loss of human  capital.

The  urgency of a comprehensive  assessment  of the current patterns of establishment  and liquidation  of legal entities in Russia  is due to standing problems of the development  of the national  economy  and increasing the volume of production of goods and rendering of various services, including based on accelerated growth of import substitution.

The purpose of the study: to assess the current  demography  of organizations  in Russia,  namely  the levels of fertility and  liquidation of organizations.  The  analytical  assessment is based on two aspects of organizational  demography:  sectoral and  regional. To  provide  a comparative  analysis  the  assessment  was  carried  out  according  to the data for 2017 and 2018.

Materials and  methods.  In  the  course of the  study,  the  following indicators  were  considered  for  19  types  of  economic  activity  and 82 regions of our country  for 2017  and  2018  as the  share of new organizations  in the  total number  of functioning  organizations,  the share of liquidated  organizations,  as well as the existing ratio of the number of new and liquidated  organizations. Official information  of the Federal  State  Statistics  Service  on institutional  transformations in the economy and demography of organizations was used as initial data.  The  research  methodology  is based  on  the  consideration  of newly  created and  liquidated  organizations  formed by industry  and territorial characteristics. Modeling of values’ differentiation of specific weights of such organizations  in the total number  of the functioning organizations  was carried out with the use of functions of density of normal distribution.

Results.  According to the results of the study,  the values  of specific weights of new and liquidated  organizations in their total number by industry  and  regions of the country were determined;  it was shown that the number of liquidated organizations in most regions and types of economic  activities for the period exceeded  the number  of newly created  organizations;  lists of regions and  industries  with  high and low values of the considered indicators were established.  The  study tested and confirmed the following two hypotheses: the presence of a significant differentiation  of the specific weights of created and liquidated  organizations  in the total number  of organizations  by types of economic activity; the presence of a significant differentiation  of the specific weights of created  and  liquidated  organizations  in the total number  of organizations  by region.

Conclusion. The results are of theoretical importance in the conduct of research and training of students. Functions of density of normal distribution can be used at justification of plans and programs of development of economy of regions. The practical significance of the research results is associated with the possibility of their use in assessing the possibility of creating enterprises in specific industries and regions. The  units of regional  and  municipal  authorities  monitoring  the  business  climate can use the results of the work.  The  given methodological  approach and  obtained  analytical  estimates  are of interest  to financial  credit and leasing companies in the justification of their policies, taking into account various activities and regional characteristics.

The   aim  of  the  research  is  the  usage  of  an  artificial  neural network  as a tool not only for forecasting,  but also for operative diagnostics of a financial  state through combining  deterministic and  stochastic factors in one model.  This  circumstance  expands the possibilities of effective influence on the formation of an acceptable  level of the company’s  financial  condition  in various activities.  The  proposed universal  model  is presented  in the article in relation  to the  company’s  characteristics  in the  housing and  utilities  sector.

The  article  proposes a  method  for  diagnosing  the  level  of  the housing and  utility  company’s  financial  condition  based on the use of a factor neural model of the financial results of their activities.

Materials and methods.  The  neural network  modeling methodology allows you to create models that have  several advantages:  learning ability  (they  adapt  to various  changes);  universality  (able  to solve a wide  range of data analysis and processing tasks);  speed (process various  data  in  parallel  mode);  ease  of use (easy  to operate  after training);  fault  tolerance  (resistant  to local  damage  to the  neural network  structure and external  noise).

One of the main  tasks that neural networks  successfully solve is the problem of classification  – the assignment  of the sample  to one or several predefined classes. Most often, the input sample is determined by  the  input  data  vector.  The  components  of  this  vector  are  the various characteristics of the sample. The  classifier in the form of a neural network  relates the object to one of the classes in accordance with the partitioning of the N-dimensional input space. The  number of components of the vector determines  the dimension  of this space. In the context of this article, the input sample is the financial condition of the organization at a particular point in time. The input vector that characterizes the sample includes a set of direct and indirect factors of the financial results of a housing and utility company. The neurons of the  output  layer  are  a  set of different  classes.  In  the  course of operation,  the neural network  assigns to each input vector a neuron in the output layer. The significance of the input data can be adjusted using connections between neurons and changing the neural network architecture. Neural networks can have a complex architecture when different  parts of the  neural  network  include  different  numbers  of connections and different neurons.

The  article develops  the  ideas  laid  down  by its authors  in [7,  8], where a neural network  of direct propagation and a way of learning with a teacher have already been used. The model, described below, has been modified due to the authors’ desire to improve it, as well as dictated by the specifics of the housing and utility companies: a list of key indicators has been developed that affect not the financial  result, but, consequently,  the financial  condition of companies in this sector of the Russian  economy;  the number  of input factors characterizing the input sample was increased,  each direct factor or group of direct factors was supplemented  with an indirect factor; direct and indirect factors explaining the same processes are combined  into clusters that influence  the  corresponding  neuron;  the  number  of neurons  in  the output layer has been expanded, the number of classes has been increased, the data are classified by means of the neural network in more detail; in the course of the program, it is possible to select the period to which  the input data (month, quarter,  half year,  year) belong. The  additions  made  a positive  impact  on the  work  of the  neural network.  The  accuracy of attributing the input sample to a specific cluster and the sensitivity of the neural network  has increased. The number of clusters has grown up to 50. Innovations  have increased the  usability  of  the  program.  New   interface  allowed  to  analyze data  monthly.  The  programmatic  way  of interpreting the data  has changed  due to the fact that not all input data  changes depending on the period.

Purpose  of the study.  Improving  the  speed and  efficiency  of decision-making on the development  of sales of products by manufacturing companies  based on the application  of quantitative  estimates of the potential  of retail  outlets,  using automated  methods  of intellectual data analysis of Big Data.

In order to create new mechanisms  for the intellectual  management of enterprises (economic agents) in the concept of the digital economy, it is necessary  to simulate  the  processes of their  interaction  in  the organizational  market  environment  from the perspective of   a multi-agent  approach.  The  approach  describes cybernetic  mechanisms of interaction  of agents with  the ability to adapt  to the needs of the population  based  on  the  analysis  of market  situations  in  order  to develop  intelligent  management  systems  for enterprises  to increase their market  potential  and  competitiveness.  Today  the paradigm  of open complex systems is used to simulate and analyze the mechanisms and processes of interaction  of agents in the market  environment. The main modern functioning of enterprises are corporate information systems,  telecommunication networks, Internet  technologies,  mobile communication systems,  Big Data,  technologies of intellectual  analysis, forecasting and machine  learning. In addition,  information  for modeling,  research and  analysis  of the market  and  the behavior  of agents can be collected from open sources on the Internet.  In the field of mass sales, the result of the introduction of innovative  technologies is the support of decision-making in the process of managing the sales of goods and  services in order to synthesize  effective business-strategies for the production  and  sale of goods, aimed  at increasing the company’s  profits.

From the point of view of the manufacturer, it is necessary to carry out strategic and tactical planning for the deployment  and maintenance of the  sales network  through  retail  outlets that  implement  or have the potential to realize the company’s products. In the overwhelming majority of cases, experts make the collection and analysis of objective data  on outlets,  and  the models  for building  quantitative  estimates of the potential of outlets are used only the internal data on the sale of goods or partners.

Materials and methods. The solution is based on the analog method of assessing the attractiveness  of the outlet, used in marketing.  Data mining  techniques  (various  clustering methods)  and  the  method  of mathematical statistics  - variance  analysis  are  also  used  to solve this problem.

Modern methods of processing large volumes of data and their intellectual  analysis  allow  us to offer new  methods  for quantifying potential based on the analysis of the whole diversity of data stored in the open information  sources.

Results. A multi-stage method of quantitative  assessment of the potential of retail outlets in ruble equivalent  was proposed. A method  has been developed for dividing retail outlets into strata based on features describing the position of the retail outlets, competitive environment, transport accessibility,  and a typical consumer. A modification  of the K-means clustering method  is proposed.

Conclusion.  The  paper proposes an  approach  to solving the  task  of promoting a wide range of products by the manufacturer  through existing distribution  networks.  A method  for quantitative  assessment  of the potential of outlets is proposed. The  proposed method  is based on the analog method of comparing outlets and uses the methods of clustering outlets across a wide range of indicators. The  results of approbation of the approach on the data for 33 regions of the Russian  Federation are given. The  results of the work  are planned  to be used in the future to solve the problem of building a matrix of consumer preferences for outlets.

The  article  describes  the  development   of  information-analytical system of decision-making in the treatment  of abdominal  organs of patients. The  structure of the system provides for the formation  of a preliminary  diagnosis of the  patient’s  condition  based  on a neural network  and statistical analysis of the electronic medical record. For the operating control of the patient’s condition  during the operation or getting a quick consultation  in the case of a critical situation,  the system provides an expert assessment  of the circumstances  with  the possibility of a surgeon’s speech dialogue with the intellectual system. Purpose.  Increase the intelligence of decision-making in the department of a medical  in-stitution  based on neural network, production and statistical models.

Materials and methods. Neural networks and the statistical approach for analyzing  and  processing a  large amount  of medical  data,  as well as computer modeling of the practical problem,  using the Java programming language,  were used to obtain scientific results.

Results. The developed prognosis program is a hybrid dynamic expert system,  the use of which  will improve the efficiency  of processes for assessing the severity of the underlying dis-ease,  taking into account pathology; predicting the risk of intraoperative  complications  in the planning mode and in real time; recommendations  of surgical tactics with  combined  surgery; predicting the risk of postoperative  complications; determine  the volume  of intensive  care in the postoperative period.

Conclusion.  The  structure  of creating  a  fuzzy  model  of predicting operational risk for performing simultaneous  interventions  depending on the patient’s  condition  based  on production  rules is considered, the  base  of  which  can  be  corrected  in  the  training  regime  of  the expert system.