1. The implementation of a more efficient way of collecting data
Prof. Ene-Margit Tiit Heli Jaago
Senior Methodologist Leading Methodologist
Methodology department Methodology Department

2. Our general aims:
Development of estimation methods for SBS investments and financial leasing variables;
Elaboration of target for data load programs (Extract, Transform, and Load process for data warehouses –ETL)
Creating and developing of warehouses metadata (repository) and normalized data model for data collection from businesses.
3/12/18

3. Project overview (1)
analysis and technical specifications of administrative data sources
general description of Metadata Framework (data about describing of data)
data warehouse dictionary - metadata
normalized data model as the main structure for administrative data repository
data load programs (Extract, Transform, and Load process for data warehouses -ETL)
3/12/18

4. Project overview (2)
The improvement of the existing estimation methods for the production of the SBS investment and financial leasing variables.
Creation of statistical models for estimating the distribution of investments in small enterprises (with less than 20 persons)
The increased usage of administrative data for small enterprises with help of statistical models created.
As a result the need for collecting empirical data will decrease.
3/12/18

5. The project is divided into two sub-projects
development of estimation methods for SBS investments and financial leasing variables
Project leader Prof. Ene-Margit Tiit)
creating of metadata warehouse (repository) of collected (meta)data
Project leader leading methodologist Heli Jaago
3/12/18

6. Prof. Ene-Margit Tiit Main target of the project:
To develop the estimation methods for the SBS investment and financial leasing variables, i.e to create the statistical models for the breakdown of investments by kind of fixed assets.
In the project we consider very small (employed <10 persons) and small (employed 10—19 persons) enterprises in areas of manufacturing, construction, trade and real estate.

7. Data
For solving the task we have a sample gathered in years 2000—2006, totally enterprises (about 3000 per year).
In general, each enterprise represents 4—5 enterprises of the total population.
From administrative sources it is possible to get information about their gross investments (E15000).

8. The components of investments to be estimated are the following:
E15120 Gross investment in land
E15130 Gross investment in existing buildings and structures
E15140 Gross investment in construction and alteration of buildings
E15150 Gross investment in machinery and equipment
E15440 Gross investment in intangible goods.

9. To estimate the structure of investment
the following ratios were calculated: y1= E15120/E15000, y2= E15130/E15000, y3= E15140/E15000, y4= E15150/E15000, y5= E15440/E15000 for each unit of the sample, where 0 ≤ yi ≤ 1; (1) y1 + y2 + y3 + y4 + y5 = 1. (2) The task is to create a model for the 5-dimensional vector Y = (y1, y2, y3, y4, y5).

10. Grouping the data
The enterprises are grouped into 8 assumingly homogeneous groups. Size of enterprise <10 10—19 Manufacturing Construction Trade Real estate In each group the structure of investments is somewhat different, soit is possible that the models have different parameters in different groups.

11. Possible types of model
In principle, there are two main ways to create a model:
Regression-type or prognostic models, where the values of the predictable variable are calculated by known values of explanatory or background variables.
Simulation models, where the model consists of random numbers having the same distribution as the predictable variable or vector.

12. Choice between the types of model
The regression-type models are useable only in the case when there exist statistical dependencies between measurable explanatory variables and predictable variables.
In this task all dependencies between investment’s ratios and background variables (year, size of investment, number of employees etc) and also the description rates R2 of models were quite small, see the following Figure.

13. Average description rate of regression models for investment’s ratio was 6,1%

14. Creating a simulation model
As the description rate of regression-type models is too small, it is rational to use simulation models.
That means, it is necessary to create a series of 5-dimensional random vectors Y = (y1, y2, y3, y4, y5) the distribution that is having similar to the empirical distribution of the sample.
The starting point is studying the structure of investments in the sample

15. Structure of investments in an enterprise
It became evident that in most cases small enterprises have concentrated their investments into one sphere, that means, in many cases most components of the investments vector Y equal to zero.
From all 31 possible combinations (5 single-component structures, 10 two-component structures, 10 three-component structures, 5 four-component structures and one structure with all 5 non-zero components about ¾ formed one-component structures, where the only non-zero ratio was equal to one, see the following Table.

16. The most frequent combinations of investments
machinery
68,72
Construction, machinery
9,08
77,8
Buildings, machinery
3,66
81,46
Land, machinery
3,04
84,5
Construction
2,77
87,27
Machinery, intangible goods
2,36
89,63
Buildings
1,73
91,36
Land, construction, machinery
1,69
93,05
Land
1,44
94,49
Land, buildings, machinery
1,25
95,74
Buildings, construction, machinery
1,03
96,77

17. Modeling the structure of investments
The simulation consists of 2 modelling steps.
The structure of a random vector is modelled (using multinomial distribution and empirical probabilities);
If the vector contains only one non-zero component, then this component equals to 1 and the others are equal to 0;
If the vector contains several non-zero components, then their values are simulated (using either Normal, Beta or Uniform distribution) checking the conditions (1) and (2) are fulfilled;
The parameters of these distributions are estimated by sample data.

18. Checking the model The model was checked using the data from 2007.
The distributions of simulated data and empirical data were compared, the results were satisfactory.

20. Heli Jaago (Leading methodologist)
* Over repeat– what is Metadata?*
Metadata – data about data
Concepts. Definitions.Data Processing rules.References.
Classifications (description of structure, versions)
XML-based ontologies (XBRL, SDMX, HL7 etc)
Workflow descriptions
Data model specifications
Informational system specification
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21. Metadata without metadata models

22. META-METAMODEL allows to describe the metamodel
METAMODEL describes the data model objects and their
relationships between
DATA MODEL describes the data and the links between
DATA describe a real-world objects

23. MMX Framework Architectural solution to the metadata
Interpretation of Statistics Estonia
MMX Framework Architectural solution to the metadata
Neuchâtel model Allows the description of statistical acitivities
Description of the survey sample, time-period, collected variables,
statistical indicators
Survey data Number, values, decimal points,..

24. Neuchâtel model variable system
Key words:
Subject area
Concept family, conceptual variable
Statistical activity, statistical activity instance
Statistical unit type
Statistical characteristic
Variable (contextual variable)
Measurement unit type
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25. User interface- MMXMetadata Navigator
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26. Related objects in Metadata Navigator
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27. MMX Metadata Navigator
Key points:
Completely metadata driven
Metamodel is not hard-coded into application
New metamodels (classifications, workflows, etc) require no changes in the application
Always in context of a single metadata object
Full context of the object (details, properties, relations) is visible
Simple navigation links to any related object via a single click
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28. Why Metadata Repository and not Wiki?
Structured not based on free text: formalized, can be queried
Capturing of constraints and business rules possible
Associations carry rich semantics and are not merely navigational links
Can be linked to other systems (SQL for database apps, RDF etc. For semantic web apps)
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29. What is MMX? Data layer (data model, database objects and APIs)
Metamodels, methodology for creating and implementing them in data layer
Technological stack (ORM, Application server, AJAX, ...) for creating Web applications based on data layer and metamodels
Experience in creating and deploying such applications in specific customer environments
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