fluCTuATiONS Of liThuANiAN ECONOMY: idENTifiCATiON rulES ANd fEATurES

This article is focussed on the problem of economic-statistical calculations and includes an economic-statistical research of economic fluctuations. The author also discusses the rules for statistical identification of economic welfare, economic development features of Lithuania after it became a member of the European Union. The notion of specific economic indicators, their classification issues, and the role and place of economic indicators presenting a systemic view of the country’s economy through identification of economic activities and their development are also discussed.


Introduction
Economic state and public conditions are usually identified as a country's welfare.Therefore, short-term economic trends, the current situation and the evaluation of the factors that determine it are always very relevant topics.These topic have been analysed from different angles by many authors, such as Aguiar, Gopinath (2004), Levine Stephan D., Krehbiel, Berenson (2005), Daniel, Terrell (1992), Kedaitis (1999), Rinne (1994), Wonnacott, Wonnacott (1994), Blanchard (2007), Blaug (1985,1986), Debreu (1986), Martinkus, Zilinskas (2001), Friedman (1966), Gary, Hansen (2002), Jay (1980), Mankiw, Romer, Weil (1992), Snieska, Baumiliene, Bernatonyte (2001), Mendoza (1991), Bordo (2008) and a plethora of others.In the researches of these authors, we will find various methodical and methodological approaches which are used for economic cyclicality research.Nevertheless, a more rational methodology , which could be used to assess the economic fluctuations' peculiarities of interrelated numbers, a kind of integrated system language are missing.Systemic views in assessing or calculating concrete objects which reflect the situation and perspective of a certain country is also rather poor.The assessment is limited to the statement that during the last quarter of 2011 Lithuania's statistical data show the change in economic growth direction in which the economy moves into the growth stage.Nevertheless, today Lithuania is experiencing the effects of the political-economic decisions made at the beginning of the last decade of the last century.The current Lithuanian situation is characterized by a stratified society, division between the wealthy minority and the poor majority.For this reason, the country's social, cultural, moral and demographic situation is deteriorating.Despite this distinctive pessimism, Lithuania's economy is potentially getting closer to the change in trends.Nevertheless, the reliability of similar research is questionable, at least in some respects: the optimal statistical identification method was selected for these evaluations; therefore, the question arises whether the methodical side of calculations is optimal and provides reliable and optimal data for evaluation.
In this research, we will mostly focus on the last of the below mentioned historical development stages of Lithuania's economic-statistics practice and theory: The author will give priority to the explanation of the country's economic cycle consistent with the realities of the country, which is presented in the book "Ūkio statistika (Teorijos ir praktikos apybraižos)" ("Economic statistics.Essays in theory and practice") (1995): economic cycle is reflected by certain economic fluctuations observed in the • overall economy of the country, when the economic activities are based on profitbased companies; economic cycle classification by periods: the economic expansion period, which • is similar for all economic activities, and the economic recession period, which later develops into the economic expansion period of a next economic cycle; the course of these events is repetitive, but not periodical; • the length of an economic cycle can fluctuate from one to twelve years; • economic cycle cannot be divided into similar but shorter cycles whose the • amplitude would be the same as of the main cycle (p.65-66).
The aim of this research is to determine the way to statistically identify the components of economic fluctuations and to formulate the methodology of economic-statistical calculations.
The study object: components of economic fluctuations.
The study sources: analysis of scientific statistics and economic statistics literature, publications of the Lithuanian Department of Statistics and estimates of the country's gross domestic product.
The research methods: data collection, comparison, grouping, aggregation and other quantitative and specific economic-statistics methods.
The study results and practical significance comprise not only the discussed ways of identifying economic fluctuation components, but also the methodology for their numerical identification, their optimality which was tested using calculations on the experimental level.

Notions for statistical identification of economic welfare
The term "identification" includes a wide range of considerations.In the context of economic-statistical calculations and economic welfare assessment, "statistical identification" is more important than "identification".Nevertheless, to identify [Latin identificare] means to acknowledge, determine the same, establish the equivalence.
The authors of the book "Ūkio statistika (Teorijos ir praktikos apybraižos)" (1995) state that statistical identification is perceived as formulation of statistical definitions which allow to identify an economic phenomenon or a process (p.8).Therefore, the concepts of most of the social sciences have to be concretized and adapted in order to numerically describe and investigate a particular economic phenomenon or process.Simple indicators are used to explain a separate economic phenomenon, its parts or independent components which describe the aims of economic policy and the ways of achieving them.
The above notions are general and may be adapted to various economic-statistical calculations, such as numerical description of a country's economic welfare.The main calculation premise in this description might be that the gross domestic product is an optimal indicator which provides a synthetic view of the economy.Historical gross domestic data are an informational database which enables to thoroughly describe an economic development stage.The characteristics, features, contents and views of these development stages in Lithuania's economic-statistical practice and theory confirm them and relate to the overall context -features of the country's economic development.In its stages, and especially in the last stage when Lithuania became a member of the European Union, the notions of economic state and the statistical identification of its growth were formed.We will also assume that the indicators provided below can be attributed to these notions and will be sufficient for the statistical identification of the main economic features.Their main calculation notions are as follows: Gross Domestic Pr • oduct (GDP) -an indicator which shows the economic development level of a certain territory.Gross Domestic Product and value added are calculated using the production method of the Lithuanian Department of Statistics, which corresponds to the requirements of the European System of Integrated Economic Accounts (ESA).The Department of Statistics calculates GDP data using the following formula: GDP = gross added value + taxes on productssubsidies on products.Gross added value is calculated by adding up added values created in separate economic sectors: gross added value = AV 1 + AV 2 +... + AV 16 .This research uses added values created in the following economic sectors: manufacturing, wholesale and retail trade repair of motor vehicles, motorcycles, and personal and household goods sector, transport, storage and communication sector, real estate, renting and other business sectors, construction sector.
In order to evaluate the gross domestic product and its dynamics the author of this article used data from the EUROSTAT, Lithuanian statistics, financial and other institutions, periodicals.The author also used the material of conferences and symposiums, electronic references.The data and other information sources were used for evaluation of the following indicators: growth of Gross Domestic Product / chain-linked volume growth (%).It is the • growth rate of sums of all final goods and services produced in a certain period and calculated using base year (comparable) prices; the average yearly change in the harmonized index of consumer prices (%) • (HICP).It is a Laspeyres type "consumer inflation" index which methodologically corresponds to the indexes used by other EU countries.This index shows an average price change, while keeping constant household consumption expenditure's structure of the base year and consumer population composition.HICP shows the price change of goods that are most popular in the EU countries, while the consumer price index shows the price change of the goods most popular in our country; the average yearly change in consumer price index (%).It is the main inflationary • indicator; its aim is to evaluate the average overall consumer price change and to estimate the price change trend in the country; unemployment rate (%).It is the economic indicator showing the fraction of the • labour force that is not occupied; a ratio (%) of all residents who can and want to work but have no job to all employable residents (workforce); budget deficit / surplus.It is a difference between the State's expenditures and • revenues; when expenditures are greater than revenues, it is a budget deficit, and when the State's revenues are greater than expenditures, it is a budget surplus; gross Domestic Product per capita, expressed in purchasing power parity.

•
Purchasing power parity is a collective artificial currency unit used in order to eliminate price level differences among the European Union countries.Economic aggregates are expressed in purchasing power parity and obtained by dividing their initial value in national currency units from the corresponding purchasing power parity.
Nevertheless, statistical identification is inseparable from its methods and procedures.Statistical identification is a specific process in which statistical methods are important.However, using only statistical methods for the evaluation of economic-statistical country's welfare would not be sufficient.Statistical methods are, and must be, a content element of the statistical identification of welfare in its broader sense.According to Schlittgen (1993) 2001) and others, effective results of economic-statistical calculations are possible when combining various methods.Similar views are also expressed in the literature analysing the problematic of economic activities of companies and other micro-objects (e.g., Mackevicius, 2005).We should also consider the matters that are important for the economic and tactical reflection of the country's welfare, its perspectives and other social-economic phenomena.One of statistical identification results is the nature of relation among economic-statistical indicators.Even though this is an important aspect, economic-statistical indicators can also be explained through their calculation consistency.This consistency is explained in Fig. 1.
Consistency of economic-statistical calculations presented in Fig. 1 is only a generalized view which shows the diverse interaction of statistical and other methods.Undoubtedly, the "beginning of all beginnings" is the statistical identification whether it is a particular macroeconomic or microeconomic phenomenon, economic-statistical calculations in a particular sphere of economic activities or a micro-object.once the indicators are determined, data acquisition methods should follow, together with data precision and reliability, reporting procedures, analysis and modelling.
Statistical identification procedures become important in the context of data reliability, more than what is entered into a specific category.Finally, when the notion for calculating the indicator is defined, its calculation method should be chosen, the, indicator components must be identified, the ways of obtaining the needed data for calculations must be outlined, etc.These steps are important for the economic-statistical evaluation of the country's welfare.Nevertheless, based on the notion that the current calculations of the basic economic indicator -gross domestic product -used in statistical practice are favourable for the reliability of economic-statistical calculations performed on its basis, we can define the possible courses of analysis.Two main directions emerge: assessment of the overall situation and assessment of the situation in the context of economic fluctuations.

Economic development features of Lithuania after joining the European Union
The author presents the dynamics of the main macroeconomic indicators in Table 1 which describes Lithuania's economic state after joining the European Union.Lithuania's gross domestic product in 2010 started to recover and grew to 95,074 million Lt. Its gross domestic product per capita amounted to 28,926 million Lt. or by 1,402 million Lt. more than in 2009.The highest growth of created value added was recorded in mining and quarrying -327.7 million Lt (10 %), manufacturing -16024.5 million Lt (9.9 %), financial intermediation -2027.5 million Lt (8.4 %), and transport and storage -9953.3 million Lt (7.2 %).A decline was noted in other economic sectors: construction -396.4Lt (7.3%), information and relations -244.2Lt (7.3%), real estate, rent and other business activity -524.7 Lt (8.5%).
The year 2010 can be perceived as a breaking year in Lithuania's economic development.The author would like to stress one of the macroeconomic indicators mentioned in Table 4 -the gross domestic product.Nevertheless, the trend of the indicators could be evaluated as is shown in Fig. 2  The year 2008 brought a rapid grow in inflation, which almost doubled as compared with 2007.
Data in Fig. 4 show that the consumer price index decreased by 0.3% in December 2009, since the second half of that year was dominated by a deflationary trend.This trend was reversed only briefly in September, after the value added tax was increased from 19% to 21% and the cigarette excise tax augmented.Foreign direct investment also slightly decreased in 2008, whereas the unemployment level increased.Nevertheless, the change in the level of direct foreign investment and unemployment in 2008 had no great influence on Lithuania's business conditions.However, the rapid growth in inflationary pressures during the first quarter of 2008 became an important negative factor in the country's economic development.
Conclusively, the above data show that Lithuania's gross domestic product fell in the fourth quarter of 2008, inflation increased together with the price level due to a higher excise burden, but foreign trade augmented.Even though Lithuania's economy did not yet reach its pre-crisis level, it rebounded well.During the mentioned, period gross domestic product adjusted to inflation grew by 53%.Lithuania experienced a robust economic development during the period 2001-2007.During this period, the country's economy grew at an average rate of 8% per year.In Table 2, the main modules sufficient for the description of development trends of Lithuania's gross domestic product during the period 2000-2010 are presented.Data presented in table 2 show that the trend of Lithuania's gross domestic product can be described using a first-degree polynomial and an exponent.The most rational approach is an exponential trend, since its mean absolute error is the lowest, showing the functions' optimality.Based on this assumption, we can forecast that Lithuania's gross domestic product in the upcoming years will be described by values presented in Table 3.These calculations are based on the known trends and processes of the time and disregard the dynamics of this field.Currently, we have a unique situation when today's exports exceed the volume of exports before crisis.Nevertheless, this fact does not allow much optimism.It can be a reflection of the Lithuania's struggle to attract the streams of wanted and ambitious foreign direct investment.There is a plethora of reasons: the lack of qualified and economically active residents, interpretation dynamics of rational economic policies, corruption, emigration trends, etc. Emigration processes and the trends of natural growth rate might be the reasons for the rapidly decreasing number of economically active residents.The search for work abroad is stimulated by worsening expectations and salary differences: the average salary in Lithuania is almost seven times lower than in the richest EU countries.

Gross domestic product and components of economic fluctuations
For this research, the author makes an assumption that gross domestic product is the totality of economic fluctuation components, the totality of long-term, seasonal, cyclical and irregular fluctuations.The origins of rational calculations in support of this assumption are presented in Fig. 7. Later, the author describes the adjusted data using the optimal mathematical function and calculates the theoretical numbers -an overall cyclical and irregular fluctuation.Then, the seasonally adjusted gross domestic product is divided by this indicator, and an indicator showing cyclical fluctuations is obtained.These fluctuations are described by an optimal mathematical function, at the base of which irregular fluctuations are calculated.

Dependencies between components of economic fluctuations
Two main methodology features are seen from the information presented above: The research of economic cycle components is performed using analytical • smoothing and the least squares method.
Selection of an optimal mathematical function for calculation stages of the main • economic cycle components.
These features of the methodology at hand are closely related, since the least square method was also used for selecting the type of mathematical function.It is essential for the function to be optimal on the basis of certain criteria.Selection of the optimal function using the least square method is natural.In Levine, Stephan, Krehbiel, Berenson (2005), Daniel, Terrell (1992), Martisius, Kedaitis (2010), Rinne (1994), Schlittgen (1993) and in many other publications we will find a theory of statistical conclusions, inferential statistics, which makes it easier to discuss the decisions in this context.The following solution is proposed in the book "Ūkio statistika (Teorijos ir praktikos apybraižos)" (1995): "the function that reflects the trend of the analysed process best must be determined when there are many functions.In order to obtain the answer to this question, we need to calculate the mean absolute error (%), the standard mean error (%), the root-mean-square deviation and the sum of squares" (p.82).The minimum of these statistics will be the criterion of the optimal function.In this case, the following statements are helpful in selecting the type of the function, the direction and content of calculations: F test: if the actual value ( • F) of the Fischer criterion is lower than the critical value for the selected confidence interval α (F α ; sufficient results are obtained in research of economic fluctuation components with α = 0.05, degrees of freedom k 1 = -1 and k 2 = N -m, where m is the number of equation parameters and N is the length of the time series), in other words, F < F α , then the correct function is selected; A first-degree polynomial is selected: where a and b are the equation parameters and t is time.Then the equation parameters are calculated, and the hypothesis is tested: H 0 : the increase in the degree of the polynomial causes the residual variance to decrease, but this decrease is statistically insignificant; H 1 : the increase in the degree of the polynomial causes the residual variance to decrease, and this decrease is statistically significant.The content of seasonal, cyclical and irregular economic fluctuations' data presented in Table 6 in the context of proposed methodology would be: Data in Tables 5 and 6 confirm the vitality of the discussed methodology in the statistical identification of economic fluctuation components.They offer a more precise view than dves classical research of economic fluctuation components composed using the moving average methodology, in comparison to methodologies using partial content cases of fluctuation components' connections.Nevertheless, it is not unconditional for the presented calculations and methods.This methodology is a provisory but often sufficient way to perform calculations.Economic fluctuations, causes creating them, their interrelations are diverse and their thorough identification is a task of a different type.

Conclusions
Economic state and public conditions are usually identified as a country's welfare • components.Gross domestic product is one of the indicators of the economic Concretized notions become economic-statistical indicators.According to Rinne (1994), economic-statistical indicators used to describe the economic activity are called economic indicators.They can be classified according to various features.The main classification feature is the indicators' classification based on their complexity.In dependence on this feature, economic indicators are classified into simple and general.General indicators are synthetic and assess the overall economic situation in a country.The class of general indicators is integrated.Combinations of simple indicators are attributed to this class.

First stage .
FIG. 1. Stages of economic-statistical calculations . The year 2008 was exceptional during this period.The analysis of gross added value by industry for the period 2008-2009 has shown, that growth (million Lt) was recorded only in the sectors of education -382 million Lt. (7.9%), electricity, gas and water supply -132 million Lt (4.3%), healthcare and social work -94 million Lt (2.9%).Value added in all other sectors declined: in construction -by 4,608 million Lt (46.6%), manufacturing -4,580 million Lt (25.3%), wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods -3,163 million Lt (18.5%), real estate, rent and other business activities -1,601 million Lt (12.0%), agriculture, hunting and forestry -937 million Lt (25.7%), transport, storage and communications -806 million Lt (6.6%).Five economic sectors were most important: transport, storage and communication; real estate, rent and other business activities; trade, hotels and restaurants; construction; and other economic activities.Value added created by these sectors in 2008 had a greatest comparative weight in Lithuania's gross domestic product (see Fig. 3.).

FIG. 5 .
FIG. 5. lithuania's gross domestic product, 2006-2010 FIG. 7. Components of economic fluctuations and their dependencies seasonal fluctuations result from seasonal time changes, various national or • religious holidays, customs; irregular fluctuations are unforeseen changes in economic fluctuations, which • result from different changes of the atmosphere, new technological discoveries or political events; cyclical fluctuations are long-term and irregular economic fluctuations.They • are caused by factors formed in the country's economy, internal factors of the economy, which are part of the common economic life in the country (Ūkio statistika (Teorijos ir praktikos apybraižos), 1995, p. 66).

TAbLE 1 . lithuania's main macroeconomic indicators, 2005-2010
Table1shows that the macroeconomic indicators grew together with Lithuania's economy until the end of 2008, starting with 2009 the nominal gross domestic product, export and import shrunk.When comparing the year 2010 with 2009, we see a recovery of the economy, since the nominal gross domestic product grew together with the foreign direct investment, export and import.The data presented in Table1imply that the

of economic-statistical calculations country
(1,economic welfare grew until the end of 2008, since its gross domestic product, export and import grew.The starting point of Lithuania's new economic growth period is related to the membership in the European Union.According to the data of the Lithuanian Department of Statistics, the growth of the gross domestic product in the national currency in 2005 amounted to 14.9%.The highest growth of created value added was recorded in the sectors of manufacturing(1,736million Lt, 14.7%), real estate, rent and other business activities (1.523 million Lt, 25.6%), wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods (1,353 million Lt, 13.6%), transport, storage and communication -(1,084 million Lt, 15.1%).The gross domestic product grew by 10,732 million Lt (14.89%) in 2006 and reached 82,792.80 million Lt.The highest growth of created value added was recorded in the sectors of construction (1,637 million Lt, 33.3%), real estate, rent and other business activities (1,584 million Lt, 21.2%), manufacturing (1,420 million Lt, 10.5%).Nevertheless, private households created a less value added than in the previous years.The value added of this economic activity decreased by 21 million Lt. (19.9%).In 2007, the gross domestic product reached 98,138.72 million Lt, and as compared with 2006 its growth amounted to 15.346 million Lt (18.5%).The following sectors, like in 2006, grew most rapidly: construction -2.438 million Lt (37.2%), wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods sector's The highest growth of created value added was recorded in the sectors of real estate, rent and other business activity (2,878 million Lt, 2.5%), wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods sector's (2,405 million Lt, 14.8%).However, the added value created by the agriculture, hunting and forestry sector declined by 228 million Lt. (6.3%).The gross domestic product in 2009 shrunk by 19,957 million Lt (17.9%) and amounted to 91,525 million Lt.The highest growth of created value added was recorded only in the field of education (382 million Lt), whereas the added value in all other sectors shrunk: in construction by 4,608 million Lt. (87.3%), manufacturing by 4,580 million Lt. (33.9%), wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods sectors by 3,163 million Lt. (22.7%), real estate, rent and other business activities by 1,601 million Lt (13.6%), and financial intermediation by 1,429 million Lt (76.9%).