CC BY
63
Bulletin of Taras Shevchenko National University of Kyiv. Economics, 2016; 11(188): 11-15 УДК 336 Д
JEL Classification: C12, D22
DOI: https://doi.org/10.17721/1728-2667.2016/188-11/2
N. Baltes, PhD, Professor, A.-G.-M. Dragoe, PhD Student Lucian Blaga University of Sibiu, Sibiu, Romania
STUDY REGARDING THE ASSESSMENT OF THE FINANCIAL STABILITY
OF THE ECONOMIC ENTITIES
The research presents both theoretical and practicalthe evolution of the financial stability assessed through the solvency indicators, the real economic growth rate and the GDP deflator in the manufacturing companies from Romania, through the Vector Autoregression Model (VAR). The sample consists in 36 companies belonging to the manufacturing industry in Romania, listed on the Bucharest Stock Exchange, on the first and second category. The study is conducted during the period 2007-2014 and demonstrated the fact that a change in the real economic growth causes a positive change in the GDP deflator. Not lastly, the change of the real economic growth also determines a positive change of the patrimonial solvency, and a change in the GDP deflator produces a reduction of the patrimonial solvency.
Keywords: patrimonial solvency,real economic growth,GDPdeflator, Vector Autoregression Model.
Introduction. The Vector Autoregression Model was introduced by Sims [14] with the aim to characterize the dynamic behavior of a common set of variables, becoming a common method of modeling the time series [14]. The model explains the current values of a set of variables through their past values [11]. VAR model is widely used in time series analysis especially due to its flexibility and to its ease of use. The VAR model captures the dynamic structure of several variables simultaneously, and the impulse-response functions used in the model studies the propagation of the shock of a dependent variable [13].
The VAR model has been studied in numerous specialized publications, such as books written by Hatanaka [4], Lutkepohl and Kratzig [9] and Lutkepohl [10]. In order to estimate a VAR model, it is necessary to follow a series of steps, the most important being the selection of the dependent variables that will be modeled and the selection of the number of lags. Because this research aims to identify the link between patrimonial solvency, real economic growth and GDP deflator, it is important to define the mentioned variables.
The solvency represents the company's ability to meet its financial duties both in the medium and long term.The best known and widely used rate that quantifies the solvency is the general solvency ratio. In 2009, the book "Economic and financial analysis of the company: problems, approaches, methods, applications," written by Monica Petcu defines the general solvency as the "relative expression of the net asset of the company, which is the guarantee of the owners and creditors confidence in the company's management and financial health".
Another rate particularly relevant in determining the solvency of the company, which will be used in this research is patrimonial solvency ratio. In most of the publications, the patrimonial solvency is considered appropriate when its values are in the range 0.3 to 0.5. Patrimonial solvency was determined as ratio between the equity and the permanent capital [2].
Eguity
Sp =
Permanent capital
Permanent capital = Equity + Long-term debts
The GDP deflator measures the inflation rate, and expresses the average index of the prices from the economy, for
the period that is refered to. The GDP deflator is calculated as the ratio between the nominal GDP and real GDP [15].
The real economic growth "allows comparisons of the dynamics of economic development, both in time and between economies of different sizes" [16] and is determined according to the model:
The rate of real economic growth
_ Real GDP., - Real GDP0
Real GDPn
Real GDP =-
Nominal GDP GDP deflator
The unit of measure of the GDP deflator is the percentage change compared with the same period of the last year of the price index (national currency).
Methodology.The sample used in this research consists of 36 companies from the manufacturing industry in Romania, listed on the Bucharest Stock Exchange at the first and second category. The study is conducted during the period 2007-2014, the values of the variables being quarterly. The values of the macroeconomic variables: the real economic growth rate and the GDP deflator were extracted from the Eurostat database (accessed on 04.01.2016). The last update of the data published on the site was made in 01.01.2016. In order to identify the relationship between patrimonial solvency, real economic growth and GDP deflator through the VAR model, the following steps were completed: in order to make the variables stationary, the first difference operator was applied (per t - per t -1); the lags were selected;the VAR model was developed; the Portmanteau test on the residues was conducted.
Results.Empirical study regarding the relationship between patrimonial solvency, real economic growth and GDP deflator through the VAR model
We intend to identify the existence of a relationship between the level of the patrimonial solvency recorded by the the analyzed companies, the real economic growth and the GDP deflator during the period 2007-2014.
The evolution of patrimonial solvency, real economic growth and GDP deflator, determined quarterly, on the period 2007-2014 is presented in the figures no. 1 and no. 2.
© Baltes N., Dragoe A.-G.-M., 2016
Fig. 1. The evolution of the patrimonial solvency
Source: Authors own processing
It can be noticed the fact that, even if the indicator values are decreasing, during the period 2007-2014, its
values are above average: 0.81 (2014) .
between 0.94 (2007) and
Fig. 2. The evolution of the Real Economic Growth and GDP Deflator
Source/Authors own processing
Real economic growth and the GDP deflator are recording oscillating evolutions during the period 2007-2014. The VAR model regarding the correlation between the analyzed indicators, is presented in Table no. 1 .
Table 1. VAR model
Vector Auto regression Estimates Date: 0111 0/1 6 Time: 11:02 Sample (adjusted): 2007Q3 201 4Q4 Included observations: 30 after adjustments Standard errors in () & t- statistics in []
DGDP_DEF... DQUARTER... DSP
DODP_DEFm.TOR(-1) -0.664789 (0.1 6703) [-3.98003] -0.1 80406 (0.23384) [-0.771 49] -0.088535 (0.1 5833) [-0.5591 7]
D QUARTER LY_G DP(-1) 0.622526 (0.1 51 1 9) [ 4.1 1 757] 0.475807 (0.21 1 66) [ 2.24797] 0.1 6721 4 (0.1 4331) [ 1 .1 6676]
D3P(-1) 0.1 59552 (0.1 8946) [ 0.8421 6] 0.1 99892 (0.26524) [ 0.75364] -0.579559 (0.1 7959) [-3.2271 2]
C -0.00521 7 (0.00370) [-1 .40860] -0.000560 (0.0051 9) [-0.1 0808] -0.00561 1 (0.00351) [-1 .5981 1]
R-squared Adj. R-squared Sum sq. resids S.E. equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D.dependent 0.4631 04 0.401 1 55 0.01 0054 0.01 9664 7.475505 77.44721 -4.896481 -4.709654 -0.004233 0.02541 1 0.1 67630 0.071 588 0.01 9705 0.027529 1 .745373 67.35331 -4.223554 -4.036727 -0.001 367 0.028571 0.378569 0.306866 0.009034 0.01 8640 5.279647 79.051 61 -5.003440 -4.81 661 4 -0.00291 8 0.022389
Determinant resid covariance (dof adj.) 7.65E-1 1
Determinant resid covariance 4.98E-1 1
Log likelihood 228.1 459
Akaike information criterion -1 4.40973
Schwarz criterion -1 3.84925
Source: Author's own processing through the econometric program E -Views
It can be noticed the fact that a change with one unit of the real economic growth, produces in average a positive change, respectively an increase with 0.6225 units in the value of the GDP deflator. Also, the change with one unit of the real economic growth causes, in average, a positive change with 0,167 units in the value of the patrimonial
solvency. The change with one unit of the GDP deflator causes, in average, a reduction with 0,088 units of the patrimonial solvency.
In the equations below, we highlighted the dependence between the GDP deflator and it's past values, and the past values of the real economic growth and patrimonial solvency.
DGDP_DEFLATOR = C(1,1)*DGDP_DEFLATOR(-1) + C(1,2)*DQUATERLY_GDP(-1) + C(1,3)*DSP(-1) + C(1,4) DGDP_DEFLATOR = - 0,664789*DGDP_DEFLATOR(-1) + 0,622526*DQUATERLY_GDP(-1 ) + 0,159552*DSP(-1 ) - 0,005217 (1)
The dependence between these variables is strong because the R-squared indicator is 0.46, respectively 46% of the GDP deflator dispersion is determined by the variables presented in the equation below (the past values of the GDP deflator, of the real economic growth and of the patrimonial solvency).
The dependence of the real economic growth by its past values and by the past values of the GDP deflator and of the patrimonial solvency was analyzed in the equation no. 2. It can be noticed a low dependence between the mentioned variables, as R-squared indicator is 0,1676, respectively 17% of the real economic growth dispersion is determined by the variables presented in the equation below.
DQUATERLY_GDP = C(2,1)*DGDP_DEFLATOR(-1) + C(2,2)*DQUATERLY_GDP(-1) + C(2,3)*DSP(-1) + C(2,4) DQUATERLY_GDP = - 0,180406*DGDP_DEFLAT0R(-1) +0,475806*DQUATERLY_GDP(-1 ) + 0,199891*DSP(-1) - 0,000560 (2)
The equation no. 3 shows the dependence of the patrimonial solvency by its past values and by the past values of the GDP deflator and of the real economic growth. The dependence between the mentioned
indicators is medium as R-squared is 0,3785, respectively 38% from the patrimonial solvency dispersion is determined determined by the variables presented in the equation below.
DSP = C(3,1)*DGDP_DEFLATOR(-1) + C(3,2)*DQUATERLY_GDP(-1) + C(3,3)*DSP(-1) + C(3,4) DSP = - 0,088535*DGDP_DEFLAT0R(-1) + 0,167214*DQUATERLY_GDP(-1) -0,579558*DSP(-1) - 0,005611 (3)
In order to validate the relationship between the analyzed variables and the fairness of the VAR model, we rewritten the model through the least squares method (Table 2.)
Table 2. The VAR model rewritten through the least squares method
System: SYSTEM01
Estimation Method: Least Squares
Date: 0111 0/1 6 Time: 11:20
Sample: 2007Q3 201 4Q4
Included observations: 30
Total system (balanced) observations 90
Coefficient Std. Error t-Statistic Prob.
0(1) -0.664789 0.167031 -3.980029 0.0002
0(2) 0.622526 0.151188 4.117572 0.0001
0(3) 0.1 59552 0.1 89456 0.842158 0.4023
0(4) -0.005217 0.003704 -1.408596 0.1629
0(5) -0.180406 0.233842 -0.771490 0.4427
0(6) 0.475807 0.211661 2.247967 0.0274
0(7) 0.199892 0.265236 0.753637 0.4533
0(8) -0.000560 0.0051 85 -0.108076 0.9142
0(9) -0.088535 0.158333 -0.559170 0.5776
0(1 0) 0.167214 0.143315 1.166763 0.2469
0(11) -0.579559 0.179590 -3.227115 0.0018
0(1 2) -0.005611 0.003511 -1.598109 0.1141
Determinant residual covariance
4.98E-11
Equation: DGDP_DEFU\TOR = *DQUARTERLY_GDP(-1) Observations: 30
0(1 )*DGDP_DEFU\TOR(-1) + 0(2) ■ C(3)*DSP(-1) + 0(4)
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.463104 Mean dependent var -0.004233
0.401155 S.D. dependent var 0.025411
0.019664 Sum squared resid 0.010054 2.269859
Equation: DQUARTERLY_GDP *DQUARTERLY_GDP(-1) + Observations: 30
= C(5)*DGDP_DEFLXTOR(-1) + 0(6) C(7)*DSP(-1) + 0(8)
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.167630 Mean dependent var -0.001367
0.071588 S.D. dependent var 0.028571
0.027529 Sum squared resid 0.019705 1.832892
Equation: DSP = C(9)*DGDP_DEFLATOR(-1)
(-1) + 0(11)*DSP(-1) + 0(1 2) Observations: 30
+ 0(1 0)*DQUARTERLY_GDP
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.378569 Mean dependent var -0.002918
0.306866 S.D. dependent var 0.022389
0.018640 Sum squared resid 0.009034 1.969424
Source: Author's own processing through the econometric program E -Views
Also, after rewriting the VAR model through the least squares method, we fiind out that a change with one unit of the real economic growth determines an increase of the GDP deflator, with the same amount calculated after determining the VAR model itself. After rewriting the VAR model through the least squares method, we reached to the same conclusions as applying the VAR model itself. We refer more exactly to the positive impact of the change of the real economic growth on the patrimonial solvency,
but also on the negative influence of the change of the GDP deflator on the patrimonial solvency.
The Portmanteau test conducted in order to verify the residues autocorrelation in the VAR model, assumes that there is no autocorrelation between residuals (the nule hypothesis) (Table no. 3).
The probabilities associated to the Portmanteau test are higher than the significance level of 5%. Thus, the test null hypothesis that assumes that there is no autocorrelation between residuals is accepted.
Table 3. The Portmanteau test
VAR Residual Portmanteau Tests for Autocorrelations Null Hypothesis: no residual autocorrelations up to lag h Date: 01/10/16 Time: 11:13 Sample: 2007Q1 2014Q4 Included observations: 30
Lags Q-Stat Prob. Adj G-Stat Prob. df
1 2.673467 NA* 2.765655 NA* NA*
2 9.800691 0.3669 10.40197 0.3189 9
3 14.97043 0.6640 16.14612 0.5824 18
4 27.73192 0.4249 30.87092 0.2765 27
5 33.52487 0.5869 37.82246 0.3861 36
6 41.98296 0.6005 48.39507 0.3375 45
7 45.42638 0.7905 52.88649 0.5174 54
8 54.07255 0.7812 64.67672 0.4179 63
9 58.54426 0.8736 71.06488 0.5090 72
10 61.56953 0.9469 75.60279 0.6485 81
11 66.80233 0.9681 83.8651 0 0.6621 90
12 70.75423 0.9857 90.451 60 0.7184 99
The test is valid only for lags largerthan the VAR lag order, df is degrees of freedom for (approximate) chi-square distribution
Source: Author's own processing through the econometric program E -Views
Conclusion & Discussion.The study highlighted the correlation between the macroeconomic variables: patrimonial solvency, GDP deflator and real economic growth in the manufacturing industry from Romania, represented by 36 companies listed on the Bucharest Stock Exchange at the first and second category, over the period 2007-2014. Through the VAR model, we demonstrated that the change with one unit of the real economic growth causes a positive change,ie an increase of the patrimonial solvency and of the GDP deflator and that the change with one unit of the GDP deflator produces a reduction of the patrimonial solvency.
7. GeamSnu M., VAR analysis on Foreign Direct Investment in Romania,Revista Economie Teoreticä si aplicatä, 2014 - Vol. XXI, Nr. 4(593) -P. 39-52.
8. Keen S., Finance and Economic Breackdown: Modeling Minsky's "Financial Instability Hypothesis", Journal of Post Keynesian Economics,1965 - vol. 17, no 4 -p.613.
9. Lutkepohl H., Vector autoregressive and vector error correction models, in H. Lutkepohl and M. Kratzig, Applied Time Series, Econometrics, Cambridge University Press, Cambridge, 2004, pp.86-158.
10. Lutkepohl H., New Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin, 2005.
11. Luetkepohl H.,Vector Autoregressive Models European.University Institute, Department of Economics, EUI Working Papers, ECO 2011/30, 2011.
12. Petcu M., Analiza economico-financiarä a Tntreprinderii: probleme, abordäri, metode, aplicatii.Editia a 2-a, rev., Bucuresti, Editura Economicä,2009, p. 442.
13. PSuna, B., Modelarea si evaluarea impactului investitiilor directe, nationale si internationale asupra pietei muncii si evolutiei macroeconomice din Romänia-metode VAR §i VEC, 2007.
14. Sims C.A., Macroeconomics and Reality, Econometrica1980 -48 (1) - pp.1-48.
15. http://www.asecib.ase.ro/Roman/am/cap2.pdf
16. http://ec.europa.eu/eurostat
Hagiwwna go peflKonerii 20.05.16 Date of editorial approval 26.08.16
Author's declaration on the sources of funding of research presented in the scientific article or of the preparation of the scientific article: budget of university's scientific project
References
1. Akerlof G.A.Dickens W, Perry G.The macroeconomics of low inflation. Brookings Papers on Economic Activity, 1996.
2. Baltes N., Vasiu D. E.Analiza performantei financiare a entitatii economice. Editura Universitátii „Lucian Blaga" din Sibiu, 2015. pp. 104.
3. Codirla§u A. ,Moinescu B, Chidesciuc N. A, Econometrie avansata, 2010.
4. Hatanaka M..Time-Series-Based Econometrics: Unit Roots and Co-Integration.Oxford University Press, Oxford,1996.
5. EViews, EViews 4.0. User's Guide. Quantitative Micro Software, Irvine, CA.2000.
6. Floyd J. E., Vector Autoregression Analysis: Estimation and Interpretation, 2005, pp. 1-36.
Н. Балтеш, канд. екон. наук, проф., А.-Г.-М. Драгое, асп.
Ушверситет iMeHi Луч1ана Блага, Ci6iy, Румушя
ДОСЛ1ДЖЕННЯ ОЦ1НКИ Ф1НАНСОВО1 СТ1ЙКОСТ1 ГОСПОДАРЮЮЧИХ СУБ'еКТ1В
Дослiдження представляе теоретичну i практичну еволюцю ф/нансовоУ cmiüKocmi, оцнену за показниками платоспроможност/, реальними темпами економiчного зростання i дефлятором ВВП в виробничих компанiях Румунй, через ee^opHi авторегрес/'йн/' моделi. Bu6ípка складаеться в 36 компанй обробно'У промисловост/' Румунй, зареестрован на Бухарестськй фондовй бipжi, першо'У i друго'У категорУУ. Дocлiджeння проводилося за перод 2007-2014 рр i продемонструвало той факт, що змна реального eкoнoмiчнoгo зростання викликае позитивн зм1ни в дeфлятopi ВВП. Не в останню чергу, змна реального eкoнoмiчнoгo зростання також визначае позитивну змну платоспроможностi, а також змта дефлятора ВВП призводить до зниження платоспроможностi.
Ключoвi слова: родова платocпpoмoжнicть, реальне eкoнoмiчнe зростання, GDP дефлятор, векторна авторегресйна модель.
Н. Балтеш, канд. экон. наук, проф., А.-Г.-М. Драгое, асп.
Университет имени Лучиана Блага, Сибиу, Румыния
ИССЛЕДОВАНИЕ ОЦЕНКИ ФИНАНСОВОЙ УСТОЙЧИВОСТИ ХОЗЯЙСТВУЮЩИХ СУБЪЕКТОВ
Исследование представляет теоретическую и практическую эволюцию финансовой устойчивости, по показателям платежеспособности, реальным темпам экономического роста и дефлятору ВВП в производственных компаниях Румынии, через векторные авторегресионные модели. Выборка состоит в 36 компаний обрабатывающей промышленности Румынии, зарегистрированные на Бухарестской фондовой бирже, первой и второй категории. Исследование проводилось за период 2007-2014 гг и продемонстрировало тот факт, что изменение реального экономического роста вызывает позитивные изменения в дефляторе ВВП. Не в последнюю очередь, изменение реального экономического роста также определяет положительное изменение платежеспособности, а также изменение дефлятора ВВП приводит к снижению платежеспособности.
Ключевые слова: родовая платежеспособность, реальный экономический рост, GDP дефлятор, векторная авторегрессионная модель.
