The aim of this research to set up a system which helps to estimate the impact of public funding from projects. The impact depends on all factors affecting demand for the good or service provided under the structural intervention at stake. Thus, the present research provides a pilot econometric model for simulating the main mechanisms that lead to public investment impact from the perspective of 42 counties for the year 2006. The importance of this time span is due to the fact that it represents the end of the second programming period for European Union funds and it reveals the impact of economic structural measures at microeconomic level. Data observed to estimate the linear regression model contain at least 50 observations and the t test, the F test and the coefficients of determination are used to analyse the worthiness of the model after prior estimation of the parameters. Findings showed that the independent element of the model is unbiased, whilst the regression coefficient is biased.econometric modeling, public spending, evaluation, monitoring, pilot project, micoreconomics

Mihaela Ştefănescu

Ruxandra Savonea

English

Research Papers in Economics

Romanian Economic and Business Review – Vol. 4, No. 4  103 
ECONOMETRIC MODELLING FOR SIMULATING THE 
ECONOMIC IMPACT OF STRUCTURAL REFORMS IN ROMANIA: A 
PILOT PROJECT 
 
Ruxandra Savonea and  
Mihaela Ştefănescu
∗ 
 
 
Abstract:  
The aim of this research to set up a system which helps to estimate the impact of public funding 
from projects. The impact depends on all factors affecting demand for the good or service provided 
under the structural intervention at stake. Thus, the present research provides a pilot econometric 
model for simulating the main mechanisms that lead to public investment impact from the perspective 
of 42 counties for the year 2006. The importance of this time span is due to the fact that it 
represents the end of the second programming period for European Union funds and it reveals the 
impact of economic structural measures at microeconomic level. Data observed to estimate the linear 
regression model contain at least 50 observations and the t test, the F test and the coefficients of 
determination are used to analyse the worthiness of the model after prior estimation of the 
parameters. Findings showed that the independent element of the model is unbiased, whilst the 
regression coefficient is biased. 
 
Keywords: econometric modeling, public spending, evaluation, monitoring, 
pilot project, micoreconomics 
 
JEL Classification: C40, D61, D63, D7, E20  
 
I. Introduction 
Budgetary policy modelling is a must in the current state of affairs of the 
member states in the Community. The present paper provides a pilot econometric 
model as a specific instrument for replicating and simulating the main mechanisms of 
a the regional and national Romanian economic system from the perspective of 42 
counties for the year 2006. The importance of this timespan is due to the fact that it 
represents the end of the second programming period for European Union funds of 
2000-2006 and results reveal important benchmarks for evaluating the impact of 
structural interventions made by the Union in Romania on welfare distribution, 
having  in mind the overall expenditures and revenues of the population in that 
specific year. Data observed to estimate the linear regression model contain at least 
50 observations and the t test, the F test and the coefficients of determination are 
used to analyse the worthiness  of the model after prior estimation of the parameters.     
 
                                                           
∗ Ruxandra Savonea and Mihaela Ştefănescu are Ph.D students at the Academy of Economic Studies 
in Bucharest. E-mail: ruxandra.savonea@gmail.com, mihaella.stefanescu@gmail.com Econometric modelling for simulating the economic impact of structural reforms in Romania: a pilot project 
 
104 
1.1 Characteristics of OLS estimators 
Relevant data of the total revenues and expenditures in the 42 Romanian 
counties in 2006 are as follows: 
 
Counties 
Total revenue 
 (million lei) 
Total expenditures 
 (million lei) 
Alba  457,4 422,6 
Arad  626,8 548,7 
Arges  785,4 739,2 
Bacau  781,5 733,8 
Bihor  938,9 804,6 
Bistrita-Nasaud  382,9 354,3 
Botosani  476,2 456,2 
Braila  388,4 357,9 
Brasov  811,5 756,1 
Bucuresti  4238,7 3870,1 
Buzau  521,8 494,7 
Calarasi  301,1 282,2 
Caras-Severin  389,1 385,9 
Cluj  965,8 848 
Constanta  1016,7 866,9 
Covasna  258 235,3 
Dambovita  536,7 507,6 
Dolj  765,5 709,7 
Galati  718,3 667,3 
Giurgiu  306,3 288,8 
Gorj  456 422,3 
Harghita  399,5 355,4 
Hunedoara  626,4 574,9 
Ialomita  296,4 256,4 
Iasi  928,9 835,6 
Ilfov  478,3 398,3 
Maramures  590,9 543,5 
Mehedinti  322,5 303,7 
Mures  678,7 621,4 
Neamt  568,1 523,5 
Olt  462 436 
Prahova  913 864,1 
Salaj  312,1 272,2 
Satu Mare  426,7 409,5 
Sibiu  648,2 607,9 Romanian Economic and Business Review – Vol. 4, No. 4  105 
Suceava  768,6 734,9 
Teleorman  429,1 392,3 
Timis  974,3 920,1 
Tulcea  351,6 290,5 
Valcea  484,3 444,8 
Vaslui  500,2 461,6 
Vrancea  425,8 394 
TOTAL  27708,6 25392,8 
 
The data were sampled from the National Statistics Institute (www.insse.ro) 
from the TEMPO-Online  database which contains time series data. 
 
 
 
The least square method was applied using the using the Excel for all 42 
Romanian counties which form the overall population in order to detect the 
dependency of the total expendtures from the total revenues. Exstimations issued 
through this model are presumed to be the real values of the parameters associated 
to the population. 
 
The results of the OLS are presented in the table below: 
 
SUMMARY OUTPUT         
            
Regression Statistics          
Multiple R  0,999255           
R Square  0,99851           
Dependency between the total revenues and expenditures 
y = 0,9109x + 3,6355
R
2 = 0,9985
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 1000 2000 3000 4000 5000
Million LeiEconometric modelling for simulating the economic impact of structural reforms in Romania: a pilot project 
 
106 
Adjusted R Square  0,998473           
Standard Error  21,58427           
Observations 42           
            
ANOVA            
   df  SS  MS  F  Significance F   
Regression 1  12486222,28  12486222,28  26801,31677  3,66124E-58   
Residual 40  18635,23704  465,8809261       
Total  41  12504857,52            
            
   Coefficients  Standard Error  t Stat  P-value  Lower 95%  Upper 95% 
Intercept 3,635508  4,956548249  0,733475808  0,467545619  -6,382049356  13,65307 
Venit total  0,910912  0,005564147  163,7110771  3,66124E-58  0,899666888  0,922158 
 
RESIDUAL OUTPUT   
    
Observation 
Predicted Total 
Expenditures Residuals 
1 437,412  18,78798407 
2 238,6509  -3,350919792 
3 358,0715  27,82845829 
4 277,9112  4,288753703 
5 835,2986  28,80142682 
6 459,2739  2,326085322 
7 574,5954  -25,89543058 
8 3864,72  5,379898782 
9 883,3948  -35,39475043 
10 391,502  2,497971447 
11 929,7602  -62,86019403 
12 478,9496  15,75037645 
13 521,1249  2,375130109 
14 492,5222  15,07778097 
15 394,508  -2,208039631 
16 439,3249  -41,02493207 
17 700,939  8,76101289 
18 323,9123  -33,41232492 
19 367,545  -12,14503117 
20 282,648  6,152008974 
21 419,0116  3,28841552 
22 420,2869  2,313138093 
23 357,4339  0,466097 
24 849,7821  -14,1820811 
25 273,63  -17,22995779 Romanian Economic and Business Review – Vol. 4, No. 4  107 
26 703,7628  31,1371843 
27 657,9439  9,356080431 
28 719,0661  20,13385518 
29 287,9313  -15,73128322 
30 891,1375  28,96249376 
31 444,7904  0,009593244 
32 297,4048  6,295227317 
33 392,3218  17,17815024 
34 742,841  13,25904029 
35 858,8912  -54,29120558 
36 715,5136  18,28641372 
37 574,2311  0,668934395 
38 424,4771  11,52294083 
39 352,4239  1,876115463 
40 594,089  13,81104303 
41 541,8937  1,606326296 
42 621,8718  -0,471786631 
 
1.2 Summary output Table 
Analysis of the results showed that from the SUMMARY OUTPUT table 
offering information on the worthiness of the model, R= 0,99925 reveales that there 
is a strong connection between the model parameters. Moreover, 
2 R  = 0,998509 
shoes that 99,85 % of the variation of total expenditures is determined by the total 
revenue level, so that it represents a determinative factor of the model. Due to the 
fact that the average square deviation of errors is  u s = 21,584274, the correlation 
report and the determination coefficient are close to 1 so that the simple linear 
regression model can be considered valid. 
  
1.3 Anova table 
The ANNOVA Table reveales that F = 26.801,32 which is a big value and 
Significance F = 3,66E-58, by consequence a very small value, concluding that the 
model adjusts well the sample data. 
 
1.4 Residual output table 
Intercept is the independent element, thus cofficient  a ˆ is 3,6355. The 
independent element is the point where the exogenous variable equals 0. In 
conclusion, the total expenditures level is 3,6355, if the total revenue is 0. Because 
0 a t = 0,7334 and the significance threshold P-value is 0,4675>0,05, the coefficient is 
not significant. The Confidence Interval for this parameter is -6,38205 ≤ ≤α  
13,65307. Econometric modelling for simulating the economic impact of structural reforms in Romania: a pilot project 
 
108 
Coefficient  b ˆ is 0,91 which means that the revenue increase by 1% determines 
an increase in the total revenues by 1%, therefore, the total expenditures level will 
increase by 0,91. Due to the fact that  1 a t = 163,711 and the significance threshold P-
value is 3,66E-58<0,05  means that this a significant coefficient. The Confidence 
Interval for this parameter is 0,899667 ≤ ≤ β .0,922158. 
 
For the independent element of the model (Intercept), P-value = 0,4675, 
concluding that rejecting the hypothesis that the Intercept equals 0, there will be a 46% 
error. In conclusion, the independent element is no signifficantly different from 0. 
 
Regarding the Confidence Intervals, for the theoretical independent element, 
the interval is (-6,38205, 13,65307). In addition, for the slope of the regression 
equation we have the Confidence Interval  (0,899667, 0,92215801). It is very 
important that none of these Confidence Intervals contain 0 in order to make the 
assumption that the model is valid. 
 
II. Conclusion 
In order to finalise the research, the model was checked for any behaviour 
distortions of the estimation by extracting 30 samples out of 10 counties. For each 
sample we applied the method of least squares in view of estimating the parameters 
of the regression models. Results are aggregated in the tale below: 
 
 
Estimations of the linear regression model for various samples 
  Parameter b  Parameter a  
SAMPLE I  0,913735952 -6,776491812 
SAMPLEII  0,896178877 7,34152897 
SAMPLEIII  0,922172001 -0,505534353 
SAMPLEIV  0,859074039 25,80192753 
SAMPLEV  0,802227534 52,61527764 
SAMPLEVI  0,854058987 31,82990717 
SAMPLEVII  0,913685202 3,106485105 
SAMPLEVIII  0,94603653 -17,97586774 
SAMPLEIX  0,911004828 11,18535746 
SAMPLEX  0,863267446 21,85895454 
SAMPLEXI  0,91507055 12,72408929 
SAMPLEXII  0,941591495 -4,847258257 
SAMPLEXIII  0,980589735 -33,94349337 
SAMPLEXIV  0,957452179 -20,42890225 
SAMPLEXV  0,913516245 -5,840508736 
SAMPLEXVI  0,911856849 -0,990140053 Romanian Economic and Business Review – Vol. 4, No. 4  109 
SAMPLEXVII  0,911141139 11,76752161 
SAMPLEXVIII  0,930952531 -3,950102424 
SAMPLEXIX  0,897929762 14,54030544 
SAMPLEXX  0,897929762 14,54030544 
SAMPLEXXI  0,86139412 27,7376706 
SAMPLEXXII  0,91243849 -2,434070771 
SAMPLEXXIII  0,857465912 36,20548307 
SAMPLEXXIV  0,928416446 -12,92086762 
SAMPLEXXV  0,902045928 12,10271017 
SAMPLEXXVI  0,912488083 4,421866955 
SAMPLEXXVII  0,840212401 34,58592647 
SAMPLEXXVIII  0,839713064 32,53746602 
SAMPLEXXIX  0,909279674 13,14017589 
SAMPLEXXX  0,879661975 14,55266864 
Estimators average 
 
0,89942 
 
9,06608 
 
 
Values pressumed as valid at population level are: 
 
ε + + = bx a y  
a=3,635508(p-value=0,467545619) 
b=0,910912(p-value=3,66124E-58) 
 
Following the analysis of the probability p-value for both parameters one can 
say that, because p-value for the independent element is a is 0, 467545619>5%, 
parameter a is not signfifficantly different from 0. Nevetheless, for the regression 
coefficient, p-value is <5%,  so we can assume that b is significantly different from 0. 
 
After analysing the unbiased property for the independent element a: 
 
a a M = ) ()  
) 635508 , 3 ( 06608 , 9 ) ( = ≠ = a a M )  it is not an unbiased estimator and its bias 
equals the difference  5,430571 ) ( = − a a M ) . 
 
After analysing the unbiased property for the egression coefficient: 
 
b b M = ) (
)
 
0,910912) ( 0,89942 ) ( b b M ≅ =
)
 it is not an unbiased estimator. 
 Econometric modelling for simulating the economic impact of structural reforms in Romania: a pilot project 
 
110 
In conclusion, the linear regression model regarding the dependency of total 
expenditures of total revenues is that the independent element of the model is 
validated as unbiased, whilst the the biased assumption is validated in the case of the 
regression coefficient. 
 
References 
 
Heckman, James J. 2000. Causal Parameters and Policy Analysis in Economics: A 
Twentieth Century Retrospective. Quarterly Journal of Economics 115(1): 45-97. 
D. Miles and J. Mora. 2003. On the performance of nonparametric specification tests in 
regression models. Computational Statistics and Data Analysis, 42(3):477–490.  
A. Amendola, C. Francq and S.J. Koopman. 2006. Special issue on nonlinear 
modelling and  financial econometrics. Computational Statistics and Data Analysis, 
51(4):2115-2117.  
Hoover, Kevin D. and Stephen J. Perez. 2004. Truth and Robustness in Cross 
Country Growth Regressions. Oxford Bulletin of Economics and Statistics 66(5): 765-
798. 

Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective.

On the performance of nonparametric specification tests in regression models.

Special issue on nonlinear modelling and financial econometrics.

Truth and Robustness in Cross Country Growth Regressions.

ECONOMETRIC MODELLING FOR SIMULATING THE ECONOMIC IMPACT OF STRUCTURAL REFORMS IN ROMANIA: A PILOT PROJECT

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ECONOMETRIC MODELLING FOR SIMULATING THE ECONOMIC IMPACT OF STRUCTURAL REFORMS IN ROMANIA: A PILOT PROJECT

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