One of the important indicators that characterize the economy of the country is the business confidence index. It is the basis for tracking the cycles of economic dynamics and analysis of the country's business climate. Evaluation of this indicator makes it possible to predict the crisis phenomena that are occurring in the economy, and to develop possible ways out of difficult situations. On the example of the five countries (Ukraine, Germany, Hungary, Slovenia, Poland) it was also analyzed the possibility of constructing the business confidence index based on economic indicators, which characterize current economic activity of the country. For analysis, the quarterly values of economic indicators over the last years were taken. The selected economic indicators based on crosscorrelation analysis were ranked into three groups: coincident, lagging and leading indicators. Using coincident and leading economic indicators, the several regression models of the business confidence index were built. On the basis of the obtained regression models, the forecast of business confidence index value for the next period is evaluated and the trends of its development are established.
The modern development of the domestic economy is characterized by deepening
international economic relations in connection with the intensification of European
integration processes. This is the reason for the new tasks that confront the national
economy and require solution and coordination with the world methodology of business
management. One such task is the assessment and analysis of the country’s business
climate. The indicator characterizing the business climate in European countries is the
index of business expectations, or as it is called the business confidence index (BCI)
[
The purpose of this paper is to verify whether it is possible to use some of the
economic indicators for calculating the business confidence index. Based on the
selected methods we will try to confirm or reject the hypothesis which say that business
confidence index can be predicted on the base of coincident indicators. The
methodological basis for choosing economic indicators is the recommendations for
calculating the business expectations indicators set out in the Joint Harmonised EU
Programme of Business and Consumer Surveys, which contains the clear definition of
the list of business expectations indicators and the methodology for their calculation
[
Confidence indicators reflect entrepreneurs' perceptions and expectations at the
sector level in the one-dimensional index. They are calculated as the simple arithmetic
average of the balances of answers (in percentage points) to selected questions [
Ukrainian business confidence index is calculated since 2006 and consists of five averages of the balances in the industry, construction, retail and wholesale trade, agriculture, transport and some others (Figure 1).
Business expectations index n o i t i d n o c c i m o n o c e d n a l a i c n a n i F n o i t c u d o r p n w o f o s e l a s f o e m u l o V n o i t c u r t s n o c r o f s t s o c t n e m t s e v n I , y r ien ry ch to rfaom iednnv s n t so tan tc e tseevnm ieuqp
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Fig. 1. The balances of business confidence index in Ukraine.
According to the method of the Organisation for Economic Co-operation and
Development (OECD) composite confidence indicator (business confidence index) is
average of the industrial confidence indicator (ICI), construction confidence indicator
(CCI), retail trade confidence indicator (RCI) and confidence indicator for services
(SCI) [
The retail trade confidence indicator is an average of the balances to the questions in the retail trade survey relating to present business situation, future tendency of the business situation and stocks. The confidence indicator for services is an average of the balances to the questions in the survey relating to the future tendency of employment, present business situation, future tendency of business situation. 2
Construction and Forecast of Business Confidence Index
Cross-correlation is a standard method of estimating the correlation degree of
sequences [
In order to verify if a given economic indicator shown sufficient concurrence with
business confidence index and does not behave as e.g. leading or lagging indicator, we
will apply cross correlations for 12 periods forward and backwards. The first condition
for including economic indicators in the groups of leading, coincident or lagging
indicators is the highest absolute value of correlation coefficient must be at least 0.55
[
We have chosen the secondary data for our analysis, obtained from the National Bank
of Ukraine [
To calculate the index of business expectations, we use the time series of economic indicators that correspond to the components of the index of business expectations. Based on the International System of Leading Indicators in the part of business tendency survey for analysis the following indicators were chosen: producer prices (index points), unemployment rate (% of labour force), Gross Domestic Product (annual growth rate), new orders (index points). The indicators used for the countries have been analyzed and time series are listed in Table 1.
After the application of the selected methods, we got the result of cross correlations for all selected economic indicators for each country. Based on the results, we are able to assess, which indicators behave in line with the business confidence index and which act with a delay or in advance as compared to the business confidence and expectations.
Concerning Ukraine, we have the time series of the selected economic indicators and analyzed their in relation to business confidence index through cross correlations. The results of these correlations are compared in Table 2.
The results of these correlations, with the range of 12 quarters forwards and backwards show that only two economic indicators act with business confidence index of Ukraine. The maximum value of cross correlation between producer prices and business confidence index were achieved in the period of time t – 12 and there absolute value were below 0.55 (0.2119). It means that this indicator does not show any relationship with any Ukrainian business confidence expectations. The result of the cross correlation between unemployment rate and business confidence index was achieved in the period of time t – 1 and there absolute values were above 0.55 (0.721). Consequently, unemployment rate is the leading indicator for business confidence index of Ukraine. The Gross Domestic Product (annual growth rate) is coincident indicator with business confidence index, because the highest absolute value of correlation coefficient is in the period of time t and its meaning 0.8657. This economic indicator is a reflection of the financial and economic state of the country and is very important for the business community of each country.
We have also assessed the relation between the business confidence index and economic indicators of Germany. The results of these correlations are compared in Table 3.
Cross correlations between producer prices and business confidence index and between unemployment rate and business confidence index do not show any relationship insofar as there maximum absolute value were below 0.55 (0.4883 and 0.503 respectively).
But, in the case of Germany we have found that 2 out of 4 tracked indicators report the maximum values of cross correlations above the level of 0.75 showing strong relationship of these indicators with the business confidence index. Such indicators are Gross Domestic Product and new orders. These indicators are coincident with the highest value of correlation coefficients in the period of time t (0.7685 and 0.907 respectively). We can recommend these indicators as an alternative to business confidence index, when it comes to monitoring economic tendency of Germany.
The next country for analysis is Hungary. The results of correlations for this country are compared in Table 4.
0.6181
Cross-correlation analysis revealed that three economic indicators do not affect the business confidence index of Hungary. The absolute value correlations of these economic indicators with business confidence index are below the threshold value of 0.55. They are equal 0.360, 0.476, 0.4297 for producer prices, unemployment rate and new orders respectively. Only Gross Domestic Product is coincidental indicator with business confidence index; maximum absolute value of cross correlation is equal 0.6181.
We have also assessed the relation between the economic indicators and business confidence index of Slovenia. The results of these correlations are compared in Table 5.
Such economic indicators as producer price and unemployment rate are characterized by lack of relations with business confidence index of Slovenia. There maximum absolute value of cross correlations are 0.1644 and 0.415 respectively. The Gross Domestic Product is leading indicator of business confidence index with a lead of 1 quarter and maximum absolute value of cross correlation 0.8425.
The last monitored economy is Poland. The results of correlations for this country are compared in Table 6.
0.8794
Cross-correlation analysis revealed that two economic indicators do not affect the business confidence index of Poland. Such indicators as producer prices and unemployment rate have a maximum absolute value of cross correlation less than the threshold value (0.4894 and 0.435 respectively). The maximum absolute value of cross correlation between new orders and business confidence index is above the threshold value (0.7012), but this indicator is lagging with period of time t + 1. Therefore, this indicator is excluded from further consideration. The Gross Domestic Product is coincident indicator with the business confidence index with high level of correlation (0.8794).
The next step of business confidence index analysis is the construction regression
model with coincident economic indicators and the forecast of the business confidence
index for the next period. In order to forecast economic indicators for the next value in
the next period of time it was used the single exponential smoothing with smoothing
parameter α [
Investigate the applicability of linear and nonlinear (multiplicative) regression
models for forecast of the business confidence index. Linear regression models are
easiest to calibrate and are the most common. Some nonlinear regression models can
be transformed to a linear model by means of some transformation such as
logarithmization of dependent and independent variables. Predicted values can then be
converted to ordinary numbers by taking their antilog or exponential [
For Ukraine time series of data consists of 47 quarterly values (1st quarter 2007 – 3rd quarter 2018). The regression model of business confidence index of Ukraine is BCIUkraine 136,892 2, 729 URUkraine 1, 579 GDPUkraine , (1) where BCIUkraine – business confidence index of Ukraine (index points); URUkraine – Ukrainian unemployment rate (% of labour force); GDPUkraine – Gross Domestic Product (annual growth rate) of Ukraine.
The model is qualitative (R2 = 0.882) and statistically significant (Fcal = 197.9 > Ftable = 3.2 with 95% confidence that there is no significant difference in precision). The mean absolute percentage error (MAPE) of predicted values is 1.4%. Actual and predicted values of business confidence index of Ukraine is presented on Figure 2.
x 150 ed 140 n i ec 130 end 120 i fno 110 ssc 100 isen 90 uB 80 70 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3Q 2 7_002Q _0702Q 0_802Q 0_902Q 0_102Q 10_02Q 1_102Q _2102Q 1_302Q 13_02Q 4_102Q _1502Q 6_102Q 61_02Q 1_702 81_02Q
Time
The forecast value of the business confidence index for the 4th quarter of 2018 is 119.5 and is in confidence interval [92.24; 146.77] with 95% confidence level. The forecast confirms the continued growth of business activity level in the economy of Ukraine.
For Germany time series of data consists of 56 quarterly values (the 1st quarter 2005 – the 4th quarter 2018). The regression model of business confidence index of Germany is
BCIGermany 57, 33 0, 69 GDPGermany 0, 41 NOGermany , (2) where BCIGermany – business confidence index of Germany (index points); GDPGermany – Gross Domestic Product (annual growth rate) of Germany; NOGermany – new orders (index points) of Germany.
The model is qualitative (R2 = 0.787) and statistically significant (Fcal = 81.4 > Ftable = 3.2 with 95% confidence that there is no significant difference in precision). The mean absolute percentage error (MAPE) of predicted values is 5.2%. Actual and predicted values of business confidence index of Germany are presented on Figure 3.
105 edx 100 ien 95 c 90 ifenond 8805 c s s e n i s u B
The forecast value of the business confidence index for the 1st quarter of 2019 is 101.4 and is in confidence interval [91.41; 111.41] with 95% confidence level. The forecast confirms the continuation of the fall in the level of business activity in the economy of Germany.
For the next country – Hungary – time series of data consists of 72 quarterly values (1st quarter 2001 – 4th quarter 2018).The regression model of business confidence index of Hungary is
BCI Hungary 99,19 0, 31 GDPHungary , (3) where BCIHungary – business confidence index of Hungary (index points);
GDPHungary – Gross Domestic Product (annual growth rate) of Hungary. For the constructed model, the approximation accuracy is insufficient (R2 = 0.382) and the model requires improvement. Actual values of business confidence index of Hungary are presented on Figure 4.
For the construction of the index of business expectations in Hungary required an additional analysis of indicators, that affect the expectations of the business environment.
For Slovenia time series of data consists of 76 quarterly values (1st quarter 2000 – 4th quarter 2018). The regression model of business confidence index of Slovenia is 104 edx 110023 iiifssceecendnnon 1100999999875601 su 94 B
104 ex 102 d iifsseececonnnnd 199900864 i su 92 B
Time
The model is qualitative (R2 = 0.701) and statistically significant (Fcal = 173.4 > Ftable = 3.97 with 95% confidence that there is no significant difference in precision). The mean absolute percentage error (MAPE) of predicted values is 0.9%. Actual and predicted values of business confidence index of Slovenia presented on Figure 5.
BCISlovenia 99,123 0, 474 GDPSlovenia , where BCISlovenia – business confidence index of Slovenia (index points); GDPSlovenia – Gross Domestic Product (annual growth rate) of Slovenia. (4)
Time
The forecast value of the business confidence index for the 1st quarter of 2019 is 101.1 and is in confidence interval [97.78; 104.43] with 95% confidence level. The forecast confirms the continuation of the fall in the level of business activity in the economy of Slovenia.
For Poland time series of data consists of 52 quarterly values (the 1st quarter 2006 – the 4th quarter 2018).The regression model of business confidence index of Poland is BCIPoland 98, 283 0, 486 GDPPoland , (5) where BCIPoland – business confidence index of Poland (index points); GDPPoland – Gross Domestic Product (annual growth rate) of Poland.
The model is qualitative (R2 = 0.773) and statistically significant (Fcal = 170.7 > Ftable = 4.03 with 95% confidence that there is no significant difference in precision). The mean absolute percentage error (MAPE) of predicted values is 0.38%. Actual and predicted values of business confidence index of Poland are presented on Figure 6.
103 x 102 e ind 101 ce 100 iedn 99 fon 98 ssc 97 e n i su B 1Q 4Q 3Q 2Q 1Q 4Q 3Q 2Q 1Q 4Q 3Q 2Q 1Q 4Q 3Q 2Q 1Q 4Q _6002 _6002 _7002 8_002 9_002 9_002 _0012 _1012 T2_012ime _2021 3_012 4_012 _5012 _5012 6_120 7_120 _8120 _8120
The forecast value of the business confidence index for the 1st quarter of 2019 is 100.7 and is in confidence interval [98.88; 102.48] with 95% confidence level. The forecast confirms the continuation of the fall in the level of business activity in the economy of Poland. 3
Conclusions As mentioned above, generalized business confidence index is determined on the basis of the survey of respondents in a country regarding their business expectations. This assessment process is quite costly and problematic, as business entities may be more optimistic in their expectations during the survey. This, in turn, will lead to inaccurate information. Thus, the existing approach of determining the business confidence index is imperfect and requires clarification by formalizing the evaluation process. It can be carried out by means of the selection and justification of quantitative socio-economic indicators, on the basis of which the business confidence index will be determined. One more important condition is that the indicators belong to the group of leading, which would allow to establish and recognize crisis phenomena in the economy.
Finally, the business confidence index was improved, based on a system of socioeconomic factors. The absolute value of correlation coefficient the indicators with the business confidence index must be the high. Also the indicators were grouped into three groups, and indicators that belonged to the group of coincident and the group of leading were chosen for estimation and forecasting.
The result of prediction of the business confidence index of Ukraine in the 4th quarter of 2018 will be equal to 119.5 and will increase by 1.97% from the 3rd quarter of 2018. This confirms the continuing optimism of respondents in Ukraine.
Predicted results for Germany, Slovenia an Poland show that the business confidence index for these countries in the 1th quarter of 2019 will be decrease from the 4th quarter of 2018 by 0.57, 0.43 and 0.64 percent respectively. This is due to the uncertainty of business expectations in these countries.
For the prediction of the index of business expectations in Hungary the additional analysis is required. The results of cross-correlation and regression analyzes showed that Gross Domestic Product (annual growth rate) is not the main factor that influences business expectations in this country.