Analysis of the Relationship between the Economic Confidence Index
and Gross Domestic Product Growth in Azerbaijan
ARZU HUSEYNOVA1, OPHELYA MAZANOVA2, SIMUZAR MAMMADOVA3,
SAADAT MAJIDOVA4, AFAQ ASLANOVA3, SAMIRA RUSTAMOVA3
1Department of Digital Economy, Azerbaijan State Economy University, AZERBAIJAN
2E-Management Center, Azerbaijan State Economy University, AZERBAIJAN
3Department of Business Management, Azerbaijan State Economy University, AZERBAIJAN
4Department of Theoretical and Practical Economics, Azerbaijan State Economy University,
AZERBAIJAN
Abstract: - The paper focuses on the economic policy in Azerbaijan directed to eliminate the results of the
Covid-19 pandemic and the return of the country's economy to purposeful structural reforms to ensure
productivity and economic growth gradually. Today, the main goal is to direct economic policy to eliminate the
results of the Covid-19 pandemic. To analyze short-term economic indicators is important to control economic
development in real-time to make operational decisions and to give decision-makers early signals of turning
points in economic activity. Monitoring of processes that occurred in the real sector of the economy is carried
out through questionnaires. The importance of determining the economic indicators required to assess
economic activity in Azerbaijan is considered appropriate. The proposed economic confidence index is
considered reliable and important for analysis, forecasting. Special software provides time sequence
visualization and integration capabilities. The Economic Confidence Index should assess economic activity as
the preventive indicator.
Key-words: - Economic Confidence Index, Gross Domestic Product (GDP), economic shocks, economic
indicators, Business Activity Index of Real Sector, Monitoring
Received: July 12, 2021. Revised: February 19, 2022. Accepted: March 7, 2022. Published: March 18, 2022.
1 Introductıon
The impact of the COVID-19 pandemic, which has
lasted for more than a year, remains alarming
despite the improvement of economic condition due
to vaccination. Development perspectives of the
world economy are characterized by high
uncertainty, primarily related to the subsequent
course of the pandemic. So, the aim of today's
economic policy is to eliminate the results of the
Covid-19 pandemic. Gradually, the country's
economy should return to purposeful structural
reforms ensured productivity and economic growth.
Despite the increase in the number of tools
stimulating the economic activity of market
participants, the problem of confidence has not only
lost its actuality but has also begun to have an
increasing impact on economic development.
Researches are conducted to evaluate economic
activity in the world. The whole activity of most
research institutes is to study changes in the
economy through various indices.
Azerbaijan's economy has not yet been studied
in terms of confidence, and index estimates are still
in their infancy.
One of the main tasks of the analysis and
forecasting of economic activity is the development
of systems for the early detection of changes in the
phases of economic periods based on specially
developed economic indicators. Leading economic
indicators are used to obtain early information about
phase changes and to estimate the moments of phase
change called crucial points within these systems.
Assumed that the turning points of the forecast
indicators are ahead of the turning points of some
key economic indicators (for example, real GDP)
that characterize the economic situation as a whole.
Currently, there exist two main methodological
centers for the development of such indicators:
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DOI: 10.37394/23207.2022.19.75
Arzu Huseynova, Ophelya Mazanova,
Simuzar Mammadova, Saadat Majidova,
Afaq Aslanova, Samira Rustamova
E-ISSN: 2224-2899
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Fig. 1: Methodological centers
Source: Compiled by the authors
EU countries and a number of other countries (more
than 30 countries) are currently using an adapted
methodology to build leading indicators based on a
survey prepared by the OECD Statistics Department
and the European Commission (now OECD-EU
methodology).
The OECD methodology currently serves as a
generally accepted international standard for
constructing economic indicators based on
questionnaire data and its usage provides a
comparison of the procedures for setting and
applying the indicators considered in different
countries. In this methodology, small questionnaires
are answered. The answers allow you to evaluate the
current state of the organization and the expected
changes. The selection of questions focuses on
obtaining the information necessary for the analysis
of changes in the developing economic situation,
both in certain types of economic activity and in the
country's economy as a whole.
The Economic Confidence Index (ECI) is one of
the most important indicators of macroeconomics.
This indicator is similar to the business activity
index. The difference is that economic confidence is
most often associated with business expectations in
the near future.
ECI has powerful forecasting properties.
These forecasting features can be used in two ways:
Fig. 2: Economic Confidence Index
Source: Compiled by the authors
Such an extensive list of uses allows the Economic
Confidence Index (ECI) to be called a reliable and
important tool for analysis and forecasting..
In the presented research we evaluate the
turning points of the business period of the country's
economy and identify the leading nature of the
proposed economic confidence index. Forecasting
possibilities of the economic confidence index are
determined in the autoregression and error
correction models for the monthly and annual
growth rates of the country's real GDP.
2 Methodology
The monthly growth rate of GDP(Gross Domestic
Product) from January 2015 to March 2021 and the
business activity statistics of the real sector provided
by the Central Bank of the Republic of Azerbaijan
(CBA) on monthly surveys [4].
The Economic Confidence Index is calculated
based on the following indices:
Fig. 3: Economic Confidence Index calculat
Source: Compiled by the authors
The consumer confidence index was not used in
the calculations due to a lack of information.
Note that the Institute has been conducting
consumer surveys among consumers on the
calculation of the consumer confidence index since
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Arzu Huseynova, Ophelya Mazanova,
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Afaq Aslanova, Samira Rustamova
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May 2021 and it will be used in the calculation of
the Economic Confidence Index in the coming
months.
Preliminary data processing on the methodology
of calculation and application of confidence indices
and economic confidence index covers the
following main stages:
Fig. 4: Stages of calculation economic confidence
index
As mentioned above, the preparation and processing
of the questionnaires specified in the first stage of
the calculation of confidence indices are carried out
by the Central Bank of the Republic of Azerbaijan
on a monthly basis and published on the official
website. Therefore, that information was obtained
from there.
Objectives set in the adapting process of this
methodology to the data of the Azerbaijani
economy:
1.Selection and optimization of application
conditions for statistical analysis of time series
used, including seasonal smoothing, selection of
periods, algorithms for calculating the composite
lead index.
2.Study of economic aspects of selection and
calculation of key economic indicators, as well as
determination of the predictive nature of the
calculated economic confidence (table) index and
assessment of turning points of business periods
of the economy in Azerbaijan.
3.Assessing the potential of econometric as well as
predictive indicators of the economic confidence
index of the dependence of real GDP growth rates
on the economic confidence index (ECI) and
establish analysis models for turning points of
business cycles
The Azerbaijan Institute for Scientific Research
on Economic Reforms has calculated the
Entrepreneurial Confidence Index in 2020 through
quarterly surveys, considering the OECD
methodology and international experience in this
field. The index was compared with the quarterly
growth rate of GDP.
3 Confidence Index and Calculation
Methods of Composite Economic
Confidence Index
Direct or indirect calculation methods can be used to
calculate the ECI.
The indirect method in the calculation of ECI. Via
the indirect method, the confidence indices are
calculated for seasonally adjusted time group series
of response balances in the form of geometric
averages for each economic activity. In this case, the
calculation of the ECI index assumes the following:
Fig. 5: Calculation of the ECI index
For calculating the ECI, in contrast to the indirect
method the direct method, provides the use of
standardized time series of response balances
without seasonal adjustment. In this case, the
calculate of seasonal unregulated confidence indices
and the composite index of ECI is possible [4].
In the study, a special weight of industry,
construction, retail, and services in GDP was
adopted for the observed period:
Fig. 6: Services in GDP
Thus, according to the OECD(Organisation for
Economic Co-operation and Development)
methodology, the calculation of the ECI consists of
three stages:
1. Standardization of time series of response
balances:
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Arzu Huseynova, Ophelya Mazanova,
Simuzar Mammadova, Saadat Majidova,
Afaq Aslanova, Samira Rustamova
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where,
2. Calculation of the average weight of the standard
response balances:
where – is the weighted ratio of the answers to
the survey questions. The sum of these ratios is
equal to Weight ratios ( ) are calculated based on
statistics for the considered period. According to the
methodology, for the calculation of weight ratios,
the usage of the special weight of key economic
sectors in GDP is recommended [7].
3. Standardization and scale of the composite
confidence index of economic is carried out
according to the following equations:
where,
In the used measurement versions, the values of ECI
vary mainly in the range from 90 to 110.
Fig. 7: Dynamics of gross domestic product.
Source: the State Statistics Committee (SSC)
We assume that the significance of the ECI at the
level of 100 units is consistent with the long-term
trend. Exceeding this level, ie a positive deviation
from the long-term trend is interpreted as economic
growth, and a value below 100 indicates a negative
deviation from the trend and a deterioration of the
economic situation. An indirect method was used
to calculate the composite ECI for Azerbaijan [3].
Before assessment of the Economic Confidence
Index, first, we should observe the dynamics of
GDP from 2015 to the current period, as well as the
dynamics of the industry, construction, retail,
service confidence indices provided by the Central
Bank of the Republic of Azerbaijan.
As seen from Fig.7, the dynamics of
GDP(Gross Domestic Product) by months are
positive, excluding local increases and decreases.
However, in the period when the COVID-19
pandemic began to spread in the Azerbaijan,
business activity decreased due to the restrictive
measures applied by the Operational Headquarters
under the Cabinet of Ministers in the country, which
manifested itself in monthly reductions in GDP.
However, the need to ease social restrictions is
already accelerating the recovery process in the
economy.
Fig. 8: Business activity indices in real sector.
Source: Central Bank of the Republic of Azerbaijan
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Fig.8 shows the confidence indices prepared on the
basis of the Central Bank's survey on business
activity in the real sector of the economy.
Fig. 9: ECI time series and the seasonal adjusted
ECI time series
Fig. 9 shows the diagram of the initial ECI time
series and the seasonal adjusted ECI time series for
the period from January 2015 to June 2021 for
comparison.
The seasonal adjustment was performed using the
Hodrick-Prescott (HP) statistical filter.
4 Methods for Obtaining the Cyclic
Component if ECI
When using the indirect method to construct the
ECI, the use of two methods for the subsequent
statistical processing of the time series of the ECI is
recommended to obtain its cyclic component:
1. Double use of the Hodrick-Prescott filter with
parameter values for the first
stage and for the second stage,
analogous to the key economic indicator;
2. Single application of Hodrik-Prescott filter with
parameter value .
The first method assumes the ECI has a long-term
trend over time. The second method is based on the
assumption that the ECI series (in economic terms)
is stationary.
Therefore, the problem of choosing the processing
method can be selected depending on the type of
probable model.
If the time sequence of the ECI is determined, there
is no need to eliminate the trend, and it is sufficient
to eliminate the high-frequency "noise component"
using the Hodrick-Prescott filter with the
parameter value,
This key economic indicator allows a smoother and
more convenient cycle component to identify
important components that can be used in the
analysis cooperatively with a sound business cycle.
Otherwise, the use of a two-stage isolation
procedure is recommended.
Also, known that structural changes and short time
series make it difficult to determine the type of time
series model using statistical tests known as “single
root” tests. In such a situation, the test results should
be analyzed economically to substantiate and
interpret the moments of structural change.
The non-stationary timing of the ECI may be due to
structural changes due to shocks in the economy, as
well as the inability of respondents to distinguish
between market fluctuations and structural changes.
Therefore, the structural fluctuations in the economy
through the responses of the respondents are
reflected in the time series of the ECI [3].
In other words, at certain stages, the ECI may
contain a trend component and may not be
stationary.
Fig. 10: The effect of a trend reversal on the
dynamics of the ECI
Fig.10 shows the comparative results of the time
series of the normalized ECI with the above values
of the filter parameter. ECI-1 is a cyclic component
of the ECI after two staged applications of the
Hodrick-Prescott (HP) filter, and ECI-2 is an ECI
regulated by a single application of the Hodrick-
Prescott filter.
Fig.10 shows that the dynamic characteristics of the
time series ECI-1 and ECI-2 are different.
The application of a two-staged HP filter has
significantly smoothed the ECI-1 index, bringing it
to an almost stable straight line after 2018.
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Fig.11 Time series obtained by the two-staged
method for GDP and ECI cycles
Fig. 11 shows the two-staged application of the
Hodrick-Prescott filter for GDP and ECI-1, and the
single ( ) application for ECI-2.
Table 1. Turning points of ECI and GDP
Types of
turning
points
Year/mon.
ECI
GDP
botton
03.2015
90,4495
99,6852
peak
07.2015
119,413
101,58
botton
01.2016
82,1907
98,4275
peak
07.2016
111,124
101,032
botton
12.2016
82,0086
99,5126
peak
07.2017
111,575
100,364
botton
09.2017
89,5044
99,5656
peak
05.2018
117,221
101,426
botton
10.2018
93,2355
99,8738
peak
01.2020
117,182
101,402
botton
05.2020
79,7208
97,741
peak
11.2020
108,507
100,863
Fig. 12: ECI and GDP turning points
This year the physical volume index of GDP has
increased compared to February-March. The
increase of confidence indices in these sectors
during this period led to an increase in the
Economic Confidence Index by 3.5%. In general,
the significant easing of the special quarantine
regime connected with the pandemic, the growing
popularity of the vaccination process in the country
has led to the revival of economic life.
Although the volume of industrial productions
increased by 12.2% in nominal terms in March
compared to February of the current year, in
January-March it decreased by 4.6% in real terms
compared to the same period of the previous year.
In the first quarter of 2021, the volume of work and
services in the construction sector increased by
4.8% compared to the same period in 2020. In the
same period, retail trade turnover decreased by
1.1%.
Due to the short duration of surveys conducted by
the Institute for Scientific Research on Economic
Reforms (surveys started in the first quarter of 2020)
and the small number of respondents, the Economic
Confidence Index was calculated based on the
European Union methodology based on monthly
confidence indices calculated by the Central Bank of
Azerbaijan since 2015.
Periods were determined by conducting seasonal
leveling on GDP and Economic Confidence Index.
ECI peak points ahead of GDP growth about 2-4
months, bottom points 1-4 months ahead.
Periods are determined by applying seasonal
smoothing to the Industrial Production Index and
the Industrial Confidence Index.
VAR Model:
GDP_ = 0,37 ECI_(-1) - 0,91 ECI_(-2) +
0,91 ECI_ (-3) - 0,37 ECI_ (-4) + 2,79 GDP_(-1)
- 2,95 GDP_(-2) + 1,30 GDP_ (-3) - 0,14 GDP_(-
4) + 0,87
ECI_ = 3,07 ECI_ (-1) - 3,76 ECI_ (-2) +
2,13 ECI_ (-3) - 0,46 ECI_ (-4) + 0,15 GDP_ (-1)
- 0,38 GDP_ (-2) + 0,356 GDP_ (-3) - 0,12 GDP_
(-4) + 1,50
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Fig. 13: Shows the radar diagram of business
activity indices for 2021.
The diagram shows an improvement in the
decentralization rate. From a sectoral perspective,
confidence in construction and industry increased in
January 2021. Overall, economic confidence was
above average in all sectors during this period. In
February, confidence levels were low in all sectors
except the service sector. In March, confidence
remained below average in the industry, although
confidence increased in construction, services, and
retail trade.
5 Economic Confidence İndex And
GDP Forecasting
The following diagrams show the ECI and GDP
forecasts for June 2022 based on the vector
autoregression (VAR) model.
Fig. 14: Gross Domestic Product forecast
Fig. 15: Economic Confidence Index Forecast
Table 2. Turning points of ECI and GDP forecast
ECI
GDP
peak
bottom
peak
bottom
2015M01
2015M01
2015M07
2016M01
2015M07
2016M02
2016M07
2017M01
2016M09
2017M02
2017M07
2017M11
2017M11
2018M01
2018M05
2018M12
2018M09
2019M03
2019M07
2020M06
2019M10
2020M05
2021M01
2021M03
Comment: The table understands 2015M01 -
2015Month January.
6 Conclusıon
The presented work has great importance in
studying the effects of the COVID-19 pandemic on
the business activities of production and service
enterprises. The Economic Reforms Research
Institute of the Ministry of Economy of the Republic
of Azerbaijan initially developed survey forms
characterizing the development of the real sector.
During the preparation of the surveys, the
experience of different countries in this area was
studied, as well as methodological tools developed
by the Organization for Economic Cooperation and
Development (OECD) in this area were used. The
developed surveys were further improved
considering the opinions and suggestions of the
relevant structural units and agencies of the Ministry
of Economy and Industry of Azerbaijan.
The composite index of economic confidence
index was calculated based on the economic sector
according to the “Business Activity Indices in the
Real Sector” database formed on the basis of
surveys conducted by use of the Conjuncture
Surveys of the Central Bank of the Republic of
Azerbaijan for the observation period from January
2015 to March 2021, as the initial statistical base of
the study;
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Seasonal smoothing on confidence indices,
economic confidence index, and real GDP by
economic sectors was conducted.
The predictive nature of the calculated economic
confidence index has been identified and the turning
points of the business cycle of the Azerbaijani
economy have been evaluated;
The dependence of real GDP growth rates on the
Economic Confidence Index (ECI) has been
econometrically assessed and business cycle turning
point analysis models have been developed, and
forecasts for the period up to March 2022 have been
prepared based on the VAR (vector autoregression)
model.
We observed that the turning points of the ECI and
real GDP cycles coincide. So, the economic
confidence index can be used to prepare short-term
forecasts of economic growth rates.
ECI is predictive. Moreover, these predictive
features can be used in two ways:
1.ECI can be considered as a leading indicator in
terms of economic activity. In this context,
information is provided on the expected changes in
economic activity and the forthcoming phase of
business cycles.
2.ECI can be used as an explanatory variable for
GDP growth rate in relevant forecasting models.
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DOI: 10.37394/23207.2022.19.75
Arzu Huseynova, Ophelya Mazanova,
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Afaq Aslanova, Samira Rustamova
E-ISSN: 2224-2899
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Volume 19, 2022
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Arzu Hueynova, Ophelya Mazanova carried out the
simulation and the optimization.
Simuzar Mammadova, Saadat Majidova has
organized Section 1
Afaq Aslanova, has organized Section 4
Samira Rustamova. was responsible for the
Statistics.
Sources of Funding for Research Presented
in a Scientific Article or Scientific Article
Itself
Paid for only by the authors.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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