JOINT EUROPEAN COMMISSION – OECD WORKSHOP ON INTERNATIONAL DEVELOPMENT OF BUSINESS AND CONSUMER TENDENCY SURVEYS
BRUSSELS
14 – 15 NOVEMBER 2005
Building up a Real Sector Business Confidence Index for Turkey
Dilara Ece, Türknur Hamsici, Ece Oral
Central Bank of the Republic of Turkey Research and Monetary Policy Department
- November 2005 -
Building up a Real Sector Business Confidence Index for Turkey
Dilara Ece, Türknur Hamsici, Ece Oral *
Central Bank of the Republic of Turkey Research and Monetary Policy Department
[email protected] [email protected]
Abstract The aim of this study is two-fold; the first one is to receive valuable insight into the Business Tendency Survey (BTS) of the Central Bank of the Republic of Turkey (CBRT) and the second one is to construct a real sector confidence index by using the questions of the BTS. The most important motivation behind constructing a real sector confidence index is to provide an indicator of short-term business conditions for economic policy makers and business managers by surveying business managers’ views on general business conditions and their future anticipations. According to statistical criteria and economic theory, the most appropriate index is formed and its performance in tracking the cyclical features of industrial production index is tested in this study.
JEL Classification: C42, E32, C19
Keywords: Business tendency survey; Industrial production index; Cross-correlation; Principal component.
* The views expressed in this paper are those of the authors and do not necessarily correspond to the views of the Central Bank of the Republic of Turkey.
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TABLE OF CONTENTS
1. INTRODUCTION........................................................................................................................... 1
2. BUSINESS TENDENCY SURVEYS OF TURKEY.........................................................………. 2
2. 1. Description of the Surveys...................................................................................................... 2
2. 2. Business Tendency Survey of the Central Bank of Turkey (BTS).......................................... 3
2. 2. 1. Sample Design............................................................................................................. 3
2. 2. 2. Profile of the Participants............................................................................................ 3
2. 2. 3. Questionnaire Design.................................................................................................. 5
2. 2. 4. Response Rate............................................................................................................. 6
2. 2. 5. Timing and Follow Up Mechanism............................................................................. 6
2. 2. 6. Reliability of the Survey.............................................................................................. 6
2. 2. 7. Statistical Interpretations............................................................................................. 6
2. 2. 8. Dissemination of the Survey Results........................................................................... 7
2. 2. 9. Comprehensive Inquiry of the Survey Questions........................................................ 7
3. REAL SECTOR BUSINESS CONFIDENCE INDEX.................................................................…9
3. 1. What Does Confidence Mean?.............................................................................................….9
3. 2. BTS Questions Related to Confidence ...............................................................……………10
4. METHODOLOGY........................................................................................................................ 10
4. 1. Diffusion Indices................................................................................................................... 10
4. 2. Selection of the Potential Cyclical Series............................................................................. 11
4. 2. 1. Standardization.......................................................................................................... 12
4. 2. 2. Seasonality................................................................................................................. 12
4. 2. 3. Cross-Correlation Analysis........................................................................................ 13
4. 2. 4. Peak-Trough Analysis............................................................................................... 13
4. 2. 5. Low Volatility........................................................................................................... 13
4. 2. 6. Economic Significance.............................................................................................. 13
4. 2. 7. Weighting.................................................................................................................. 13
4. 2. 8. The Interpretation of the Real Sector Confidence Index and the Points to be
Highlighted................................................................................................................ 14
5. THE EMPIRICAL RESULTS....................................................................................................... 14
5. 1. Reference Series..................................................................................................................... 14
5. 2. Standardization...................................................................................................................... 15
5. 3. Seasonality............................................................................................................................. 15
5. 4. Selection of the Series............................................................................................................ 16
5. 5. Weighting............................................................................................................................... 21
5. 6. Additional Remarks............................................................................................................... 21
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6. PERFORMANCE OF THE REAL SECTOR CONFIDENCE INDEX (MBRKGE) AND ITS
COMPONENTS ........................................................................................................................... 23
7. THE SECTORAL INDICES......................................................................................................... 29
8. CONCLUSION.............................................................................................................................. 31
REFERENCES..................................................................................................................................... 33
APPENDIX
A1. Questions of the BTS.................................................................................................................... 35
A2. Original Questionnaire................................................................................................................. 38
B. Stages of the Production.............................................................................................................. 42
C. Data Description.......................................................................................................................... 43
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1. INTRODUCTION
There is a growing concern among countries in the short-term economic indicators to monitor the
economic developments and provide the economic analysts with the early signals of the turning
points in the economic activity. Such indicators are used to help both the government and the
private sector decision-makers check their performance and plan their actions. Recently, countries
are trying to improve their indicators system by including indexes from the surveys.
The surveys on expectations are primarily designed to signal changes in economic activity and
widely used in macroeconomic assessments and forecasts. The advantage of using survey results is
that they are available promptly before the related quantitative measures covering the same types
of economic activity and hence, they are considered as complementary to the official statistics. The
main aim of the business tendency surveys conducted in various ways is to find out the general
tendency of the cyclical developments and provide economic decision-makers with the necessary
information about future expectations.
BTS of the CBRT has been conducted since December 1987. It has been prepared with the aim of
discovering the opinions of the senior managers of the major private sector firms about the recent
past and the future, on production, demand, investment, sales, employment, capacity utilization of
their company and their inflation expectations.
It is generally difficult to follow all the questions in a survey. Nilsson (2000) states, “The reason
why a group of indicators combined into a composite indicator should be more reliable over a
period of time than any of its individual components is related to the nature and causes of business
cycles”. Thus, the responses given to different questions are evaluated collectively by summing
them up into a single indicator. The aggregated indicator, which is a function of respondents’
current and past evaluations, and future expectations, is called “confidence indicator”.
The first study by Candemir and Karabudak (1994) using the BTS searched for a Business
Confidence Index and a separate Investment Confidence Index for Turkey. They constructed a
monthly composite index of business confidence, based on the cross-correlation analysis, by
utilizing the survey series related to business outlook, domestic deliveries, domestic new orders
received and expected investment expenditures.
This paper aims to form an indicator of short-term business conditions using information obtained
from the BTS based on the statistical point of view. We mostly focus on the cyclical performance
of the survey indicators and develop a real sector confidence index as an early warning indicator
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for economic conditions in Turkey. Since the BTS is similar to the surveys in the OECD member
countries, the methodology behind our confidence index is mostly based on the idea from the
OECD studies (Nilsson, 1999). So, our confidence index can be comparable with the ones in
OECD, EU and the transition countries.
The paper is organized as follows. After this introduction, we discuss all business tendency
surveys of Turkey with particular interest in the BTS in Section 2. The interpretation of the
confidence is discussed in Section 3. Section 4 introduces the methodology. Section 5 presents the
results obtained applying the methodology introduced in Section 4. The effectiveness of the BTS
data and the performance of the confidence index are discussed in Section 6. Section 7 gives a
brief discussion of confidence indices constructed on sectoral level. Finally, the main conclusions
of the study are drawn in Section 8.
2. BUSINESS TENDENCY SURVEYS OF TURKEY
2.1. Description of the Surveys
The CBRT and the Prime Ministry State Institute of Statistics (SIS) are the two state institutions
carrying out business tendency surveys in Turkey. Besides these state institutions, Istanbul
Chamber of Industry (ICI) conducts the “Economic Situation Assessment Survey”, which is
similar to the BTS, on its member companies twice a year.
The “Quarterly Manufacturing Industry Tendency Survey” of the SIS has been conducted for
twenty-six years with nearly three thousand and five hundred firms from public and private sectors
answering the survey questionnaire regularly. The survey reflects the views of the firms in the
manufacturing industry on production, sales, stocks, capacity utilization and prices. The second
survey conducted by the SIS is the “SIS Monthly Manufacturing Industry Tendency Survey”.
Participants to this survey, which was started in February 1991, consist of one thousand and two
hundred panelists. The monthly survey is a smaller version of the Quarterly SIS Survey.
Significant and continuous backward revisions are applied for both surveys.
The “Economic Situation Assessment Survey” of the ICI covers the firms from the private
manufacturing industry. It has been conducted twice a year. The semiannual survey belonging to
the first half of 2003 had approximately 501 respondents, which correspond to 6-10 percent of the
ICI member firms. The survey questions are mainly concerned with production, employment,
domestic sales, financing and international trade.
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Finally, the BTS of the CBRT is a monthly survey, which is intended to find out the assessments
and the expectations of the senior managers of the major firms in general and on sectoral basis, by
asking questions about the past and the future. The BTS, which was firstly launched as a pilot
study in September 1987, has been carried out since December 1987. The survey, covering various
aspects of the economy, is quite comprehensive. Detailed information about the BTS is given in
the following section.
2.2 Business Tendency Survey of the Central Bank of Turkey (BTS)
2.2.1. Sample Design
The survey is designed to communicate with the senior managers, who have accepted to be a
respondent of the survey, from the industrial enterprises that are ranked1 among the “First 500
Industrial Enterprises of Turkey” and the “Next 500 Major Industrial Enterprises of Turkey” lists
prepared by the ICI. The participants comprise the firms from the private companies operating in
the manufacturing sector. Sampling method adopted for the BTS is non-probabilistic purposive
sampling method. The sample is revised once a year as to cover the new companies included in the
ICI's latest biggest firms ranking, and kept fixed thereafter until the next revision.
2.2.2. Profile of the Participants
Besides the BTS, a questionnaire by which the profile of the participants is sought is regularly sent
once a year. This survey comprises questions on the situation of firms’ output, number of
employees, prepaid capital, exports, net sales and stocks. For the profile analysis of 2002, the
questionnaires were mailed to 846 firms. As of the results of 2002, 479 firms have responded to
the profile survey.
The size classification of the firms is made on the basis of the net sales by utilizing the net sales
criterion of BACH2 (The Bank of Harmonized Data on Company Accounts). Aggregate net sales
of the private companies within size categories are presented in Figure 1.
1 The enterprises are ranked according to the sales from production criteria. 2 According to BACH criterion, the firms with net sales less than or equal to EUR 7 millions are grouped in small firms while the firms with net sales between EUR 7-40 millions and the firms with net sales higher than EUR 40 millions are grouped in medium and large firms, respectively. The annual average of the Central Bank buying rate of EUR is utilized in the conversions.
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Figure 1: Aggregate Net Sales by Size Classes Figure 2: Percentages of Firms Across Size
0
5000
10000
15000
20000
25000
30000
Small SizedFirms
MediumSized Firms
Large SizedFirms
Net
Sal
es
(T
rilli
on T
L)
The breakdown of the firms by size is presented in Figure 2. The survey revealed that half of the
respondents are medium scaled. Although 78 percent of the aggregate net sales belong to large
sized firms, the percentage of the large sized firms is 32 percent.
Figure 3: Ratio of Exporting Firms
The ratios of the exporting firms across size classes are given in Figure 3. From the Figure 3 it can
be seen that the ratios of exporting firms do not significantly differ across size. Approximately 93
percent of the large sized firms and 79 percent of the small sized firms are exporting firms. Out of
the total sales, the share of domestic sales is 62 percent while the share of exports is 38 percent.
The economic sectors are classified according to the ICI’s sectoral breakdown in Figure 4 and 5.
The survey covers most of the sectors of the economic activities, namely, mining, food, textiles,
forestry, paper products, chemicals, stone, metals, machinery and energy. The sectoral breakdown
of the respondent firms in Figure 5 shows that machine-vehicle, textile, food and chemistry sectors
make up approximately 75 percent of the entire participation in the BTS. Services sector, which
has a share of 6.5 percent in GNP, is not represented in the survey.
0
20
40
60
80
100
Small Sized Firms Medium Sized Firms Large Sized Firms
78.9%
89.2% 92.8%
52%
32%
16%
Small Sized Firms Medium Sized Firms
Large Sized Firms
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Figure 4: Sectoral Breakdown of the Figure 5: Sectoral Breakdown of the
Participants (846 firms) Respondents (429 firms)
2.2.3. Questionnaire Design
The questionnaire contains 34 questions with different structures. The first 23 questions are
presented in a 3-point Likert scale format listed by “more optimistic – the same – more
pessimistic”, “more – the same – less”, “ more than adequate – adequate – less than adequate”,
“higher – the same – lower”, “above normal – normal – below normal”, “up – the same – down”.
Although the first 11 questions search either one of past, present or future information, the
questions between 12 and 23 contain information on both the recent trends and expectations
explicitly. They are in the form of “up – the same – down” to follow the trend over the past three
months and the expected trend over the next three months. Question 24 is a multiple-choice
question. Questions between 25 and 28 are about ranking some factors.
The BTS has been adopted from the Industrial Trends Survey of the Confederation of British
Industry (CBI). Then, question 29, which is a qualitative question about the expectation on
wholesale prices (WPI) inflation, was added in 1997. Later, in 1999 and 2000, four quantitative
questions (30 - 34) were added on WPI inflation and loan interest rate. Meanwhile, the CBI survey
was also revised by adding three more questions, which have not been included in the BTS. The
questions of the BTS are about: General business situation in their industry, export prospects,
capital expenditures, level of output below capacity, orders (total, domestic, export), stocks of
finished goods and raw materials, employment, volume of output, unit cost, prices, production
schedule, limits on output and export orders, wholesale inflation rates and loan interest rates. The
questions and the questionnaire can be found in Appendix A1 and A2, respectively.
TEXTILE24%
PAPER4%
STONE and SOIL8%
METAL7%
ENERGY1%
FOOD17%
MINING1%
FORESTRY2%
MACHINERY 22%
CHEMISTRY14%
FORESTRY2%
MINING1% FOOD
15%
ENERGY1%
METAL7%
STONE and SOIL8%
CHEMISTRY14%
PAPER4%
TEXTILE26%
MACHINERY 22%
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2.2.4. Response Rate
The number of respondents, which was 256 in 1987, has currently reached 400 firms and the
response rate is generally close to 50 percent. By the end of October 2004, the survey has been
sent to 850 firms and the response rate has been 51 percent.
2.2.5. Timing and Follow up Mechanism
The BTS is mainly conducted by means of mailed questionnaires. However, twenty percent of the
responses have been collected via electronic mail since January 2002. The timing of the survey has
been modified from time to time and the time schedule after the last revision is as follows: For the
current month, questionnaires are sent together with an introductory letter in the last week of
previous month. The firms are asked to return the completed form by the 2nd of the following
month. The firms are reminded via telephone or e-mail in advance. Before January 2004, each
month the results of the previous period were revised for the additional responses received after the
announcement of the overall results. However, no revision was made for the responses received
after 15th of the month following the month of the survey. There has been no revision as of January
of 2004. Questionnaires are to be filled up by senior managers. Returns are subject to editing and
validation.
2.2.6. Reliability of the Survey
The Cronbach α coefficient based on the first 23 questions of the BTS was calculated as 0.78 by
Dengiz & Ozcan (1991). The reliability of the BTS has been recalculated on the data of 523 firms
of the private sector in February 2002 (Oral, 2002). At this time, in addition to the first 23
questions, 29th and 32nd questions are used for the reliability analysis. The answers of the questions
have the formation as qualitative and ordinal choices. So Likert scale is used (Dengiz & Ozcan,
1991; Moser & Kalton, 1972). The scaling is done by giving the biggest score to the most
optimistic answer and the smallest to the most pessimistic answer. Cronbach α coefficient is used
for measuring the reliability. When the coefficient is between 0 and 0.40, 0.40 and 0.60, 0.60 and
0.80, 0.80 and 1, the test is not reliable, less reliable, quite reliable and highly reliable, respectively
(Ozdamar, 1997). Oral (2002) has calculated the Cronbach α coefficient as 0.8166, which points to
a high consistency.
2.2.7. Statistical Interpretations
In the monthly report of the aggregated results, the answers of multiple-choice questions are
presented in percentages tables. Trends and expectations are identified by net balance method. Net
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balance is calculated as the differences between the percentages of the respondents giving positive
(optimistic, up, above normal) and negative (pessimistic, down, below normal) replies. For the
data pertaining to inflation and loan interest rate expectations, descriptive statistics, which include
the number of observations, minimum and maximum values, standard deviation, arithmetic mean,
median, mode and appropriate mean are reported. The appropriate mean is calculated through
comparing arithmetic mean, median, mode, alpha-trimmed mean and by doing outlier and extreme
value analysis. The first part of the monthly report consists of the participation table and summary
of the monthly developments. The second part consists of the tables and graphs of the percentages
and the balances for each question.
2.2.8. Dissemination of the Survey Results
The monthly report is disseminated to the public through web site
http://www.tcmb.gov.tr/yeni/eng/index.html. The survey results are sent to all participants.
2.2.9. Comprehensive Inquiry of the Survey Questions
With the purpose of receiving valuable insights about the BTS content and making correct
interpretation, the relation between questions is explored. The correlation analysis of domestic
demand related questions are shown in Table 1 (last three months trend) and Table 2 (next three
months trend) and foreign demand related questions in Table 3 (last three months trend) and Table
4 (next three months trend).
Table 1-2-3-4: Results of the Correlation Analysis
Table 1: Trend of the Last Three Months Table 2: Trend of the Next Three Months
New
domestic orders (d13-2)
Production (d15-2)
Goods sold
(d16-2)
Work in progress (d19-2)
New domestic orders (d13-2)
1.00 0.97 0.99 0.97
Production (d15-2)
0.97 1.00 0.96 0.98
Goods sold (d16-2)
0.99 0.96 1.00 0.96
Work in progress (d19-2)
0.97 0.98 0.96 1.00
New domestic orders (d13-1)
Production (d15-1)
Goods sold
(d16-1)
Work in progress (d19-1)
New domestic orders (d13-1)
1 0.98 0.98 0.98
Production (d15-1)
0.98 1 0.98 0.98
Goods sold (d16-1)
0.98 0.98 1 0.98
Work in progress (d19-1)
0.98 0.98 0.98 1
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Table 3*: Trend of the Last Three Months Table 4: Trend of the Next Three Months
*Note that d10 shows this month’s trend
For both, past and future questions related to the groups of domestic and foreign demand, there
exist significantly high correlations among the questions within each group. High correlations in
Table 1 indicate that the questions related to the developments at the consecutive stages of the
production process over a three-month period are equally well informative about the direction of
the changes in the economic activity. The managers seem to preserve the same structure in their
projections for the same variables over the next three months period (Table 2).
Similarly, high correlations between the export related questions (Table 3 and 4) might reflect the
opinions of the exporters about the market conditions rather than reflecting the firm's specific
events. As a final result of the analysis, Tables 1 to 4 may show that the respondents cannot make a
clear distinction between the questions on similar subjects.
Although the survey offers a wealth of information, there is also another point to be highlighted
about the information content of the BTS. Table 5 shows that there exists a high correlation
between the last and the next three months trends for most of the questions. In other words,
answers to the BTS questions on the expectations about the future developments are largely
influenced by the last three months' trends.
Export orders (d10)
New export orders (d14-1)
Exported Goods (d17-1)
Export orders (d10)
1.00 0.92 0.89
New export orders (d14-1)
0.92 1.00 0.96
Volume of exported
Goods (d17-1)
0.89 0.96 1.00
Export opportunities
(d2)
New export
orders
(d14-2)
Exported Goods (d17-2)
Export opportunities
(d2)
1.00 0.81 0.80
New export orders (d14-2)
0.81 1.00 0.97
Volume of exported
Goods (d17-2)
0.80 0.97 1.00
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Table 5: Correlations Between the Last and the Next Three Months' Trends
Employment (d12-1, d12-2) 0.84
New orders from domestic market (d13-1, d13-2) 0.71
New orders from export market (d14-1, d14-2) 0.78
Production (d15-1, d15-2) 0.69
Goods sold in domestic market (d16-1, d16-2) 0.67
Exported goods (d17-1, d17-2) 0.72
Raw- material stocks (d18-1, d18-2) 0.59
Work in process (d19-1, d19-2) 0.70
Stocks of finished goods (d20-1, d20-2) 0.69
Average unit cost (d21-1, d21-2) 0.91
Average price for new domestic orders (d22-1, d22-2) 0.92
Average price for new export orders (d23-1, d23-2) 0.89
Another important point which must be taken into account in the analysis of the results is the
“Bias”, appeared as permanent optimism or permanent pessimism. Whereas the respondents are
systematically pessimistic (except for a few months) in answering questions related with monthly
developments of the financial requirement, past-due receivables, amount of the stocks of finished
goods, total amount of orders, amount of export orders and three-month averages of the unit costs
and prices for new orders received from the domestic market, they are systematically optimistic for
the productive capacity over the next twelve months, as well. However, the persistency in
optimism or pessimism can be corrected by comparing the gap between the diffusion indices and
their long-term averages.
3. REAL SECTOR BUSINESS CONFIDENCE INDEX
3.1. What Does Confidence Mean?
Pellissier (2002) explains confidence “Theoretically and in the economic sense of the word,
business confidence can be described as the degree of sentiment towards risk taking by business
for whatever reason. The reaction of business people to their economic environment can thus be
interpreted as being a function of their perceptions and evaluations of current business conditions
and expectations of future eventualities. The level of these two psychological identities of
perceptions and expectations impacts directly on the human nature behavior of business people
and action taken by business can to a large degree be ascribed to the level of business
confidence”.
Business tendency surveys provide the necessary data for the measurement of the business
confidence. Qualitative and ordinal choice structure of these surveys makes the responses sensitive
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to the cyclical developments (OECD, 2003). In other words, business tendency surveys can give
knowledge about future business cycles as contraction or expansion in the economic activity. The
indicator constructed as a combination of a set of survey questions in a single composite index is
called confidence indicator because it sums up economic agents’ assessments and expectations of
the economic situation.
There are numerous theoretical and applied researches in the literature investigating the effect of
confidence upon economic activity. As an example for theoretical research, Yew-Kuang (1992)
examined whether the business confidence could lead a recession. Bodo et al. (2000) analyzing the
forecasting performance of the business confidence using time series techniques such as ARIMA
and cointegrated VAR can be an example for empirical research.
3.2. BTS Questions Related to Confidence
For constructing a real sector business confidence index for Turkey, the survey questions
providing qualitative information on the current situation as well as on the expectations for the
next three months are used. The most important variables are production, employment, new orders,
sales prices, investment plans and limits to production. The variables related to the index are the
ones which measure an early stage of production (e.g. new orders), respond rapidly to changes in
economic activity (e.g. stocks), measure expectations or draw a picture of overall business
condition (e.g. general business condition), measure improvement in economic conditions (e.g.
investment expenditures) (OECD, 2003).
4. METHODOLOGY
The methodology used for the construction of the confidence indicator in this study is the same as
the one derived in OECD (Nilsson, 1999). The basic steps of this method, which are data
preparation, choice of the reference variable and selection of the potential indicators, are explained
in the following sections.
4.1. Diffusion Indices
The results of the business tendency surveys can be reported by balances and diffusion indices.
The balance is the difference between the percentage of respondents answering “up” or “more
optimistic” to each question minus the percentage replying “down” or “more pessimistic”. A
diffusion index is just an alternative approach of presenting the same information contained in the
balances although the scales are different (OECD, 2003). In this study the diffusion indices are
calculated by the method that Sutanto (1999) used for Indonesia case. For each question, the scales
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which are in the form of “more optimistic - the same - more pessimistic” or “up - the same -
down” are coded. The answers indicating improvement (better off) for a particular variable are
scored 2, while the answers showing no change are scored 1 and the answers showing a worsening
condition are scored 0. The questions affecting the industrial production index negatively are
coded with inverted sign. The scores of all firms in the sample are then summed to arrive at the
total score (Ts) for a variable. A diffusion index of this particular variable (vI ) is achieved by
dividing total score by the number of firms (N) and multiplying it by 100 percent. The formula is
given as:
%100*N
TI s
v = (1)
4.2. Selection of the Potential Cyclical Series
The economic variable representing the economic activity is called reference series. Since the aim
of the index is to forecast the expansion and contraction periods of the economic activity, a key
indicator is to be chosen as the reference series for comparison. After the identification of the
reference series, the next step is to check the cyclical profile and timing relationship between
survey series and the reference series. The diffusion indices from the surveys need not to be
detrended since they can be thought to measure period-to-period changes or deviations from trend.
Nilsson (1999) states “ The cyclical profiles of the series in many cases easier to detect because
they contain no trend i.e. their long term averages are stable. They may be considered as
stationary series”. At this step, three types of comparison are possible. The first type of
comparison uses the diffusion index of the survey series and the changes over the previous period
of the reference series. In the second comparison, the diffusion indices of the survey series can be
compared with the changes over the same period of the previous year of the reference series. In the
third one, the detrended reference series can be compared with the diffusion indices. The trend
estimation of the reference series can be done in two different ways. Whereas the first method
applies long term centered moving averages (75 or 60 month), the second method uses OECD
adapted Phase-Average-Trend (PAT)3 method.
At this point it is worth to note that a standard set of series across countries or an individual set of
series per country may be used in the construction of the confidence index. Although the use of a
standard set of series across countries is a good approach for obtaining international comparability,
cyclical series, which perform well in one country, may not work well in another because of the
3 OECD, “Cyclical Analysis and Composite Indicators System User Guide”, Paris 2000.
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differences in economic structure and statistical system (Nilsson, 2000). Therefore, the criteria
used for the selection of appropriate series to construct the confidence index can be put into
practice for the Turkish case. The criteria applied when assessing and selecting the indicators are
explained below.
4.2.1. Standardization and Smoothing
Ronny Nilsson (1999) states, “Standardization or normalization of component series is necessary
in order to prevent series with marked cyclical amplitude from dominating the composite
indicator…. Standardization of balance series from business tendency surveys is not always
performed. The argument for not standardizing such series is the fact that the amplitudes in the
different survey series are not so different due to the fact that the same fixed scale is used for all
series”. Therefore, the necessity of the standardization is to be checked.
In order to confirm that all component series have equal “smoothness”, the procedure utilized by
OECD, namely the "Months for Cyclical Dominance" (MCD) moving average can be used. This
procedure ensures that month-to-month changes in the confidence index are not excessively
influenced by irregular movements in any one of the diffusion indices. Nevertheless, as stated in
OECD’s Handbook (2003) “Business tendency survey series are also relatively smooth compared
with quantitative statistics. This is partly explained by the fact that business tendency survey series
are less sensitive to disruptive events such as changes in holidays or plant shutdown schedules and
unusual weather conditions that will affect quantitative statistics, particularly if they are monthly”.
4.2.2. Seasonality
Although the respondents are noticed to keep the seasonal effects in mind, the business survey
series may exhibit significant seasonality. Ferenczi and Reiff (2000) state, “From a business cycle
point of view and in case of indicators published for the non-specialist public, seasonality should
be treated as statistical noise, be always tested, and if necessary, corrected for”.
Seasonal adjusted data are subject to revisions when adding data at the end of the time series.
According to European Commission (2002), business survey data are economic agents’ opinion at
a certain point in time and revisions of the historical data do not seem to be acceptable. Therefore,
before applying seasonal adjustment method, the significance of the seasonality should be tested
carefully.
Once the diffusion indices per question for the survey are calculated, they can be seasonally
adjusted. The seasonality of the diffusion indices for each question in the BTS is searched each
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month by the TRAMO-SEATS programs within the Demetra interface (Gomez and Maravall,
1998).
4.2.3. Cross-Correlation Analysis
In simple cross-correlation analysis, positive cross-correlation with the cycles of the industrial
production index is examined. The correlation between variables is assumed to be ‘strong’ when
the correlation coefficient is greater than 0.45 in absolute values. This cut off point is not based on
any statistical test (Adamowicz, Dudek and Walczyk, 2002).
4.2.4. Peak-Trough Analysis
Peak-trough analysis includes examination of the behavior of the diffusion indices per question in
relation to the cyclical turning points4 of the reference series. The statistics such as mean and
median leads, extra or missing cycles in the diffusion indices with respect to the reference series
are collected. In this kind of analysis, the median, rather than the mean is usually preferred due to
the small number of observations.
4.2.5. Low Volatility
The standard deviation from median of the lead for each series being low is important because the
leading time of the series at turning points should be consistent.
4.2.6. Economic Significance
Before the series are accepted as an indicator according to the statistical criteria, there has to be an
economic reason. Therefore, an algorithm describing stages of the production process for Turkey
has been derived in Appendix B5. This algorithm shows all the stages assumed to be thought by
managers before making production decision.
4.2.7. Weighting
In order to obtain a single composite index, different weights can be assigned to component series.
The aim of weighting is to improve reliability by giving higher weight to better components. The
weights of the components can be given according to their economic significance or statistical
adequacy. Principal component analysis can be used for the selection of the optimal weights.
However, such a technique would minimize the contribution of the series that do not act with the
other series while the series have a good leading property. This may reduce the reliability of the
4 See Section 5.1. 5 We are particularly thankful to Cevriye Aysoy for developing the algorithm based on the BTS questions and her explanations and comments.
14
index because some series perform better in one cycle and others in a different cycle. Therefore,
most countries use an equal weighting system (Nilsson, 1999).
The general formula for the confidence index is given as:
n
wII v∑= (2)
where
I = Confidence index, w = Weight,
vI = Diffusion index of a variable,
n = Number of variables
4.2.8. The Interpretation of the Real Sector Confidence Index and the Points to be
Highlighted
A proxy interpretation of the index magnitude (Sutanto, 1999):
I = 200 (maximum) : all respondents showing an improvement/ better off.
I = 100 : number of establishments showing an improvement and worsening
conditions are balanced, or such that the general conditions of respondents are unchanged.
I > 100 : number of establishments showing an improvement is higher than
that showing a worsening condition, or such that the general condition of respondent is better.
I < 100 : number of establishments showing an improvement is lower than
that showing a worsening condition, or such that the general condition of respondent is worse.
However, while interpreting the confidence index there are some important points to be
highlighted. Diffusion indices do not give information about the magnitude of the business cycles.
Furthermore, intermittent increases or decreases in the index should not be interpreted as
indications of expansion or contraction of the economic activity.
5. THE EMPIRICAL RESULTS
5.1. Reference Series
Ideally, the gross domestic product (GDP) of Turkey would be used as the reference series, but it is
available only on quarterly basis and there is one quarter of time lag in the publication of GDP
estimates. However, industrial production index is available on a monthly basis and the turning
15
points of industrial production index and GDP are closely related. Therefore, the industrial
production index, which is an important indicator for the economic activity, is chosen as the
reference series for comparison.
The detrended reference series is preferred to be compared with the diffusion indices due to the
availability of the relevant series for Turkey. For the cycles of the industrial production index, the
series derived by the cooperative study of the CBRT and OECD is used (See
http://www.tcmb.gov.tr/yeni/eng/index.html ).
The reference chronology6 of the industrial production index is given in Table 6.
Table 6: Reference Chronology of the Industrial Production Index
Turning Points Duration in Months
Trough Peak Deceleration Acceleration
- December 1987 - - April 1989 May 1990 16 13
November 1991 April 1993 18 17
May 1994 February 1998 13 45
August 1999 August 2000 18 12
April 2001 - 8 - Mean 14.6 21.8
Median 16.0 15.0 5.2. Standardization and Smoothing
The series of the BTS have the same fixed scale i.e. the amplitudes of the series do not differ from
each other (the standard deviations of all series range between 5.3 and 21.1). On the other hand,
most of the diffusion indices show MCD values in the range 2-4 (See Table 7). Therefore,
smoothing of the diffusion indices is performed but irregular components’ effect is found to be
little on each of the diffusion indices. Consequently, no standardization and smoothing seem to be
necessary.
5.3. Seasonality
Seasonality is examined for each of the diffusion indices. Seasonal influences are found to be
present in some of the indices. However, in general the seasonal influences have little impact on
the identified cyclical patterns. Besides, seasonally adjusted diffusion indices seem only to
marginally improve correlations with the reference cycle. Therefore, to keep the work simple,
using seasonally unadjusted series are considered to be appropriate for the construction of the real
6 See http://www.tcmb.gov.tr/yeni/eng/index.html
16
sector confidence index. The seasonality structures of the selected diffusion indices and the
confidence index, discussed further in Section 5.6, also provided no evidence against the use of
unadjusted series.
5.4. Selection of the Series
The series are selected on the basis of the cross-correlations and peak-trough analysis together with
low volatility and economic significance criteria. The results from the cross-correlation, volatility
and peak-trough analysis including the number of extra or missing cycles are given in tables 7 and
8.
The diffusion indices are tested for extra and missing cycles (Table 7). Among these indices, 22
series show one extra cycle while few of them miss cycles present in reference series. 10 diffusion
indices out of 35 are in line with the cycles of the reference series.
Most of the diffusion indices except for the indices related with prices, the financial requirement
(next month), the stocks of finished goods (trend of last three months) and the indices related with
exports excluding export opportunities (next three months) are potentially good indicators on the
basis of their correlations with the reference series. As it can be seen in Table 8, the correlation
coefficients of those series and cycles of the industrial production index are between 0.45 and
0.666. While their mean lead times are between 0 and 9 at all turning points, the median lead times
are between 1 and 6 at all turning points. However, among these indicators investment
expenditures and stocks of finished goods (trend of next three months) are found to have relatively
high volatility. While the average unit cost indices are good indicators in terms of cross-correlation
analysis, the trend of the next three months index has high volatility and the last three months trend
index has a lagging property instead of leading.
In addition to the statistical criteria, the adopted stages of the production decisions are also
examined for the selection of the series. The explanation of the selection algorithm is as follows:
As a first step, we need to think about the decision-making process in supplying goods and
services. The firm’s management must be concerned with three sets of conditions in order to
develop a plan for production: 1) conditions in the economy as a whole, 2) conditions in the
industry and especially in competitive firms, and 3) conditions inside the firm (Cyert, 1988). In
peculiar to Turkish case, the managers try to forecast the general business, economic (i.e. exchange
rate, interest rate and inflation) and political conditions. Firm’s main decision of how much output
to produce depends on the size of domestic and external market demand. In order to decide about
17
the level of the production, the firm should accurately anticipate the market demand and ensure the
sufficiency of its production to meet the anticipated demand. As to the suitability of the production
decisions with the demand, the level of stocks of finished goods compared to a firm-specific ideal
level is a good indicator for the manager's assessment of the current production level. On the other
hand, due to the raw material stocks are usually built up parallel to the anticipation of increasing
demand, direction of raw material stocks also provides information about firms' assessment of
demand conditions. The classification of the market demand as permanent or temporary is
fundamental for the firms' decisions. In case of a temporary market demand, the manager will
check the level of the stocks. If the manager makes the decision of enough stocks, there is no need
for increasing production. Only when the firms are optimistic and feel confident about a permanent
demand, they are more likely to install new equipment, add to their capacity and increase the
employment since investment is time consuming and costly. Therefore, employment and
investment plans reflect whether the firms anticipate a long lasting upward or downward
movement of demand.
18
Table 7: Irregular Variation (MCD*) and Extra/Missing Cycle Analysis
Indicators (Diffusion Indices)
Irregular variation
MCD*
Extra/Missing cycles
Number of
diff.ind.
d1 General economic situation (compared with previous month) 3 1 extra cycle
d2 Export opportunities (next three months) 4 1 extra cycle
d3 Investment expenditures (next twelve months) 2 -
d4 Capacity utilization (compared with previous month) 4 1 extra cycle
d5 Productive capacity (next twelve months) 3 1 extra cycle
d6 Sales revenues (compared with previous month) 5 1 extra cycle
d7 Past-due receivables (compared with previous month) 3 1 extra cycle
d8 Financial requirement (compared with previous month) 4 -
d9 Total orders (current month) 3 1 extra cycle
d10 Export orders (current month) 4 1 extra cycle
d11 Monthly stocks of finished goods (current month) 4 1 extra cycle
d12-1 Employment (trend of last three months) 2 -
d12-2 Employment (trend of next three months) 2 1 extra cycle
d13-1 New orders from domestic market (trend of last three months) 3 1 extra cycle
d13-2 New orders from domestic market (trend of next three months) 2 1 extra cycle
d14-1 New orders from export market (trend of last three months) 4 1 extra cycle
d14-2 New orders from export market (trend of next three months) 3 1 extra cycle
d15-1 Production (trend of last three months) 3 1 extra and 1 missing cycle
d15-2 Production (trend of next three months) 3 1 extra cycle
d16-1 Goods sold in domestic market (trend of last three months) 3 1 missing cycle
d16-2 Goods sold in domestic market (trend of next three months) 2 1 extra cycle
d17-1 Exported goods (trend of last three months) 3 1 extra cycle
d17-2 Exported goods (trend of next three months) 4 1 extra and 1 missing cycle
d18-1 Raw- material stocks (trend of last three months) 4 -
d18-2 Raw- material stocks (trend of next three months) 4 -
d19-1 Work in process (trend of last three months) 2 -
d19-2 Work in process (trend of next three months) 3 1 extra cycle
d20-1 Stocks of finished goods (trend of last three months) 4 1 extra and 1 missing cycle
d20-2 Stocks of finished goods (trend of next three months) 6 2 missing cycles
d21-1 Average unit cost (trend of last three months) 4 1 extra cycle
d21-2 Average unit cost (trend of next three months) 4 2 missing cycles
d22-1 Average price for new domestic orders (trend of last three months) 4 -
d22-2 Average price for new domestic orders (trend of next three months) 4 -
d23-1 Average price for new export orders (trend of last three months) 4 -
d23-2 Average price for new export orders (trend of last three months) 4 -
* MCD moving average method uses minimum (optimal) order of moving average which is enough to eliminate irregular fluctuation from the data without affecting trend and cyclical movements. This method uses MCD span for which the ratio between the trend and the irregular component is less than 1, i.e. I/C<1 (where I denotes the irregular component and C denotes the trend cycle component). Generally, 1, 2 and 3 months are used as the moving average order.
19
Table 8: Characteristics of Diffusion Indices from the BTS
* Standard deviation from median of the lead.
7 dXY-Z refers to the diffusion index of question XY. Z takes value 1 for last three months and 2 for next three months. 8 A positive number indicates that the series is a leading, negative lag means a lagging indicator and a zero lag indicates a coincident indicator.
Mean lead (+) at Median lead (+) at turning points (TP) turning points (TP)
Cross -correlation
Indicators (Diffusion Indices)
Peak Trough All TP Peak Trough
All TP
Standard deviation*
Lead (+)8 Coef.
Number of
diff.ind. d17 General economic situation (compared with previous month) 3 6 4 3 6 4 3.6 3 0.551 d2 Export opportunities (next three months) 10 7 9 8 8 8 5.5 5 0.447 d3 Investment expenditures (next twelve months) 5 3 4 -1 3 1 11.4 2 0.500 d4 Capacity utilization (compared with previous month) 3 6 4 3 8 4 4.0 3 0.660 d5 Productive capacity (next twelve months) -1 3 1 -1 4 1 4.6 1 0.634 d6 Sales revenues (compared with previous month) 2 7 4 3 8 4 4.1 4 0.574 d7 Past-due receivables (compared with previous month) 3 7 5 3 8 6 4.1 4 0.514 d8 Financial requirement (compared with previous month) 6 7 6 3 5 3 11.1 4 0.329 d9 Total orders (current month) 1 7 4 3 9 4 5.3 2 0.666 d10 Export orders (current month) 8 7 7 4 8 6 5.0 3 0.319 d11 Monthly stocks of finished goods (current month) 2 7 5 2 9 5 4.4 3 0.453 d12-1 Employment (trend of last three months) 2 5 3 1 6 2 4.5 2 0.586 d12-2 Employment (trend of next three months) 4 5 4 4 6 4 3.8 3 0.520 d13-1 New orders from domestic market (trend of last three months) 1 6 4 1 9 4 4.5 2 0.658 d13-2 New orders from domestic market (trend of next three months) 3 7 5 3 10 6 4.4 3 0.542 d14-1 New orders from export market (trend of last three months) 8 9 8 6 9 9 3.9 5 0.388 d14-2 New orders from export market (trend of next three months) 11 8 9 11 8 8 5.1 8 0.312 d15-1 Production (trend of last three months) 3 4 3 3 4 3 3.8 2 0.621 d15-2 Production (trend of next three months) 4 6 5 4 6 4 3.7 3 0.556 d16-1 Goods sold in domestic market (trend of last three months) 3 3 3 3 3 3 3.8 2 0.635 d16-2 Goods sold in domestic market (trend of next three months) 3 6 4 3 7 4 4.0 3 0.566 d17-1 Exported goods (trend of last three months) 11 7 9 11 9 9 5.4 5 0.355 d17-2 Exported goods (trend of next three months) 10 8 9 5 9 7 5.4 8 0.325 d18-1 Raw- material stocks (trend of last three months) -2 2 0 -1 1 1 4.0 2 0.628 d18-2 Raw- material stocks (trend of next three months) -1 6 2 -2 7 1 5.2 4 0.597 d19-1 Work in process (trend of last three months) 2 5 3 3 5 3 3.7 2 0.620 d19-2 Work in process (trend of next three months) 3 7 5 3 10 6 4.4 3 0.573 d20-1 Stocks of finished goods (trend of last three months) 2 0 1 7 9 9 15.2 -4 0.399 d20-2 Stocks of finished goods (trend of next three months) 27 20 23 27 20 20 18.3 5 0.543 d21-1 Average unit cost (trend of last three months) -1 1 0 1 1 1 4.1 -12 0.454 d21-2 Average unit cost (trend of next three months) 3 11 7 3 11 3 11.8 14 0.529 d22-1 Average price for new domestic orders (trend of last three months) 5 14 9 3 14 5 12.0 11 0.248 d22-2 Average price for new domestic orders (trend of next three months) -4 -14 -8 -6 -4 -5 14.1 11 0.317 d23-1 Average price for new export orders (trend of last three months) -10 -18 -13 -12 -10 -10 13.8 7 0.224 d23-2 Average price for new export orders (trend of last three months) -6 -7 -7 -7 -7 -7 6.3 7 0.300
20
In order to finalize the selection of the series, the information from the assumed production process
is combined with the statistical properties of the diffusion indices. Question about general business
situation, d1, has no alternative and also has good statistical properties. As given in Section 2.2.9,
there are more than one question for domestic demand and exports and they are highly correlated.
Choosing one question from each group according to statistical criteria is considered to be
appropriate. The goods sold in domestic markets (last three months), d16-1, has equal mean and
median lead times at turning points besides its small standard deviation among the domestic
demand related questions, so it is chosen as a demand indicator. The diffusion index of this
question can also be considered as an indicator of the firm’s performance. As foreign demand is
considered, the diffusion indices d10, d14, d17 related to the last three months trend and d2, d14,
d17 related to the next three months trend are examined respectively. Firms' evaluation of the
export prospects over the next three months, d2, has an average lead-time of 5 and a high
correlation with the cycle of industrial production index compared to the others. Since the
prospects of the amount of export orders received this month, d10, are always pessimistic and there
is no significant change in its trend besides its low correlation, it is not appropriate to evaluate this
question as a foreign demand indicator. Total amount of orders received this month, d9, is chosen
to indicate the strength of the total demand for the firms product. In order to evaluate the level of
the stocks of the finished goods, d11, having a consistent mean and median lead-time at turning
points and a lower standard deviation compared to d20-1, is chosen. A firm having the thought of a
permanent demand might already have started to increase the volume of its raw-material stocks
within the last three months and will increase the level of its investment and employment. As it is
mentioned above, the diffusion indices related to the investments in the next 12 months, d3, raw
material stocks (trend of last three months), d18-1, and employment in the next three months, d12-
2 have good statistical performances. So, they are chosen as the suitable series. Finally, the volume
of output, d15-2, is an important series at this stage of forecasting economic activity and has good
statistical properties. Having a low cross-correlation with the reference series and/or bad cyclical
properties, cost and price questions are not considered in the index.
Finally, the diffusion indices d1, d2, d3, d9, d11, d12-2, d15-2, d16-1 and d18-1 are chosen to
construct the confidence index, namely MBRKGE.
21
5.5. Weighting
The principal component analysis is used to choose the optimal weights. The results of the
principal component analysis illustrate that scores have approximately equal weights. In addition,
an equal weighting system is used by most indicator systems in operation. Thus, the composite
index is calculated on the equally weighted average of the sub-indices in line with the applications
in the EU countries. No base-year adjustment has been made to the index.
5.6. Additional Remarks
As a final point, we would like to turn back to the seasonality issue. According to the Nilsson’s
(2003) recommendation, seasonal adjustment should be performed at the level of the diffusion
indices instead of the aggregate level of the confidence index. Following this recommendation, the
adjusted confidence index based on the seasonally adjusted diffusion indices (see Chart 1) is
constructed and compared with the unadjusted one. Both of the confidence indices are shown in
Figure 6. The figure illustrates insignificant difference, so that the remaining part of the study is
based on the unadjusted confidence index.
Figure 6: Business Confidence Index (seasonally adjusted and unadjusted)
55.0
65.0
75.0
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Seasonally Adjusted Confidence Index
Unadjusted Confidence Index
22
Chart 1: Diffusion Indices of the MBRKGE
* d1, d2, d3, d18-1 do not show seasonal pattern.
30405060708090
100110120130140150
Dec
.87
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.88
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.92
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d2
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.87
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d9
d9 (seasonally adjusted)
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d11 (seasonally adjusted)
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d12-2d12-2 (seasonally adjusted)
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d15-2
d15-2 (seasonally adjusted)
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d16-1 (seasonally adjusted)
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d18-1
23
6. PERFORMANCE OF THE REAL SECTOR CONFIDENCE INDEX (MBRKGE)
AND ITS COMPONENTS
The questions that are used to construct the real sector confidence index are compared with the
conventional quantitative realizations to evaluate their degree of consistence with the realizations.
The analysis is restricted mostly to the concept of output and the cross-correlation coefficients and
the implications of the economic theory are used.
In order to compare the components of the MBRKGE with the evolution of the quarterly
quantitative realizations, monthly indices are transformed into quarterly series. This is done by
taking the arithmetic mean of the three months in the quarter. The diffusion indices and
realizations, showing seasonal pattern are adjusted with TRAMO/SEATS procedure. The cyclical
pattern of the quantitative series is obtained by eliminating trend component via HP filter. Besides,
year-to-year and quarter-to-quarter changes of the series are also considered. Among the
transformations of the related quantitative series, the highest correlations between the BTS
diffusion indices and economic indicators are given in Table 9.
Table 9: The Cross-Correlation Analysis of Quantitative Realizations with the BTS Indices
*Detailed information about the series can be found in Appendix C.
**Q-o-Q represents quarter-to-quarter changes and Y-o-Y represents year-to-year changes.
***A positive number indicates that the diffusion index is a leading and a zero lag indicates a coincident indicator.
+ Note that d11 has coded with inverted sign.
Because of the assumption that recovery in the general economic situation has an immediate
impact on production, the assessments of the firms about the general economic situation are
Transformation
Diffusion Index Related Quantitative
Series* Diffusion Index Related Quant.
Series** Lead*** Cross-Corr.
General economic situation (d1)
Production of Manufacturing (Private) Quarterly Mean Q- o- Q % change 1 0.626
Export prospects (d2) Export Volume of Manufacturing - Detrended 0 0.291
Investment expenditures (d3) Import Volume of Capital Goods - Y-o-Y % Change 1 0.713
Total orders (d9) Import Volume of Intermediate Goods - Y-o-Y % Change 0 0.781
Monthly stocks of finished goods (d11)
Changes in Stocks Quarterly Mean - 5 -0.462+
Employment (d12-2) Production Workers (Private) Quarterly Mean Q- o- Q % change 0 0.814
Volume of output (d15-2) Industrial Production Index (Manufacturing) -
Y-o-Y % Change 2 0.632
Goods sold in domestic market (d16-1)
Private Consumption Quarterly Mean Y-o-Y % Change 0 0.797
Raw material stocks (d18-1) Import Volume of Intermediate Goods - Detrended 0 0.533
24
compared with the production of private manufacturing and relatively a high correlation (0.626) is
found. The sub-item of the foreign trade export volume index, the volume of manufacturing
exports is expected to be consistent with the opinion about the export prospects over the next three
months compared with the previous month. It is worth to note that export prospects are sensitive to
sudden economical changes while export realizations change in the long term. For that reason the
relationship between the export prospects and its realization is found to be low (See Chart 2).
Investment expenditures are compared to the imports of capital goods finding that the amount of
expected investment expenditure displays a parallel pattern with the import volume of capital
goods. In order to compensate orders, intermediate goods are generally imported. So, there exists
quite high correlation between total orders and imports of intermediate goods. It can be easily seen
that the assessment of the monthly stocks of finished goods, the next three months’ trend of total
employment, the volume of output and the goods sold in domestic market are reasonably related to
changes in stocks, the index of production workers in private manufacturing industrial firms, the
industrial production index and private consumption respectively. The firms may increase raw
material stocks via importing. There exists correlation of 0.533 between the assessment of raw
material stocks and import volume of intermediate goods, which supports this idea.
It is to note that the indices derived from the BTS stand in quite high correlation with the
realizations even though the methodology and information sources are varied. As a result of the
analysis of index questions, it can be concluded that they are highly consistent with the
realizations.
25
Chart 2: Diffusion Indices of the MBRKGE and the Realizations
20
40
60
80
100
120
140
160
1988
Q1
1989
Q3
1991
Q1
1992
Q3
1994
Q1
1995
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1997
Q1
1998
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2000
Q1
2001
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0
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10
15d1Production of Manufacturing
20
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140
Jan
.95
Jul.9
5Ja
n.9
6Ju
l.96
Jan
.97
Jul.9
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Jan
.99
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d3Import Volume of Capital Goods
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.95
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.97
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0
20
40
60
80
d9Import Volume of Intermediate Goods
40
50
60
70
80
90
100
110
120
130
19
88
Q4
19
90
Q2
19
91
Q4
19
93
Q2
19
94
Q4
19
96
Q2
19
97
Q4
19
99
Q2
20
00
Q4
20
02
Q2
-50
-40
-30
-20
-10
0
10
20
30
40
50d16-1 Private Consumption
60
80
100
120
140
Jan.
95Ju
l.95
Jan.
96Ju
l.96
Jan.
97Ju
l.97
Jan.
98Ju
l.98
Jan.
99Ju
l.99
Jan.
00Ju
l.00
Jan.
01Ju
l.01
Jan.
02Ju
l.02
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4d2
Export Volume of Manufacturing
50
70
90
110
130
150
Jan.
89
Jan.
90
Jan.
91
Jan.
92
Jan.
93
Jan.
94
Jan.
95
Jan.
96
Jan.
97
Jan.
98
Jan.
99
Jan.
00
Jan.
01
Jan.
02
-30
-20
-10
0
10
20
30
40
d15-2Industrial Production Index
70
80
90
100
110
Jan
.94
Jul.9
4Ja
n.9
5Ju
l.95
Jan
.96
Jul.9
6Ja
n.9
7Ju
l.97
Jan
.98
Jul.9
8Ja
n.9
9Ju
l.99
Jan
.00
Jul.0
0Ja
n.0
1Ju
l.01
Jan
.02
Jul.0
2
-50
-40
-30
-20
-10
0
10
20
30
40
50
d18-1Volume of Intermediate Goods
50.0
70.0
90.0
110.0
130.0
1997
Q1
1997
Q4
1998
Q3
1999
Q2
2000
Q1
2000
Q4
2001
Q3
2002
Q2
-8
-6
-4
-2
0
2
4
6
d12-2Production Workers
26
After the analysis of the BTS effectiveness by comparing the diffusion indices with the
conventional realizations, MBRKGE’s performance is tested in the following part. MBRKGE,
having the property of leading at the turning points of economic activity, is compared with the
cycles of the industrial production index in Figure 7 for illustrating its historical performance. The
economic recession between 1988 and 1989, the slump during the Gulf crises in 1990, the currency
crisis in 1994, the Asian and Russian crisis of 1997, the financial crisis in February 2001 following
on the liquidity crisis in November 2000 and the upturns in 1989, 1992-1993, 1995 and 2000 are
forecasted by the confidence index. Therefore, the index seems to be useful for monitoring and
detecting changes in economic activity and appears to be precious in terms of conjunctural
analysis.
Figure 7: Business Confidence Index and Cycles of Industrial Production Index
50
60
70
80
90
100
110
120
Dec
.87
Jun.
88
Dec
.88
Jun.
89
Dec
.89
Jun.
90
Dec
.90
Jun.
91
Dec
.91
Jun.
92
Dec
.92
Jun.
93
Dec
.93
Jun.
94
Dec
.94
Jun.
95
Dec
.95
Jun.
96
Dec
.96
Jun.
97
Dec
.97
Jun.
98
Dec
.98
Jun.
99
Dec
.99
Jun.
00
Dec
.00
Jun.
01
Dec
.01
Jun.
02
85
95
105
115Business Confidence Index (MBRKGE)Cycles of Industrial Production Index
Gulf WarRussian Crises Bottom of 2001
Crises
Economic Recession
1994 Crises
The peak-trough analysis results of MBRKGE are shown in Table 10.
Table 10: Peak-Trough Analysis
MCD Mean lead (+) Median lead (+) Standard Cross-
at turning points (TP) at turning Points (TP) deviation correlation
Peak Trough All TP Peak Trough All TP Lead (+) Coeff
2 2 4 3 3 3 3 3.4 2 0.687
27
It is found that the real sector confidence index has median lead-times of 3 and 3 at peaks and
troughs, respectively. The real sector confidence index can be considered as quite smooth with the
MCD value of 2. Cross-correlation result indicates that the MBRKGE is well correlated with the
reference cycle at 2-month lead.
As a next step the Granger causality analysis is performed to support the result of cross-correlation
in terms of the underlying causality relationship. It is worth to note that Granger causality does not
mean any strict cause and effect relation, but simply one variable can be useful in predicting
another.
Before the Granger causality analysis, the stationarity of the series are tested by means of
Augmented Dickey-Fuller unit root test, with the lag lengths selected with Schwarz Information
Criterion (SIC) procedure.
Table 11: Unit Root Tests on MBRKGE and Cycles of Industrial Production Index
ADF Test Statistic
Lag 1 % critical value 5 % critical value
MBRKGE -4.656 1 -3.467 -2.877
Cyc. of Ind. Prod. Index
-3.818 1 -3.467 -2.877
Table 11 shows evidence of stationarity at both of the 1 % and 5 % significance levels.
Table 12: Pairwise Granger Causality Test Lags 2
Null Hypothesis Obs F-Statistic Prob
MBRKGE does not Granger cause Cycles* 177 12.6901 0.0000072
Cycles* does not Granger cause MBRKGE 177 9.23640 0.00015
* Cycles of Industrial Production Index
In order to test the Granger causality a vector autoregressive forecasting model with 2 lags9 is
developed. The Granger test strongly rejects the hypothesis of no causality between the MBRKGE
and cycles of industrial production index (Table 12). Finding two-sided causality relation indicates
the need for considering the feedback effects when MBRKGE is used for forecasting industrial
production.
9 The lag length is selected as 2 using the Schwarz information criterion and Hannan-Quinn information criterion.
28
The real sector confidence index is compared with the official composite leading indicator
(MBONCU - SUE) of the Turkish economic activity, consisting of four BTS questions and three
macro economic series, in Figure 8.
Figure 8: Business Confidence Index and Leading Indicator of Industrial Production Index
The figure shows that the cyclical turning points of the real sector confidence index are in line with
those of the official composite leading indicator. According to the cross-correlation analysis there
is a coincident relation between these two indicators with a correlation of 0.9293. However, it is
important to note that the composite leading indicator and MBRKGE include some of the BTS
questions in common such as export possibilities, stocks of finished goods and total amount of
employment. Besides, the diffusion index of the raw material stocks can correspond to the official
statistics used in MBONCU-SUE, namely import volume of intermediate goods.
Although industrial production index is chosen as the reference series, the performance of
MBRKGE with the cycles of GDP is also examined and illustrated in Figure 9.
Figure 9: Quarterly Business Confidence Index and Cycles of GDP
50
60
70
80
90
100
110
120
July
.88
Ap
r.8
9
Jan
.90
Oct
.90
July
.91
Ap
ril.9
2
Jan
.93
Oct
.93
July
.94
Ap
r.9
5
Jan
.96
Oct
.96
July
.97
Ap
r.9
8
Jan
.99
Oct
.99
July
.00
Ap
r.0
1
Jan
.02
Oct
.02
80
85
90
95
100
105
110MBRKGE Cyles of GDP (right scale)
50
60
70
80
90
100
110
120
130
De
c.8
7
Au
g.8
8
Ap
r.8
9
De
c.8
9
Au
g.9
0
Ap
r.9
1
De
c.9
1
Au
g.9
2
Ap
r.9
3
De
c.9
3
Au
g.9
4
Ap
r.9
5
De
c.9
5
Au
g.9
6
Ap
r.9
7
De
c.9
7
Au
g.9
8
Ap
r.9
9
De
c.9
9
Au
g.0
0
Ap
r.0
1
De
c.0
1
Au
g.0
2
80
90
100
110
120MBRKGEMBONCU-SUE (right scale)
29
The confidence index is constructed by combining the quarterly averages of monthly index values
as well as taking the end of quarter values of the monthly indices (March index for the first
quarter, June index for the second quarter, etc.). The cycles of the quarterly GDP is derived by the
methodology based on the joint study of the CBRT and the OECD and compared with quarterly
index. The index calculated by the end of period values has higher cross-correlation than the index
calculated by taking quarterly averages. Besides, the quarterly index constructed by using the
quarterly average of monthly values of diffusion indices has a higher cross- correlation coefficient
than the one reached by MBRKGE. This finding is in line with the results of Candemir and
Karabudak (1994) and shows that the confidence index provides valuable information about the
economic activity on the quarterly basis as well.
7. THE SECTORAL INDICES
In order to gain information on sectoral confidence and to investigate the differences between the
sectors, the confidence index is calculated for each sector of the BTS by using the general formula
(2) and illustrated in Chart 3. The confidence indices corresponding to the textile, the food and the
stone-soil sectors show seasonality and therefore the component series are seasonally adjusted by
TRAMO-SEATS. The averages of the indices over the period December 1987-October 2002 is
taken as the long-term averages and shown in Chart 3. Because of the low sampling proportion of
the energy and mining sectors, the corresponding confidence indicators are not calculated.
However, note that the sectors such as metal, forestry, and paper also have relatively low sampling
proportions.
As it can be seen from Chart 3, some of the confidence indicators are very volatile and do not
show cyclical behavior so they cannot be considered as a leading indicator. Among the eight
sectoral confidence indicators the metal sector has the highest similarity with the cycles of
industrial production index. However, note that the comparison of the confidence indicators should
be done with the related subsector cycles rather than the cycles of the aggregate industrial
production index. Therefore, this result shows that analysis based on sectoral levels, which could
be the aim of further study, should be performed individually.
30
Chart 3: Classification by Sectors
*The confidence indices for metal, chemistry, machine, paper and forestry sectors do not show seasonality.
50.0
60.0
70.0
80.0
90.0
100.0
110.0
120.0
De
c.87
De
c.88
De
c.89
De
c.90
De
c.91
De
c.92
De
c.93
De
c.94
De
c.95
De
c.96
De
c.97
De
c.98
De
c.99
De
c.00
De
c.01
Metal Long term average
50.0
60.0
70.0
80.0
90.0
100.0
110.0
120.0
De
c.87
De
c.88
De
c.89
De
c.90
De
c.91
De
c.92
De
c.93
De
c.94
De
c.95
De
c.96
De
c.97
De
c.98
De
c.99
De
c.00
De
c.01
TextileTextile (seasonally adjusted)Long term average
50.0
60.0
70.0
80.0
90.0
100.0
110.0
120.0
130.0
140.0
De
c.87
De
c.88
De
c.89
De
c.90
De
c.91
De
c.92
De
c.93
De
c.94
De
c.95
De
c.96
De
c.97
De
c.98
De
c.99
De
c.00
De
c.01
Chemistry Long term average
40.050.060.070.080.090.0
100.0110.0120.0130.0140.0
De
c.8
7
De
c.8
8
De
c.8
9
De
c.9
0
De
c.9
1
De
c.9
2
De
c.9
3
De
c.9
4
De
c.9
5
De
c.9
6
De
c.9
7
De
c.9
8
De
c.9
9
De
c.0
0
De
c.0
1
Forestry Long term average
50
60
70
80
90
100
110
120
130
Dec
.87
Dec
.88
Dec
.89
Dec
.90
Dec
.91
Dec
.92
Dec
.93
Dec
.94
Dec
.95
Dec
.96
Dec
.97
Dec
.98
Dec
.99
Dec
.00
Dec
.01
FoodFood (seasonally adjusted)Long term average
40.050.060.070.080.090.0
100.0110.0120.0130.0140.0
De
c.87
De
c.88
De
c.89
De
c.90
De
c.91
De
c.92
De
c.93
De
c.94
De
c.95
De
c.96
De
c.97
De
c.98
De
c.99
De
c.00
De
c.01
Paper Long term average
40.050.060.070.080.090.0
100.0110.0120.0130.0140.0
Dec
.87
Dec
.88
Dec
.89
Dec
.90
Dec
.91
Dec
.92
Dec
.93
Dec
.94
Dec
.95
Dec
.96
Dec
.97
Dec
.98
Dec
.99
Dec
.00
Dec
.01
Stone and SoilStone and Soil (seasonal adjusted)Long term Average
40.050.060.070.080.090.0
100.0110.0120.0130.0140.0
De
c.8
7
De
c.8
8
De
c.8
9
De
c.9
0
De
c.9
1
De
c.9
2
De
c.9
3
De
c.9
4
De
c.9
5
De
c.9
6
De
c.9
7
De
c.9
8
De
c.9
9
De
c.0
0
De
c.0
1
Machine Long term average
31
8. CONCLUSION
The BTS of the CBRT is a sound tool for economic forecasting about the trends in the economy. In
this study qualitative questions of the BTS are analyzed and a real sector confidence index for
Turkey has been developed by the methodology and procedures of OECD (1997, 2000, 2002,
2003). The technique for the diffusion index calculation is similar to that used in Indonesia by
Sutanto (1999). Most of the diffusion indices of the related questions are found to be quite reliable,
interpretable and are proved to be helpful for macroeconomic forecasting.
This study emphasizes on the evaluation of the MBRKGE’s performance, the interpretation of the
index and the effectiveness analysis of the sub-indices with the realizations. On the basis of peak-
through, Granger causality and conjunctural analysis results, MBRKGE has the leading indicator
capability for the economic activity and is an indicator for the business confidence based on the
BTS.
Although it is clear that the index performs well, the BTS has some limitations: It requires some
developments and is still lacking in a number of respects. The survey has a sampling bias due to
the surveyed firms. More than half of the participants of the survey are fixed sample firms since
the beginning of the survey. The firms are not chosen evolving any probability sampling
techniques and the number of participants in each sector is not equal in proportion and not stable.
It is also worth to note that the real sector confidence index is based on the answers of the private
sector firms only. Besides, the BTS does not cover all the sectors covered in the construction of the
industrial production index. Therefore, the results may not accurately represent the real business
climate of the whole. This study shows us that the respondents have perceived the questions on a
similar subject in the same manner so that they give close answers. Therefore it is essential to
conduct an answering practices survey to have a clear understanding of the underlying basis upon
which respondents reply. It is clear that though there is some space for improvement of the survey,
the real sector confidence index performs very well. The survey provides early signals of economic
changes in Turkey.
Future expansions include improvement of the survey as a whole and at the sectoral level, so that
the number of participants in each sector may be stable and analogous. The real sector confidence
index at the sectoral level can be developed. Furthermore, the weighting of each reply according to
some criteria such as firm’s employment size or production value could be considered in
processing the answers.
32
It is worth to note that there is always a possibility to construct different confidence indices based
on different criteria. Besides, the explanatory properties of the indices of the MBRKGE must be
revised from time to time in order to validate the MBRKGE’s performance in the future.
33
REFERENCES
Adamowicz E., Dudek S. and Walczyk K. (2002), “ The Use of Business Survey Data in Analyses and
Short-term Forecasting, The Case of Poland”, Presented at the 26th CIRET Conference, Taipei, October.
Candemir H. B., Karabudak H. B. (1994), “The Search for a Business and/or Investment Confidence Index
in Turkey”, Selected Papers submitted to the 21st Ciret Conference 1993 in Stellenbosch, Studien 48.
Cyert M. Richard (1988), The Economic Theory of Organization and the Firm, New York University Press.
Dengiz B. and Ozcan C. (1991), “Iktisadi Yonelim Anketi’nin Gecerliliginin Incelenmesi Uzerine Bir
Calısma”, Quarterly Bulletin of CBRT, 1991/II, pp.183-191.
European Comission (2002), The Joint Harmonised EU Programme of Business and Consumer Surveys,
User Guide 2002.
Ferenczi B. and Reiff A. (2000), “The Role of Survey Data in the Forecasting Performance of Composite
Leading Indicators-The Case of Hungarian Industrial Production”, Submission to the 25th CIRET
Conference, Paris, October.
Gomez V. and Maravall A. (1998), “Seasonal Adjustment and Signal Extraction in Economic Time Series”,
Banco de España – Servicio de Estudios Documento de Trabajo n.o 0002.
Moser, C. A. and Kalton, G. (1972), Survey Methods in Social Investigation, 2nd edition, New York, Basic
Books.
Nilsson R. (1999), “Business Tendency Surveys and Cyclical Analysis”, Business Tendency Surveys,
Proceedings of the First Joint OECD-ADB Workshop, Manila, November.
Nilsson R. (2000), “Confidence Indicators and Composite Indicators”, Economic Surveys and Data
Analysis, CIRET Conference Proceedings, Paris.
Nilsson R. (2003), “Uses of Economic Indicators for Measuring Economic Trends”, OECD/ESCAP
Workshop on Composite Leading Indicators and Business Tendency Surveys, 24-26 February 2003,
Bangkok.
OECD (1997), “Development of Business and Consumer Surveys in Central and Eastern Europe, Summary
of Workshops 1991-1996”, Transition Economies Division Statistics Directorate, Paris, February.
OECD (2000), Cyclical Analysis and Composite Indicators System User Guide, Paris.
OECD (2002), “Economic Surveys and Data Analysis”, CIRET Conference Proceedings, Paris 2000.
OECD (2003), Business Tendency Surveys: A Handbook.
Oral E. (2002), “Inflation Expectations on the Basis of Qualitative Surveys”, Presented at the 26th CIRET
Conference, Taipei, October.
Ozdamar, K. (1997), Paket programlar ile Istatistiksel Veri Analizi, Anadolu Universitesi, Fen Fakultesi
Yayınları.
34
Pellissier, G.M. (2002), “Measuring Business Confidence in South Africa”, Presented at the 26th CIRET
Conference, Taipei, October.
Sutanto A. (1999), “Business Confidence Index, Consumer Confidence Index and Index of Leading
Indicators: An Experiment for Indonesia”, Business Tendency Surveys, Proceedings of the First Joint
OECD-ADB Workshop, Manila, November.
35
Appendix A1. Questions of the BTS 1. Your opinion about the general More Optimistic Same More Pessimistic course of business in your industry, compared with previous month 2. Your opinion about More Optimistic Same More Pessimistic export prospects over the next three months, compared with previous month 3. How much investment expenditure More Same Less do you expect to realize over the next 12 months compared to last 12 months 4. Your capacity utilization compared More Same Less with previous month 5. What is the level of your productive More than Adequate Adequate Less than Adequate capacity in accordance with your demand expectations for the next 12 months 6. What is the level of your sales Higher Same Lower revenues compared with previous month 7. What is the level of your past-due receivables compared with previous Higher Same Lower month 8. What is the financial requirement of your Higher Same Lower firm for the next month compared with previous month Excluding seasonal variations: 9. Total amount of orders received Above Normal Normal Below Normal this month 10. Amount of export orders Above Normal Normal Below Normal received this month 11. Amount of monthly stocks of finished Above Normal Normal Below Normal goods this month
36
Excluding seasonal variations, what is the last 3 months’ trend and what will be the next 3 months’ trend for the following items: Trend of the next Trend of the last three months three months 12. Total employment Up Same Down Up Same Down 13. The amount of new orders Up Same Down Up Same Down received from the domestic market 14. The amount of new orders Up Same Down Up Same Down received from the exports market 15. The volume of output Up Same Down Up Same Down 16. The volume of goods sold Up Same Down Up Same Down in domestic market 17. The volume of exported goods Up Same Down Up Same Down 18. The volume of raw-material Up Same Down Up Same Down stocks 19. The volume of work Up Same Down Up Same Down in progress 20. Stocks of finished goods Up Same Down Up Same Down 21. Average unit cost Up Same Down Up Same Down 22. Average price for the new Up Same Down Up Same Down orders received from the domestic market 23. Average price for the new Up Same Down Up Same Down export orders 24. According to your existing orders 1 1-3 3-6 6-9 9-12 12-18 18-24 received or production plan, Month Month Month Month Month Month Month how many months is your production programme 25. Over the next quarter, Order- Sales Labor (*) Capacity-Plant Credit-Finance Input-Cost which factor(s) might limit the production, rank according to degree of importance
(*) Qualified / Non-qualified
37
26. Over the next quarter, Price comp. Delivery date. Cred. finan Quotas- oth. Rest Foreign conj. which factor(s) might limit your export orders, rank according to degree of importance 27. Over the next 12 months, New investment what is the main reason for To increase capacity the planned expenditures on To increase productivity building, plant or equipment, Renovation rank according to degree No spending planned of importance 28. Which factor(s) might restrict Cost of financing the realization of these Shortage of capital expenditures, rank according to Shortage of external resources degree of importance Insufficient demand Insufficient net proceeds Cost of labor force 29. Over the next three months, Up Same Down what is your expectation for inflation (producer prices) rate 30. Over the next twelve months, what is your expectation for inflation (producer prices) rate 31. What is your expectation for year-end inflation (producer prices) rate 32. Over the next three months, Up Same Down what is your expectation for short term Turkish Lira credit interest rate 33. Over the next three months, what is your expectation for short term Turkish Lira credit interest rate (Determine as an annualized rate)
34. Over the next twelve months, what is your expectation for short term Turkish Lira credit interest rate (Determine as an annualized rate)
%
%
%
%
38
A2. Original Questionnaire
39
40
41
42
B. S
tage
s of
the
Pro
duct
ion
D
em
and
is p
erm
ane
nt
D
em
and
is t
em
po
rary
en
oug
h st
ock
Gen
eral
Eco
nom
ic
Situ
atio
n
Dem
and:
9
.que
stio
n tim
e t
2.q
uest
ion
time
t+3
1
6.q
uest
ion
time
t -3
Stoc
ks
11
.que
stio
n
time
t
Em
ploy
men
t: 1
2.q
uest
ion
time
t+3
In
vest
men
t: 3
.que
stio
n tim
e t+
12
R
aw M
ater
ial S
tock
s: :1
8.q
uest
ion
time
t-3
Pro
duct
ion:
15
.que
stio
n tim
e t+
3
Stoc
ks
11
.que
stio
n
time
t not e
nou
gh
sto
ck
Pro
duct
ion:
15
.que
stio
n tim
e t+
3
Stoc
ks
11
.que
stio
n tim
e t
43
C. Data Description
Questions Realizations
General economic situation Industrial Production Index, (1997=100) (SIS)
(Quarterly), (Private) Manufacturing
Export prospects Foreign Trade Export Volume Index by ISIC Rev. 3
Classification, (1994=100) (SIS) (Monthly),
Export- (Volume) Manufacturing
Investment expenditures Foreign Trade Import Volume Index by Classification
of BEC, (1994=100) (SIS) (Monthly),
Import- (Volume) Capital Goods
Total orders Foreign Trade Import Volume Index by Classification
of BEC, (1994=100) (SIS) (Monthly),
Import- (Volume) Intermediate Goods
Monthly stocks of finished goods GNP at fixed prices (1987=100) (SIS) (Quarterly),
(Expenditures) Changes in Stocks
Employment Index of Production Workers in Manufacturing
Industrial Establishments, (1997=100) (SIS)
(Quarterly), (Private) Manufacturing
Volume of output Industrial Production Index, (1997=100) (SIS)
(Quarterly), (Private) Total Industry
Goods sold in domestic market GNP at fixed prices (1987=100) (SIS) (Quarterly),
(Expenditure) Consumption (Private)
Raw material stocks Foreign Trade Import Volume Index by Classification
of BEC, (1994=100) (SIS) (Monthly),
Import- (Volume) Intermediate Goods
