Transcript Overview • Historical review of the FGV’s Brazilian Manufacturing Survey • The challenge of turning it into a monthly Survey • Detecting seasonality.

Overview
• Historical review of the FGV’s Brazilian Manufacturing Survey
• The challenge of turning it into a monthly Survey
• Detecting seasonality in the historical quarterly series
• Choosing a method for adjusting seasonality
• Analysing potential effects on seasonality while migrating the frequency
from quarterly to monthly
• Concluding Remarks
Historical Review
•
Brazilian Quarterly Manufacturing Survey created in 1966;
•
Inspired by european surveys (INSEE, Ifo);
•
1,100 responses each month;
•
Turned into monthly frequency in Nov. 05.
Challenge of moving into the Monthly Frequency
Potential problems:
•
Complexity of the questionnaire – Some questions are related to the firm’s
specific lines of products, not just the company as a whole;
•
Respondent Fatigue - Companies complain about the time spent answering
surveys in Brazil;
•
No enforcement - Companies are not obliged to answer the questionnaires.
Challenge of moving into the Monthly Frequency
Potential Solutions:
•
•
Dividing the sample into two different panels of companies with similar profiles;
Applying the same questionnaire to all respondents in all editions;
Chosen Movement:
•
•
Applying to all respondents the same questionnaire in the original months;
Applying a smaller questionnaire to all respondents in the complementary editions.
Apparent Reasons for Seasonality
in the Brazilian Manufacturing Survey
•
From 14 series of the survey analysed:


•
8 (57.1%) presented pronounced seasonal pattern
6 (42.9%) did not present a pronounced seasonal pattern
There are apparently two reasons for the presence of seasonality
in the survey series:
 The profile of the variable being measured
 The form of presenting the question (phrasing)
Reasons for Seasonality
Type of question
Actual Results
Previous Quarter
Examples:
• Future Production
• Future Employment
Month of
the Survey
Forecasts
Following Quarter
Exception:
• Future Prices
• Furture Business Situation
(compared to same semester, last year)
Reasons for Seasonality
Type of variable
Example: Level of Capacity Utilisation (reference to the month of the survey, multiple choice)
9 options:
0%
1% to 20%
Other Example:
• Level of Demand
...
80% to 89%
90% to 99%
Exception:
• Level of Stocks
• Present Business Situation
(slight seasonal pattern)
100%
Future Production Indicator *
Strong seasonal pattern
150.0
Original Data
Seasonally Adjusted Data
140.0
130.0
120.0
110.0
100.0
90.0
80.0
70.0
Quarterly Data from Jan.05 to Jul.09 * Indicator = Balance + 100
Level of Stocks Indicator *
Slight or no seasonal pattern
105.0
Original Data
Seasonally Adjusted Data
100.0
95.0
90.0
85.0
80.0
75.0
Quarterly Data from Jan.05 to Jul.09 * Indicator = Balance + 100
Seasonally adjusting the monthly series
•
For building a monthly Confidence Indicator with the most relevant indicators of
the Survey, FGV had to seasonally adjust the monthly series;
•
In 2008, with just three years of monthly series we decided to test the
interpolation of the quarterly series using Kalman filters in a structural model
framework;
•
The results were considered to be a success:
•
Seasonally adjusted monthly data of the industry tendency survey gained more relevance as a
reference to the Brazilian business cycle;
•
Even with the short monthly time series available, and using an univariate interpolation
method, the series appear consistent and present a good fit when compared to quantitative
indicators.
Adjusting the short time monthly series
Level of Capacity Utilization (LCU), Original Data
90.0
Quarterly
85.0
80.0
75.0
70.0
65.0
60.0
Monthly data available
from Nov.05 onwards
Testing Interpolation
LCU Original Data, After Interpolation using Structural Model
(Kalman filters)
90.0
85.0
80.0
75.0
70.0
65.0
60.0
LCU – Original and Adjusted
Original x Seasonally Adjusted Data, after interpolation
90.0
85.0
80.0
75.0
70.0
65.0
Interpolated Original Data
Interpolated Seasonally Ajusted Data
60.0
Choosing the Interpolation Method
Monthly Seasonal Factors for the LCU
Bivariate model, using LCU produced by the Confederation of Brazilian Industry
Univariate model
,1.5
,1.0
,0.5
,0.0
-,0.5
-,1.0
-,1.5
-,2.0
Evaluating Seasonality after four years
of Monthly Frequency (Nov. 05)
Level of Capacity Utilization
1.5
Future Production
20.0
15.0
10.0
5.0
0.0
-5.0
-10.0
-15.0
-20.0
-25.0
-30.0
1.0
0.5
0.0
-0.5
-1.0
-1.5
Future Employment
Global Demand
6.0
4.0
2.0
0.0
-2.0
-4.0
-6.0
-8.0
8.0
6.0
4.0
2.0
0.0
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
Level of Capacity Utilisation
Quarterly Seasonal Factors for the
Level of Capacity Utilisation
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
Comments:
Factors change along time but have stabilised in the ’00 decade, specially after 2002
Future Production
Quarterly Seasonal Factors for the
Future Production Indicator
20.0
15.0
10.0
5.0
0.0
-5.0
-10.0
-15.0
-20.0
-25.0
-30.0
Comments:
Seasonal factors are continuously changing but there are no signs of strutuctural changes around 2005
Level of Demand
Quarterly Seasonal Factors for the
Present Level of Demand Indicator
6.0
4.0
2.0
0.0
-2.0
-4.0
-6.0
-8.0
Comments:
No changes across time
Future Employment
Quarterly Seasonal Factors for the
Future Employment Indicator
8.0
6.0
4.0
2.0
0.0
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
Comments:
Changes occur along time. After 2005 there seems to be no structural break but since 2004, the factors for the
month of July (maximum) started to increase (03 = 4.2; 04 = 4.4; 05 = 4.7; 09 = 6.1)
Future Employment
Quarterly Seasonal Factors
1st Quarter
8.0
6.0
4.0
2.0
0.0
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
2nd Quarter
3rd Quarter
4th Quarter
Industrial Production
Quarterly Seasonal Factors for the Quantitative Indicator of the
National Bureau of Statistics (IBGE)
8.0
6.0
4.0
2.0
0.0
-2.0
-4.0
-6.0
-8.0
-10.0
Concluding Remarks
•
Questionnaire phrasing makes a difference. Seasonality appears more
pronounced in questions that imply some kind of comparison over time;
•
After four years, there is no sign that the collection of data in the other
months of the year, has changed the relative seasonality pattern of the
original months;
•
In 2008-2010 FGV is creating Services, Commerce and Building Surveys,
containing question phrasing that intend to correct seasonality from the
start. In a few years we will be able to analyse whether this measure was
successful in breaking or reducing seasonality patterns.
Thank You !