2. 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

3. 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.

4. 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.

5. 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.

6. 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)

7. Reasons for Seasonality
Type of question
Month of the Survey
Previous Quarter
Following Quarter
Actual Results
Forecasts
Examples:
Exception:
Future Production
Future Employment
Future Prices
Furture Business Situation
(compared to same semester, last year)

8. Reasons for Seasonality
Type of variable
Example: Level of Capacity Utilisation (reference to the month of the survey, multiple choice) 0%
1% to 20%
...
80% to 89%
90% to 99%
100%
9 options:
Other Example:
Exception:
Level of Demand
Level of Stocks
Present Business Situation
(slight seasonal pattern)

9. Future Production Indicator *
Strong seasonal pattern
Quarterly Data from Jan.05 to Jul.09 * Indicator = Balance + 100

10. Level of Stocks Indicator *
Slight or no seasonal pattern
Quarterly Data from Jan.05 to Jul.09 * Indicator = Balance + 100

11. 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.

12. Adjusting the short time monthly series

13. Testing Interpolation

14. LCU – Original and Adjusted

15. Choosing the Interpolation Method

16. Evaluating Seasonality after four years of Monthly Frequency (Nov. 05)

17. Level of Capacity Utilisation
Comments:
Factors change along time but have stabilised in the ’00 decade, specially after 2002

18. Future Production Comments:
Seasonal factors are continuously changing but there are no signs of strutuctural changes around 2005

19. Level of Demand
Comments:
No changes across time

20. Future Employment 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)

21. Future Employment

22. Industrial Production

23. 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 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.

24. Thank You !