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 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)

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)

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

Level of Stocks Indicator * Slight or no seasonal pattern 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

Testing Interpolation

LCU – Original and Adjusted

Choosing the Interpolation Method

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

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

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

Level of Demand Comments: No changes across time

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)

Future Employment

Industrial Production

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 !