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Depec-Bradesco Economic Highlights

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Year XIII Number 139 - March, 31 2016

Bradesco Global Indicator – a real time estimate of global GDP

Daniela Cunha de Lima

We are presenting Bradesco’s Global Indicator (BGI) which seeks to estimate the current GDP and we would like to suggest that it as a more timely alternative to the quarterly global GDP forecast. Based on more than 100 indicators from about 20 countries and global aggregate indicators, we use the principal components technique to reach a global GDP ‘nowcast’ of current and past quarters. The nowcast term is a fusion of the words now+forecast and its purpose is to track the current GDP, and not to make a prediction for the future GDP. The advantage of this nowcast against projections is the fact that it is possible to have, after the disclosure of every indicator, an overview of the current global growth rate, based on all information available to date. As a result, its global GDP forecast’s capacity is limited to the current and past quarters – that is, for the quarters of which indicators are already available.

The motivation for this new indicator comes from the challenging conditions of the current global economic scenario. An indicator that summarizes the information from several global variables can be a good guide to better understand and monitor

this scenario and, therefore, help us to contextualize the Brazilian economy.

There are several global aggregate indicators that are used as predictors of global GDP. Information such as the PMI index of the manufacturing industry and the service sector, the performance of the industrial production and of world trade, among others, have high correlation with the behavior of global GDP. Considering this, several models to estimate the performance of the GDP use aggregate indicators in their projections. The following table shows the correlation between some international indicators. For example, the PMI indicator of manufactured products is correlated by 0.89 with global industrial production, while the correlation with GDP is 0.79 (naturally a smaller figure, since GDP is not only composed of the industrial sector). Our BGI nowcast uses these indicators, but, unlike conventional models, it uses them in an optimal and time varying way. When compared with other traditional models, the BGI has a smaller mean square error and lower variation – which indicates its better predictive capability in relation to others.

Historical correlation PMIm PMIs PIM World Trade Confidence Auto GDP

PMI, manufactured (PMIm) 1.00 0.89 0.78 0.75 0.38 0.73 0.79PMI, services (PMIs) 0.89 1.00 0.67 0.63 0.29 0.57 0.75Industrial production (PIM) 0.78 0.67 1.00 0.92 0.45 0.75 0.92 World Trade 0.75 0.63 0.92 1.00 0.40 0.75 0.85Confidence 0.38 0.29 0.45 0.40 1.00 0.35 0.45Vehicle Sales 0.73 0.57 0.75 0.75 0.35 1.00 0.70GDP (annualized margin) 0.79 0.75 0.92 0.85 0.45 0.70 1.00

Mean Square Error Variation

Bradesco Global Indicator 0.147 0.02Aggregate Model* 0.326 0.17PMI Model 0.368 0.15

Models Comparison since 2010

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For the construction of the BGI, we use indicators such as confidence surveys, activity indicators (industrial production, retail sales, energy consumption), and job market and external sector data. We do not use financial variables or prices. The selection of indicators considers not only the relevance and adherence to global GDP, but also their timing in disclosure. The variables are transformed to induce the stationary of the series (that means, for example, not to consider their tendency) and to then aggregate on a quarterly basis in order to project quarterly GDP.

Due to the large number of variables, estimation of common regressions would limit the model’s freedom levels and would result in inaccurate parameters. We explore the relationship between the variables and econometric techniques to summarize the information in a few common factors that capture the dynamics of indicators. This way, we are able to estimate a parsimonious model with better prediction capacity. Common dynamic factors are unobserved variables and, in literature, are estimated from Kalman filter technique1 and Principal Components2. We follow the methodology of Giannone, D., Reichlin, L., & Small, D. (2008).

Monthly PMI Projections

Bridge Equations

Model’s update process from the disclosure of an indicator

The table above illustrates the model’s update process. Through the Kalman filter, the missing monthly indicators are projected in order to be used as input for the quarterly GDP forecasts. Please

note that, the more information we have related to the quarter, the more accurate the GDP forecast will be, since the intermediate projections are less necessary.

Bradesco Global Indicator versus global GDP (seasonally adjusted and annualized quarterly variation)

1 The Kalman filter is a recursive process used to project unobserved variables.2 The principal component analysis aims to reduce the dimensionality of a set of information.

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We can see, in the above graph, that the forecast is a step ahead of BGI in relation to GDP. It is possible to observe that it has good correlation to global GDP, especially during the 2008 crisis and first quarter recovery. Based on the economic indicators to date, the Bradesco Global Indicator suggests a stability in global GDP growth, compared to that observed in the fourth quarter of 2015 in annualized terms. We can notice the evolution of the forecasts over the quarter in the following chart. In January, for example, only few indicators that reflect the activity of the first quarter had been disclosed – for example, US jobless claims. By the first half of March, indicators such as the PMI for January and February, January’s industrial production of some countries, job market data from January and February, as well as confidence indicators of the first two months,

among others, are already available. With more information, it is possible to make increasingly accurate predictions.

Although the BGI nowcast suggest an increase of around 2.5% in the first quarter of the year, we still believe that the coming quarters will show some recovery, mainly reflecting the better dynamic of some mature economies – like the United States, which should present better growth in the coming quarters in order to grow 2% this year – as well as of some emerging economies, especially Brazil, which will go from a decrease of -2.8% in annualized terms in the first quarter compared to the previous quarter, to an increase of 0.8% in the fourth quarter. In this sense, we maintain our growth forecast of 3% for global GDP growth this year.

Bradesco Global Indicator* (1Q16) versus global GDP (projection for the year)

(*) seasonally adjusted and annualized quarterly variation

ReferencesGiannone, D., Reichlin, L., & Small, D. (2008). Nowcasting:

The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665-676.

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Bradesco Indicador Global - Evolução Projeção PIB global saar - 1º trimestre de 2016

Projection for the yearBGI

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Octavio de Barros - Macroeconomic Research DirectorMarcelo Cirne de Toledo Global economics: Fabiana D’Atri / Felipe Wajskop França / Thomas Henrique Schreurs Pires / Ellen Regina Steter Brazil: Igor Velecico / Estevão Augusto Oller Scripilliti/ Andréa Bastos Damico / Myriã Tatiany Neves Bast / Daniela Cunha de Lima / Ariana Stephanie ZerbinattiBrazilian sectors: Regina Helena Couto Silva / Priscila Pacheco TrigoProprietary survey: Leandro Câmara Negrão / Ana Maria Bonomi BarufiInternships: Gabriel Marcondes dos Santos / Wesley Paixão Bachiega / Carlos Henrique Gomes de Brito

Team

DEPEC - BRADESCO does not accept responsibility for any actions/decisions that may be taken based on the information provided in its publications and projections. All the data and opinions contained in these information bulletins is carefully checked and drawn up by fully qualified professionals, but it should not be used, under any hypothesis, as the basis, support, guidance or norm for any document, valuations, judgments or decision taking, whether of a formal or informal nature. Therefore, we emphasize that all the consequences and responsibility for using any data or analysis contained in this publication is assumed exclusively by the user, exempting BRADESCO from all responsibility for any actions resulting from the usage of this material. We all point out that access to this information implies acceptance in full of this term of responsibility and usage. The reproduction of the content in this report (partially or in full) is strictly forbidden except if authorized by BRADESCO or if the sources (the name of the authors, publication and BRADESCO) are strictly mentioned.