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Author:Bragoli, Daniela 

Working Paper
The Importance of Updating: Evidence from a Brazilian Nowcasting Model
How often should we update predictions for economic activity? Gross domestic product is a quarterly variable disseminated usually a couple of months after the end of the quarter, but many other macroeconomic indicators are released with a higher frequency, and financial markets react very strongly to them. However, most of the professional forecasters, including the IMF, the OECD, and most central banks, tend to update their forecasts of economic activity only two to four times a year. The main exception is the Central Bank of Brazil which is responsible for collecting and publishing a daily survey on GDP and other variables. The aim of this article is to evaluate the forecasting performance of the Central Bank of Brazil Survey and to compare it with the mechanical forecasts based on state-of-the-art nowcasting techniques. Results indicate that institutional forecasts perform as well as model-based forecasts. The latter finding suggests that, on the one hand, judgmental forecasters do not have computational limitations and are able to incorporate very quickly new information in a way that is as efficient as a machine. On the other hand, it confirms what has been found in other studies, namely that a linear time invariant model does a good job and hence that eventual nonlinearities, time variations and soft information (such as weather conditions or government decisions) that could be incorporated by judgment, do not provide new important information.
AUTHORS: Modugno, Michele; Bragoli, Daniela; Metelli, Luca
DATE: 2014-11-04

Working Paper
A Nowcasting Model for Canada: Do U.S. Variables Matter?
We propose a dynamic factor model for nowcasting the growth rate of quarterly real{{p}}Canadian gross domestic product. We show that the proposed model produces more accurate nowcasts than those produced by institutional forecasters, like the Bank of Canada, the The Organisation for Economic Co-operation and Development (OECD), and the survey collected by Bloomberg, which reflects the median forecast of market participants. We show that including U.S. data in a nowcasting model for Canada dramatically improves its predictive accuracy, mainly because of the absence of timely production data for Canada. Moreover, Statistics Canada produces a monthly real GDP measure along with the quarterly one, and we show how to modify the state space representation of our model to properly link the monthly GDP with its quarterly counterpart.
AUTHORS: Bragoli, Daniela; Modugno, Michele
DATE: 2016-04


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