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Nowcasting Norwegian GDP : the hard, the soft and the uncertainty data

Semjonovs, Andrejs
Master thesis
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URI
http://hdl.handle.net/11250/2561204
Date
2018
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  • Master Thesis [4657]
Abstract
This Master thesis investigates nowcasting, or predicting real time GDP, power of the

following series: i) hard data gauging real economy; ii) soft indicators reflecting business and

financial markets’ sentiment and iii) uncertainty measures depicting the overall uncertainty in

Norway. I employ approximate dynamic factor model, a framework acknowledged by

researchers and practitioners at central banks, to examine the predictive power of 209 variables

sorted in 15 blocks according to their economic content and release time. This thesis

documents that finance related variables are good in predicting the current state of the

economy. Due to their timely release and forward looking nature, they also perform well in

forecasting the economic growth over the following year. These findings suggest that finance

related variables are useful inputs for conducting timely and adequate monetary policy.

Uncertainty measures help to predict the contemporaneous economic growth rate as well. Real

variables like industrial production, while released with a lag and at a later date, add to the

precision of the nowcast the most.

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