Question

The OECD cut forecasts again for the global economy in 2019 and 2020, following on from previous downgrades in November, as it warned that trade disputes and uncertainty over Brexit would hit world commerce and businesses. The Organization for Economic Co- Operation & Development forecast in its interim outlook report that the world economy would grow 3.3 percent in 2019 and 3.4 percent in 2020. Those forecasts represented cuts of 0.2 percentage points for 2019 and 0.1 percentage points for 2020, compared to the OECD’s last set of forecasts in November. “High policy uncertainty, ongoing trade tensions, and a further erosion of business and consumer confidence are all contributing to the slowdown,” said the OECD in its report. “Substantial policy uncertainty remains in Europe, including over Brexit. A disorderly exit would raise the costs for European economies substantially,” added the OECD. Europe remains impacted by uncertainty over Britain’s plans to exit the European Union, the U.S. - China trade spat and other weak spots, such as signs of a recession in Italy. For Germany, Europe’s largest economy, the OECD more than halved its 2019 GDP growth forecast to 0.7 percent from 1.6 percent previously. It predicted a light recovery to 1.1 percent growth in 2020. Germany’s export-reliant economy is particularly affected by weaker global demand and rising trade barriers.

(Source: https://www.reuters.com/article/us-oecd-economy/global-economic-growthforecasts- cut-again-by-oecd-idUSKCN1QN13N)

a. In your opinion what forecasting tools are used in the above case for forecasting the global economy? Justify. (5 Marks)

b. Are the forecasts accurate? Comment. What all errors are possible in the forecasts and how to deal with such errors? (5 Marks)

Answer 1

a.

Whenever there is forecasting done about economic parameters of country or a region or world then there are multiple factors which are working or changing at the same time so multiple linear regression is used. And as there is calculation of difference in data along different time periods so time-series methods can also be used.

b.

Forecasting is rarely accurate because of the errors associated with it. In multiple linear regression, there are high chances of error because only few factors are considered as variable while others are considered constant which is not true in reality causing error. Similarly there are residual errors in time series as well because the behavior of any variable can change which is predicted or assumed to be constant while analysis.