The (real) GDP per capita in Belgium (in €) is provided for several years in the table below: year GDP 2010 33 509.07 2011 33 641.30 2012 33 443.71 2013 33 259.76 2014 33 728.14 2015 34 028.24 2016 34 209.76 a. Let us denote GDP in year t by Yt. Determine the percentage change for each year using the formula rt= Y₁-Y-1. Compare the results to the log-returns In (₁).

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10. The (real) GDP per capita in Belgium (in €) is provided for several years in the table below:
year
GDP
2010
33 509.07
2011
33 641.30
2012 33 443.71
2013
33 259.76
2014
33 728.14
2015
34 028.24
2016
34 209.76
a. Let us denote GDP in year t by Yt. Determine the percentage change for each year using the
formula rt =
Y-Y-1. Compare the results to the log-returns In ().
1
b. You are also given that Y1990 = 25 039.06. Again compare the percentage change over the period
1990-2016 with the corresponding log-return.
There are several reasons why percentage changes are often approximated by log-returns. One of the
reasons is that the logarithm has nice mathematical properties. For example: log-returns allow for
easier time aggregation as is illustrated in parts c. and d. below.
c. Show that the percentage change Y2016-Y2013 over the three-year period from 2013 to 2016 equals
(1+r2016) (1+r2015)(1+r2014) - 1.
d. Show that the log-return In (Y2016) for the same period is simply the sum of the yearly log-returns.
Transcribed Image Text:10. The (real) GDP per capita in Belgium (in €) is provided for several years in the table below: year GDP 2010 33 509.07 2011 33 641.30 2012 33 443.71 2013 33 259.76 2014 33 728.14 2015 34 028.24 2016 34 209.76 a. Let us denote GDP in year t by Yt. Determine the percentage change for each year using the formula rt = Y-Y-1. Compare the results to the log-returns In (). 1 b. You are also given that Y1990 = 25 039.06. Again compare the percentage change over the period 1990-2016 with the corresponding log-return. There are several reasons why percentage changes are often approximated by log-returns. One of the reasons is that the logarithm has nice mathematical properties. For example: log-returns allow for easier time aggregation as is illustrated in parts c. and d. below. c. Show that the percentage change Y2016-Y2013 over the three-year period from 2013 to 2016 equals (1+r2016) (1+r2015)(1+r2014) - 1. d. Show that the log-return In (Y2016) for the same period is simply the sum of the yearly log-returns.
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