<p>I do not know where you got the forecasts for GDP, but it is very likely they do not include a forecast on how PPPs are changing. As such, a computation of the rate of growth of GDP in 2005 PPP is a mreasure of <strong>real GDP growth</strong>.</p>

<p>Hence, what you want to do is to concatenate two series. For this, take the latest GDP <strong>level</strong> of each country from the World Bank series in 2011 prices, and <strong>extend it</strong> using the forecasted GDP <strong>growth rates</strong> in 2005 prices, calculated from your database of forecast. Naturally you only want to forecast GDP for the years that are missing in the World Bank database. The reliability of this exercise depends on how outdated the forecasts from the 2005 database are.</p>

CC BY-SA 3.0
2017-10-10T16:56:15.953Z
I think I follow, but would you be able to use a numerical example (e.g. with the numbers I provided) so that I can be sure?
Anthony S.
CC BY-SA 4.0
2019-03-09T04:46:46.913Z
@AnthonyS. forecasting involves logging the GDP values, then finding the linear line of best fit through those values. The forecasted values are the ones the trendline predicts for future values (exponentiate to get the GDP values again).
ahorn
CC BY-SA 3.0
2017-09-19T09:29:34.580Z
2017-09-19T09:29:34.580Z
luchonacho
<p>Essentially I have two different datasets that I'm trying to consolidate. One set of data comes from the <a href="http://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD" rel="nofollow noreferrer">World Bank Databank</a>, and is a set of historical GDP PPP values for all countries expressed in 2011 international \$. The other dataset is a set of forecasted GDP values for all countries, and is expressed in GDP PPP expressed in 2005 international $. I would like to convert one of these sets into the same units as the other. </p>

<p>Is this possible? If so, how would I do this?</p>

<p>The latter dataset has some historical values, so I've tried dividing the 2011\$ by the 2005$ for different countries, but have found that the ratio is different in all cases.</p>

<p>Eg.</p>

<p>China - GDP 2008 in \$2005: 7,566.755billion;  GDP 2005 in \$2011: 10,437.906billion - Ratio: 1.38</p>

<p>Canada - GDP 2008 in \$2005: 1,197.757billion;  GDP 2005 in $2011: 1,383.398billion - Ratio: 1.15</p>

CC BY-SA 3.0
2017-09-27T09:16:16.443Z
Let me know if my answer was not helpful/clear enough, to modify.
luchonacho
CC BY-SA 4.0
2018-07-16T15:47:28.190Z
You could use the PPP conversion factor available from the same source. There is more than one conversion factor, and which is more appropriate could depend on what you're analyzing. The differences between the two could lead to some comments. However, you may have to restrict the period of analysis, or remove some country observations, due to unavailability of this conversion factor for some countries. A variety of other methods could be used, but this seems to be the direction you're going in.
nathanwww
CC BY-SA 4.0
2019-03-09T04:50:19.953Z
I don't see the sense in dividing GDP in 2008 by GDP in 2005. Those ratios will obviously differ across countries due to their different growth rates.
ahorn
<currency><gdp><purchasing-power-partity>
Conversion of GDP PPP International \$ 2005 into GDP PPP International \$ 2011
Anthony S.
# Conversion of GDP PPP International \$ 2005 into GDP PPP International \$ 2011

(Asked by: username_0 on 2016-12-02)

Essentially I have two different datasets that I'm trying to consolidate. One set of data comes from the [World Bank Databank](http://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD), and is a set of historical GDP PPP values for all countries expressed in 2011 international \$. The other dataset is a set of forecasted GDP values for all countries, and is expressed in GDP PPP expressed in 2005 international $. I would like to convert one of these sets into the same units as the other.

Is this possible? If so, how would I do this?

The latter dataset has some historical values, so I've tried dividing the 2011\$ by the 2005$ for different countries, but have found that the ratio is different in all cases.

Eg.

China - GDP 2008 in \$2005: 7,566.755billion; GDP 2005 in \$2011: 10,437.906billion - Ratio: 1.38

Canada - GDP 2008 in \$2005: 1,197.757billion; GDP 2005 in $2011: 1,383.398billion - Ratio: 1.15

**Tags:** currency, gdp, purchasing-power-partity

> **Comments:**
> **username_1**: Let me know if my answer was not helpful/clear enough, to modify.  
> **username_2**: You could use the PPP conversion factor available from the same source. There is more than one conversion factor, and which is more appropriate could depend on what you're analyzing. The differences between the two could lead to some comments. However, you may have to restrict the period of analysis, or remove some country observations, due to unavailability of this conversion factor for some countries. A variety of other methods could be used, but this seems to be the direction you're going in.  
> **username_3**: I don't see the sense in dividing GDP in 2008 by GDP in 2005. Those ratios will obviously differ across countries due to their different growth rates.  

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## 1 answers

### Answer by username_1 on 2017-09-19. Score: 1

I do not know where you got the forecasts for GDP, but it is very likely they do not include a forecast on how PPPs are changing. As such, a computation of the rate of growth of GDP in 2005 PPP is a mreasure of **real GDP growth**.

Hence, what you want to do is to concatenate two series. For this, take the latest GDP **level** of each country from the World Bank series in 2011 prices, and **extend it** using the forecasted GDP **growth rates** in 2005 prices, calculated from your database of forecast. Naturally you only want to forecast GDP for the years that are missing in the World Bank database. The reliability of this exercise depends on how outdated the forecasts from the 2005 database are.

> **Comments:**
> **username_0**: I think I follow, but would you be able to use a numerical example (e.g. with the numbers I provided) so that I can be sure?  
> **username_3**: @AnthonyS. forecasting involves logging the GDP values, then finding the linear line of best fit through those values. The forecasted values are the ones the trendline predicts for future values (exponentiate to get the GDP values again).  

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