<p>So I detrend 5 variables that I fit in an Almon model to predict GDP. The problem is beforehand I detrend them and their values become much smaller. Also, I detrend the GDP itself and it becomes smaller. I face these issues.</p>
<ol>
<li>Detrending in R makes me lose observations.</li>
<li>Adding new data in the future would be hand as it will shift the trend coefficient.</li>
<li>When GDP change is detrended is becomes very very small, like 0.002 in some cases. When I make predictions of lets say 0.005 for the future they would be hand to interpret as the real change will be lets say 0.03 values that are not in the train or test data (due to the values becoming too small after the decomposition). Because of this my predictions will be hard to interpret and always far away from the real data.</li>
</ol>

CC BY-SA 4.0
2024-03-04T02:14:37.903Z
For clarification: are you looking at **levels** and removing the trend, or are you looking at **growth rates** and removing that trend?
Daniel
CC BY-SA 4.0
2024-03-07T17:10:38.543Z
Can you show some of your data?  How many years is the detrend?  You should do a reasonability check of the results.  Is 0.02 meaning billions of dollars?  How did you arrive at the trend figures?  Trends may not be constant across all years.  Can you show what you think the figures should be?  What are the magnitudes of the detrend figures?  So the factors become unreasonable at a certain point of compounding?  To add future data the trend needs to be to the same date so you either reverse the trend or you start over with all data at a new date.
Gwendolyn Anderson
CC BY-SA 4.0
2024-03-07T18:41:16.650Z
Here is a potential process to provide solutions.  Suppose you have 50 years annual data 1971-2020. Plot the data.  Observe any type of slope that might be present due to forces such as inflation or population growth.  Apply a detrend factor to 1970.  The detrend should set the data to look jagged but not sloped - jagged about a horizontal line approximately, if that is the goal of the detrend.  Try detrendint to 2009 Reexamine the selected trend to see which years it applies to.  It may change over time.
Gwendolyn Anderson
CC BY-SA 4.0
2024-03-11T19:52:21.540Z
So I should detrend year by year? Or do I do it every 5 years at a time? Could you answer this other question of mine as by your answer you seem to be knowledgeable in statistic modelling ?https://datascience.stackexchange.com/questions/128130/decomposition-and-scaling-and-their-effect-on-interpretability/128151#128151
J_Bake
<econometrics><statistics><time-series>
How to interpret detrended data in GDP modelling?
J_Bake
# How to interpret detrended data in GDP modelling?

(Asked by: username_0 on 2024-03-03)

So I detrend 5 variables that I fit in an Almon model to predict GDP. The problem is beforehand I detrend them and their values become much smaller. Also, I detrend the GDP itself and it becomes smaller. I face these issues.

1. Detrending in R makes me lose observations.
2. Adding new data in the future would be hand as it will shift the trend coefficient.
3. When GDP change is detrended is becomes very very small, like 0.002 in some cases. When I make predictions of lets say 0.005 for the future they would be hand to interpret as the real change will be lets say 0.03 values that are not in the train or test data (due to the values becoming too small after the decomposition). Because of this my predictions will be hard to interpret and always far away from the real data.

**Tags:** econometrics, statistics, time-series
> **Comments:**
> **username_1**: For clarification: are you looking at **levels** and removing the trend, or are you looking at **growth rates** and removing that trend?  
> **username_2**: Can you show some of your data?  How many years is the detrend?  You should do a reasonability check of the results.  Is 0.02 meaning billions of dollars?  How did you arrive at the trend figures?  Trends may not be constant across all years.  Can you show what you think the figures should be?  What are the magnitudes of the detrend figures?  So the factors become unreasonable at a certain point of compounding?  To add future data the trend needs to be to the same date so you either reverse the trend or you start over with all data at a new date.  
> **username_2**: Here is a potential process to provide solutions.  Suppose you have 50 years annual data 1971-2020. Plot the data.  Observe any type of slope that might be present due to forces such as inflation or population growth.  Apply a detrend factor to 1970.  The detrend should set the data to look jagged but not sloped - jagged about a horizontal line approximately, if that is the goal of the detrend.  Try detrendint to 2009 Reexamine the selected trend to see which years it applies to.  It may change over time.  
> **username_0**: So I should detrend year by year? Or do I do it every 5 years at a time? Could you answer this other question of mine as by your answer you seem to be knowledgeable in statistic modelling ?https://datascience.stackexchange.com/questions/128130/decomposition-and-scaling-and-their-effect-on-interpretability/128151#128151  

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