<p>I'm working with a time-series dataset where my dependent variable is measured annually from 2018-2021 but my covariates are not all measured over the same period. For instance, <span class="math-container">$x_1$</span> is measured from 2010-2016, <span class="math-container">$x_2$</span> from 2010-2019, etc.</p>
<p>I'd like to predict the value of <span class="math-container">$x_1$</span> for 2017-2021 and for <span class="math-container">$x_2$</span> for 2020 and 2021 while still propagating uncertainty in the predictions. What I've read about imputation seems geared to situations where there is missing data for <em>some</em> but not all observations and there isn't historical data. How is this issue typically addressed? Thanks!</p>

CC BY-SA 4.0
2022-02-14T20:57:33.590Z
I find it a bit hard to follow your explanation. You have only 4 values for your dependent variable? Essentially no values for your covariates (what are your independent variable(s))? How do you think you can get any (reliable) result with so little data? Ignoring that you don't actually have data for the variables you intend to use (assuming you would have that data).
AKdemy
CC BY-SA 4.0
2022-02-15T02:55:46.707Z
Sorry I was unclear, there are more than 50 observations each year, and the dependent variable for each subject is measured every year (so 50 values in 2018, 50 values in 2019, etc.)
akm
CC BY-SA 4.0
2022-02-17T08:05:39.920Z
Usually these things are best handled in 'State-space' approach using kalman filter.
Dayne
<econometrics><time-series>
Time series: predicting covariates for missing years from historical values
akm
# Time series: predicting covariates for missing years from historical values

(Asked by: username_0 on 2022-02-14)

I'm working with a time-series dataset where my dependent variable is measured annually from 2018-2021 but my covariates are not all measured over the same period. For instance, $x\_1$ is measured from 2010-2016, $x\_2$ from 2010-2019, etc.

I'd like to predict the value of $x\_1$ for 2017-2021 and for $x\_2$ for 2020 and 2021 while still propagating uncertainty in the predictions. What I've read about imputation seems geared to situations where there is missing data for *some* but not all observations and there isn't historical data. How is this issue typically addressed? Thanks!

**Tags:** econometrics, time-series
> **Comments:**
> **username_1**: I find it a bit hard to follow your explanation. You have only 4 values for your dependent variable? Essentially no values for your covariates (what are your independent variable(s))? How do you think you can get any (reliable) result with so little data? Ignoring that you don't actually have data for the variables you intend to use (assuming you would have that data).  
> **username_0**: Sorry I was unclear, there are more than 50 observations each year, and the dependent variable for each subject is measured every year (so 50 values in 2018, 50 values in 2019, etc.)  
> **username_2**: Usually these things are best handled in 'State-space' approach using kalman filter.  

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## 0 answers

