<p>I am estimating two different regression equations with two separate dependent variables. Each dependent variable has its own unique control variables, but they also share a similar regressor Ht.</p>
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<p>When estimating these two equations, Ht is statistically significant for each, but each equation has the opposite sign (one is negative, and one is positive).
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<p>In the long run, the dependent variable of one equation will have an impact on the dependent variable of the other equation (and will, therefore, be a long-run determinant of that variable).</p>
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<p>Hence, if I was wanting to measure the marginal impact of a change in Ht on Yb, it would be more complicated than just citing delta. That is because an increase in Ht will lead to an increase in Ya and a decrease in Yb. However, in the long term, the increase in Ya will also effect Yb.</p>
<p>Are there any econometric techniques that I could use to combine these impacts? It initially appeared that seemingly unrelated regressions (or some sort of system) may be useful, but I don't believe it will answer the marginal impact question that I am wanting to uncover.</p>
<p>Any guidance will be greatly appreciated, thanks.</p>

CC BY-SA 4.0
2022-11-22T21:06:32.630Z
what sort of data you have? In principle you could probably just use some simultaneous equation model or VAR or something like that. Judging by subscripts you only have time series data, is that right? Or is this some sort of panel?
1muflon1
CC BY-SA 4.0
2022-11-22T21:51:31.423Z
@1muflon1 yeah it is time series data
Matthew Farquhar
<macroeconomics><microeconomics><econometrics><regression>
The dependent variable of one equation could be a regressor in another
Matthew Farquhar
# The dependent variable of one equation could be a regressor in another

(Asked by: username_0 on 2022-11-22)

I am estimating two different regression equations with two separate dependent variables. Each dependent variable has its own unique control variables, but they also share a similar regressor Ht.

[![enter image description here](https://i.sstatic.net/OGpQT.png)](https://i.sstatic.net/OGpQT.png)

When estimating these two equations, Ht is statistically significant for each, but each equation has the opposite sign (one is negative, and one is positive).
[![enter image description here](https://i.sstatic.net/rFaId.png)](https://i.sstatic.net/rFaId.png)

In the long run, the dependent variable of one equation will have an impact on the dependent variable of the other equation (and will, therefore, be a long-run determinant of that variable).

[![enter image description here](https://i.sstatic.net/QgVC4.png)](https://i.sstatic.net/QgVC4.png)

Hence, if I was wanting to measure the marginal impact of a change in Ht on Yb, it would be more complicated than just citing delta. That is because an increase in Ht will lead to an increase in Ya and a decrease in Yb. However, in the long term, the increase in Ya will also effect Yb.

Are there any econometric techniques that I could use to combine these impacts? It initially appeared that seemingly unrelated regressions (or some sort of system) may be useful, but I don't believe it will answer the marginal impact question that I am wanting to uncover.

Any guidance will be greatly appreciated, thanks.

**Tags:** macroeconomics, microeconomics, econometrics, regression

> **Comments:**
> **username_1**: what sort of data you have? In principle you could probably just use some simultaneous equation model or VAR or something like that. Judging by subscripts you only have time series data, is that right? Or is this some sort of panel?  
> **username_0**: @username_1 yeah it is time series data  

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## 0 answers

