<p>The language &quot;holding fixed other regressors&quot; is heuristic (but not problematic).
Consider <span class="math-container">$$y_i =\beta_0 +\beta_1 x_{1i} +\beta_2 x_{2i}+ u_i$$</span></p>
<p>The OLS estimate of <span class="math-container">$\beta_1$</span> is the same as from the regression,</p>
<p><span class="math-container">$$y_i =\beta_0 +\beta_1 \widetilde{x_{1i}}+ u_i$$</span></p>
<p>where <span class="math-container">$\widetilde{x_{1i}}$</span> are residuals from <span class="math-container">$x_{1i} =\delta_0 +\delta_1 x_{2i}+\widetilde{x_{1i}}$</span>.  This is an application of the Frisch-Waugh-Lovell theorem.</p>
<p>Because OLS residuals are by construction uncorrelated with regressors, we know that <span class="math-container">$Cov(\widetilde{x_{1i}}, x_{2i})=0$</span>. Thus, what OLS is really doing is creating a version of a regressor that is uncorrelated with the controls (<span class="math-container">$\widetilde{x_{1i}}$</span> above), and using variation in that version. When that version of the variable changes, the other controls don't change on average (due to uncorrelatedness) and are &quot;held fixed&quot;.</p>
<p>Some formal derivations exist that are more in the style of what you are asking. In chapter 3 of Mostly Harmless Econometrics, Angrist and Pischke consider the setting in which <span class="math-container">$x_{1i}$</span> is binary. Define <span class="math-container">$\delta_v$</span> as the average effect of <span class="math-container">$x_1$</span> on <span class="math-container">$y$</span> when <span class="math-container">$x_{2i}=v$</span></p>
<p><span class="math-container">$$\hat{\beta_1} = \frac{\sum_{v}\delta_v[P(x_{1i}=1|x_{2i}=v)(1-P(x_{1i}=1|x_{2i}=v))]P(x_{2i}=v)}{\sum_{v}[P(x_{1i}=1|x_{2i}=v)(1-P(x_{1i}=1|x_{2i}=v))]P(x_{2i}=v)}$$</span></p>

CC BY-SA 4.0
2022-05-04T14:12:45.333Z
2022-05-04T14:12:45.333Z
Michael Gmeiner
<p>Suppose that we regress a variable <span class="math-container">$Y$</span> on two independent variables <span class="math-container">$X_1$</span> and <span class="math-container">$X_2$</span>. Since we have included <span class="math-container">$X_2$</span> in the regression, the obtained <span class="math-container">$\hat{\beta_1}$</span> coefficient is often said to be the effect of <span class="math-container">$X_1$</span> on <span class="math-container">$Y$</span> ‘holding <span class="math-container">$X_2$</span> fixed’ or 'controlling for <span class="math-container">$X_2$</span>'. This is then said to eliminate any omitted variable bias that is due to <span class="math-container">$X_2$</span>.</p>
<p>To investigate this more closely, observe that we can hold <span class="math-container">$X_2$</span> fixed at a variety of different values. Restrict attention to those values that reappear at least once across the dataset (e.g. because there are two datapoints for which <span class="math-container">$X_{2i} = 5$</span>). In such cases, we can conduct a simple regression of <span class="math-container">$Y$</span> on <span class="math-container">$X_1$</span> solely using those datapoints for which (say) <span class="math-container">$X_{2i} = 5$</span>. We can then repeat this regression for all possible <span class="math-container">$X_2$</span> values (assume they reappear at least once). It is natural to conjecture that our obtained <span class="math-container">$\beta_2$</span> estimate should be similar (identical?) to the average of the <span class="math-container">$\beta_2$</span> coefficients obtained from our simple regressions. Is this indeed the case?</p>

<econometrics>
Multiple regression and "holding variables fixed"
afreelunch
# Multiple regression and "holding variables fixed"

(Asked by: username_0 on 2022-05-04)

Suppose that we regress a variable $Y$ on two independent variables $X\_1$ and $X\_2$. Since we have included $X\_2$ in the regression, the obtained $\hat{\beta\_1}$ coefficient is often said to be the effect of $X\_1$ on $Y$ ‘holding $X\_2$ fixed’ or 'controlling for $X\_2$'. This is then said to eliminate any omitted variable bias that is due to $X\_2$.

To investigate this more closely, observe that we can hold $X\_2$ fixed at a variety of different values. Restrict attention to those values that reappear at least once across the dataset (e.g. because there are two datapoints for which $X\_{2i} = 5$). In such cases, we can conduct a simple regression of $Y$ on $X\_1$ solely using those datapoints for which (say) $X\_{2i} = 5$. We can then repeat this regression for all possible $X\_2$ values (assume they reappear at least once). It is natural to conjecture that our obtained $\beta\_2$ estimate should be similar (identical?) to the average of the $\beta\_2$ coefficients obtained from our simple regressions. Is this indeed the case?

**Tags:** econometrics


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## 1 answers

### Answer by username_1 on 2022-05-04. Score: 4 | Accepted answer

The language "holding fixed other regressors" is heuristic (but not problematic).
Consider $$y\_i =\beta\_0 +\beta\_1 x\_{1i} +\beta\_2 x\_{2i}+ u\_i$$

The OLS estimate of $\beta\_1$ is the same as from the regression,

$$y\_i =\beta\_0 +\beta\_1 \widetilde{x\_{1i}}+ u\_i$$

where $\widetilde{x\_{1i}}$ are residuals from $x\_{1i} =\delta\_0 +\delta\_1 x\_{2i}+\widetilde{x\_{1i}}$. This is an application of the Frisch-Waugh-Lovell theorem.

Because OLS residuals are by construction uncorrelated with regressors, we know that $Cov(\widetilde{x\_{1i}}, x\_{2i})=0$. Thus, what OLS is really doing is creating a version of a regressor that is uncorrelated with the controls ($\widetilde{x\_{1i}}$ above), and using variation in that version. When that version of the variable changes, the other controls don't change on average (due to uncorrelatedness) and are "held fixed".

Some formal derivations exist that are more in the style of what you are asking. In chapter 3 of Mostly Harmless Econometrics, Angrist and Pischke consider the setting in which $x\_{1i}$ is binary. Define $\delta\_v$ as the average effect of $x\_1$ on $y$ when $x\_{2i}=v$

$$\hat{\beta\_1} = \frac{\sum\_{v}\delta\_v[P(x\_{1i}=1|x\_{2i}=v)(1-P(x\_{1i}=1|x\_{2i}=v))]P(x\_{2i}=v)}{\sum\_{v}[P(x\_{1i}=1|x\_{2i}=v)(1-P(x\_{1i}=1|x\_{2i}=v))]P(x\_{2i}=v)}$$


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