Help: Partitioned samples efficiency in OLS compared to one sample regression As usual, we can estimate by OLS the model (in matrix form) $Y=\alpha+\beta*X+u$ with a sample of $n+m$ observations. The OLS estimator is $\hat{\beta}=(X^{T}X)^{-1}X^{T}Y$. Now, if we partition our sample in two subsamples A and B of sizes n and m, respectively, we could get the OLS estimators for each sumbsample: $\hat{\beta_A}=(X_A^{T}X_A)^{-1}X_A^{T}Y_A$ and $\hat{\beta_B}=(X_B^{T}X_B)^{-1}X_B^{T}Y_B$. We know both of these estimators are unbiased, and their variance is (assuming homoscedasticity) equal to $\sigma^2(X_i^{T}X_i)^{-1}$. Now let's define $\tilde{\beta}=\frac{\hat{\beta_A}+\hat{\beta_B}}{2}$, which is also unbiased, and, assuming no serial correlation, its variance is $V(\tilde{\beta})=\frac{V(\hat{\beta_A})+V(\hat{\beta_B})}{2}$.
How could we prove this estimator $\tilde{\beta}$ is less efficient/worse than $\hat{\beta}$?

This follows from some basic properties of quadratic forms.  Let's get those out of the way first.
Fix a finite dimension $k$ and suppose $A$ and $B$ are both symmetric positive-definite (SPD) $k\times k$ matrices.  This (easily) implies $A+B$ is symmetric positive-definite and all three of $A,$ $B,$ and $A+B$ are invertible, whence their inverses are SPD, too.
Lemma: For all vectors $u,$ $$u^\prime (A+B)^{-1} u \le \frac{1}{2} u^\prime(A^{-1}+B^{-1})u.$$
Proof: Let $v = (A+B)^{-1}u.$  Because $A^{-1}$ and $B^{-1}$ are SPD, $v^\prime BA^{-1}Bv = (Bv)^\prime (A^{-1}) (Bv) \ge 0$ and likewise $v^\prime A B^{-1} A v \ge 0.$
Therefore
$$\begin{aligned}
u^\prime (A+B)^{-1} u &= v^\prime (A+B)(A+B)^{-1}(A+B) v\\
&= v^\prime(A+B)v\\
&\le v^\prime(A+B) v + \frac{1}{2}\left(v^\prime(A+B) v + v^\prime BA^{-1}B v + v^\prime AB^{-1}Av\right)\\
&= \frac{1}{2}v^\prime(A+B)(A^{-1}+B^{-1})(A+B)v\\
&= \frac{1}{2}u^\prime (A^{-1} + B^{-1})u,
\end{aligned}$$
QED.
Apply this to $A = X_A^\prime X_A$ and $B = X_B^\prime X_B.$  Because $u$ represents an arbitrary linear combination of parameter estimates, conclude that the variance of any linear combination of parameter estimates using the second estimator $\tilde \beta$ is never less than its variance using the OLS estimator $\hat\beta.$
