bold math

[Jeff Pitblado (StataCorp)]

Playing with bold in math expressions:

Here is the code

\[
M \ne \mathbf{M}
\]

Here is the result
\[
M \ne \mathbf{M}
\]

[Jose Fuentes]

\[ ln(PP_{ij})=\tilde{\beta}_1 Z_{ij} + \tilde{\beta_2} X_{ij} + {FE} +\tilde{\varepsilon}_{ij}\]


\[ Y_{ijt}=exp(\beta_1 ln(PP_{ij}) + \widehat{\tilde{\varepsilon}_{ij}} + \beta_2 X_{ijt} + {FE}) +\varepsilon_{ijt} \]

[Aidan Sloyan]

test

\[ m_{it} = \sum\gamma_j . D_{i,t-j} + \alpha_i + \delta_t + \beta.X_{it} + \epsilon_{it} \]

Where
\[ m_{it}, D_{i,t-j}, \alpha_i, \delta_t , X_{it} \] are out-out-pocket spending, event time dummies, individual fixed effects, time (e.g., year-month) fixed effects, and other covariates, respectively.

[Romano Tarsia]

\[
\beta_1 T_{i,t} - \beta_1 T_{i,t-1} = \beta_1 (T_{i,t} - T_{i,t-1})
\]

[Stefan Kreisel]

`icc` test

[Stefan Kreisel]

\[ r = {\sigma^2_\text{Between} \over \sigma^2_\text{Between} + \sigma^2_\text{Error}} \]

[mariofiorini]

test \[ (e^{\beta}-1) = \frac{\mathbb{E}[Y_{it}(1)\mid \text{Treat,Post}]-\mathbb{E}[Y_{it}(0)\mid \text{Treat,Post}]}{\mathbb{E}[Y_{it}(0)\mid \text{Treat,Post}]} \]

[Chengmou Lei]

trying within line math symbol:the treatment effect (\[delta_q\] in the following model).

[Chengmou Lei]

just trying:
\[TFPQ = \sum_{q=-3}^{+3}\delta_q\mathbb{I}\mathbb{I}\left \{ t-EventYear=q \right \}\times Treat+\theta+\epsilon \]

[Xavier Pedros]

\[\frac{dY_{i}}{dEUF_{i}} = \frac{\gamma}{\frac{EUF_{i}}{Y_{i}}} \]

[Xavier Pedros]

test test \[y_{it} = \mu_i + \theta y_{i,t-1} + \varepsilon_{it}\]

\[ln (Y_{i,t}) = \beta_0 + \beta_1 ln (Y_{i,t-1}) + \beta_2 ln(S_{i,t}) + \beta_3 ln(P_{i,t}) + \sum_{j=1}^{5} \gamma_j ln (EUF_{i,t-j}) + \mu_{i} + \tau_{t} + u_{it} \]

[Sam Rickman]

\[ \begin{bmatrix}
\sigma^2_{u0} \\
\sigma_{u0,u1} & \sigma^2_{u1} \\
\sigma_{u0,u2} & \sigma_{u1,u2} & \sigma^2_{u2}
\end{bmatrix} \]

\[ \begin{bmatrix}
1.71368 & & & & & & & \\
0.756977 & 1.57591 & & & & & & \\
0.529473 & 0.786411 & 2.08232 & & & & & \\
0.491731 & 0.558595 & 0.933849 & 2.13161 & & & & \\
0.276758 & 0.342848 & 0.638271 & 0.821977 & 1.58599 & & & \\
0.170209 & 0.260075 & 0.510564 & 0.435605 & 0.899372 & 1.92696 & & \\
0.205027 & 0.248522 & 0.631041 & 0.556762 & 0.742223 & 1.02729 & 2.1866 & \\
0.060675 & 0.164207 & 0.300921 & 0.441473 & 0.58498 & 0.639033 & 1.20417 & 2.22011
\end{bmatrix} \]

[Sam Rickman]

It doesn't work in Preview so I am posting to see if it works \[ M \ne \mathbf{M} \]

\[ \frac{2}{3}+ \mathbf{\pi} \]
\[ M \ne \mathbf{M} \]

\[ y_{ij} = \beta_{0j} + \beta_{1j} mo1 + \beta_{2j} mo2 + e_ij \\
\beta_{0j} = \beta_0 + u_{0j} \\
\beta_{1j} = \beta_1 + u_{1j} \\
\beta_{2j} = \beta_1 + u_{2j} \]

[Leon Faerber]

\[
\Delta lncaputil_t = u_t + \beta_t intv_t
\\
u_t = \alpha u_{t-1} + \epsilon_t
\\
\beta_t = \beta_{t-1} + v_t
\]

[Owen Wallbanks]

Just trying...

\[
married_{ijk} = \alpha+\beta_1 sibsize_{jk}+\beta_2 \textbf{z}_{jk}+\beta_3 \textbf{x}_{ijk}+e_k+c_{jk}+\epsilon_{ijk}
\]

\[
married_{ijk} = \alpha+\beta_1 sibsize_{jk}+\beta_2 \textbf{z}_{jk}+\beta_3 \textbf{x}_{ijk}+e_k+c_{jk}+\epsilon_{ijk}
\]

\[
\Delta married_{ijk} = \beta_1 \Delta sibsize_{jk}+\beta_2 \Delta \textbf{z}_{jk}+\beta_3 \Delta \textbf{x}_{ijk}+c_{jk}+\Delta \epsilon_{ijk}
\]

\[
\Delta married_{ijk} = \beta_1 \Delta sibsize_{jk}+\beta_2 \Delta \textbf{z}_{jk}+\beta_3 \Delta \textbf{x}_{ijk}+c_{jk}+\Delta \epsilon_{ijk}
\]