I have the following model:

$\log(y)=\beta_0 + \beta_1 x_1 + \beta_2 \log(x_2) + \beta_3 x_1 \log(x_2) $

In interpreting the % change of $y$ that corresponds with a 1% increase in $x_2$ at a specific value of $x_1$ (.68), is the following correct?

% change in y =

\begin{equation} \left[\left[\exp\left(\left(\beta_1.68\right)+\left(\beta_2 \log\left(1.01\right)\right)+\left(\beta_3 .68*log\left(1.01\right)\right)\right) * \log\left(1.01\right)\right] - 1\right] * 100 \end{equation}

I have the following model:

$\log(y)=\beta_0 + \beta_1 x_1 + \beta_2 \log(x_2) + \beta_3 x_1 \log(x_2) $

In interpreting the % change of $y$ that corresponds with a 1% increase in $x_2$ at a specific value of $x_1$ (.68), is the following correct?

% change in y =

\begin{equation} \bigg[\big[\exp\big(\beta_1.68\ + \beta_2 \log\left(1.01\right) + \beta_3 .68\log(1.01)\big) \times \log(1.01)\big] - 1\bigg] \times 100 \end{equation}

I have the following model:

log(y)=B0 + B1(x1) + B2(log(x2)) + B3(x1*log(x2))

In interpreting the % change of y that corresponds with a 1% increase in x2 at a specific value of x1 (.68), is the following correct?

% change in y = [[exp((B1*.68)+(B2log(1.01))+(B3.68*log(1.01))) * log(1.01)] - 1] * 100

I have the following model:

$\log(y)=\beta_0 + \beta_1 x_1 + \beta_2 \log(x_2) + \beta_3 x_1 \log(x_2) $

In interpreting the % change of $y$ that corresponds with a 1% increase in $x_2$ at a specific value of $x_1$ (.68), is the following correct?

% change in y =

\begin{equation} \left[\left[\exp\left(\left(\beta_1.68\right)+\left(\beta_2 \log\left(1.01\right)\right)+\left(\beta_3 .68*log\left(1.01\right)\right)\right) * \log\left(1.01\right)\right] - 1\right] * 100 \end{equation}